diff --git a/.ai/AGENTS.md b/.ai/AGENTS.md new file mode 100644 index 000000000..8c71c6566 --- /dev/null +++ b/.ai/AGENTS.md @@ -0,0 +1,9 @@ +# Agent Router + +This repository uses implementation-scoped memory under `.ai/implementations/`. + +## Active Implementations + +- `blast-from-the-past`: read `.ai/implementations/blast-from-the-past/AGENTS.md`, then follow that implementation's boot sequence. + +Do not put implementation plans, decisions, reviews, closeouts, references, experiments, or aspect notes at the top level of `.ai/`. diff --git a/.ai/README.md b/.ai/README.md new file mode 100644 index 000000000..a00d73563 --- /dev/null +++ b/.ai/README.md @@ -0,0 +1,9 @@ +# .ai/ - Implementation Memories + +This directory contains implementation-scoped AI memory systems. + +## Implementations + +- `implementations/blast-from-the-past/` - NVIDIA Warp exploration for PySPH GPU particle dynamics. + +Each implementation owns its own plans, decisions, ADR graph, reviews, updates, aspects, references, experiments, templates, scripts, and skills. diff --git a/.ai/implementations/blast-from-the-past/AGENTS.md b/.ai/implementations/blast-from-the-past/AGENTS.md new file mode 100644 index 000000000..5744a689b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/AGENTS.md @@ -0,0 +1,117 @@ +# Agent Operating Contract - blast-from-the-past + +This implementation directory is the memory system for one implementation - **blast-from-the-past** - being developed inside the PySPH host project. It is not a memory system for the host. When this implementation ships, this implementation memory is archived into permanent docs, not deleted. + +## Boot Sequence + +1. Read `.ai/implementations/blast-from-the-past/current.md`. +2. If `current.md` lists in-flight experiments, inspect their actual outputs/logs and update each experiment's `status` and `last_checked` before doing anything else. +3. Read `.ai/implementations/blast-from-the-past/implementation.md`. +4. Read `.ai/implementations/blast-from-the-past/host-project-notes.md`. +5. Read the most recent daily closeout. On the first session of a new week, also read the most recent weekly closeout. +6. Identify which aspects today's task touches. Read those aspects' `context.md`, `open-questions.md`, `known-issues.md`, and only ADRs scoped to those aspects plus `global` via `.ai/implementations/blast-from-the-past/decisions/index.json`. +7. Open or create today's session log and maintain its `memory_consulted` list as `.ai/` files are opened. + +State in chat that boot is complete before doing implementation work. + +## Authority Order + +1. Host project's pre-existing AI configs and conventions. No such configs were found during scaffold discovery; if added later, they override `.ai/`. +2. `.ai/implementations/blast-from-the-past/aspects//context.md` for aspect-scoped matters. +3. `.ai/implementations/blast-from-the-past/implementation.md` and `.ai/implementations/blast-from-the-past/conventions.md` for implementation-wide matters. +4. `.ai/implementations/blast-from-the-past/skills/*.md` for procedures. +5. Agent judgment. + +If `.ai/` content contradicts a host config, the host wins; flag it and propose an ADR. If two `.ai/` files contradict each other, the most recently dated artifact wins; flag and repair stale memory in the same session. + +## Five Non-Negotiable Rules + +### Rule 1 - Closeouts Are Tagged by User + +Every daily closeout, weekly closeout, and session log must begin with frontmatter containing a valid `user:` from: + +- `@kunalpuri-prediqt` + +The validator enforces this. + +### Rule 2 - Plan Before Code, Sign-Off Required + +Tier 0 trivial work: at most five lines, one file, no behavioral effect. No plan file; note it in the session log. + +Tier 1 lightweight work: single session, roughly 50 LOC, at most three files, no ADR-worthy decision, within boundary. Post a one-paragraph plan in chat, wait for approval, and quote the user's approval verbatim with timestamp in the session log. + +Tier 2 full work: write `.ai/implementations/blast-from-the-past/plans/{YYYY-MM-DD}_{slug}.md`, list aspects and host files, post it in chat, wait for `APPROVED`, `APPROVED WITH EDITS: ...`, or `REJECTED: ...`, and quote the approval verbatim in the plan. + +Approval integrity: never paraphrase approval. An approval the agent cannot quote did not happen. + +Experiments vs plans: parameter-only or config-only runs need an experiment entry. Persistent code changes need a plan. + +Boundary visibility: if a Tier 2 plan touches host files outside `.ai/implementations/blast-from-the-past/implementation.md`, set `within_boundary: false` and call it out. Approved out-of-boundary work must amend the boundary during review. + +### Rule 3 - Decisions Are Recorded as a Graph + +For non-trivial design/modeling choices: + +1. Create an ADR with `.ai/implementations/blast-from-the-past/scripts/new-decision.py`. +2. Run `python .ai/implementations/blast-from-the-past/scripts/update-decision-graph.py`. +3. Reference the ADR in plans, reviews, and session logs. + +ADR frontmatter is the single source of truth. Do not hand-edit `.ai/implementations/blast-from-the-past/decisions/index.json` or `.ai/implementations/blast-from-the-past/decisions/graph.md`. + +### Rule 4 - Review Before Commit; Owner Exception for Prototypes + +Before any commit: + +1. Produce a review artifact in `.ai/implementations/blast-from-the-past/reviews/`. +2. Include diff summary, aspects, host files, behavioral/numerical changes, raw validation output, `validate-memory.py` output, risks, unresolved questions, and at least one visual aid or a one-line waiver. +3. Post the review in chat. +4. Choose and record one review mode: + - **Promotion review (default):** wait for `@prabhu` to reply `LGTM` and + quote the verdict verbatim in the review. + - **Prototype-owner review:** `@kunalpuri-prediqt` may explicitly authorize + the commit without external `LGTM`; quote that authorization verbatim in + the review and set `status: prototype-approved`. +5. Only then commit. The commit message references the review and touched ADRs. + +The prototype-owner exception is narrow. Every condition below must hold: + +- the work is exploratory/prototype work inside the approved implementation + boundary: `.ai/` memory, experiments, `pysph/base/warp_*.py`, and focused + `pysph/base/tests/test_warp_*.py` files; +- the approved plan is recorded when Rule 2 requires one; +- the change does not alter generic public API/ABI, non-Warp host behavior, + dependencies/build/release configuration, or files outside the boundary; +- the review clearly labels unvalidated behavior, incomplete phases, and + performance claims as prototype evidence rather than production results; +- the commit subject begins with `prototype:`. + +Prototype-owner approval is not promotion approval. A cumulative promotion +review with exact `@prabhu: LGTM` is still required before any prototype change +is pushed to an upstream PR, merged into a production/release branch, presented +as completed production work, or used to amend generic host interfaces. If +scope crosses a condition above, stop and use the promotion-review path before +committing the expanded change. + +### Rule 5 - Do Not Cut Long-Running Tasks Short + +For long tasks, proceed to completion or a defined checkpoint. Long numerical runs are handed off through experiment entries with `status: running`. If context is the genuine limit, write a session log with exact file:line, current test/experiment state, and the next concrete action. + +## Closing Every Session + +1. Finalize the session log with complete `memory_consulted`. +2. Update today's daily closeout. +3. Regenerate `.ai/implementations/blast-from-the-past/current.md` as a slim pointer. +4. Update aspect questions/issues and experiment statuses. +5. If it is Friday or the user says "wrap the week", produce the weekly closeout and run compaction. + +## Curation + +Weekly: run `python .ai/implementations/blast-from-the-past/scripts/compact.py`; daily closeouts and session logs older than 12 weeks move to `.ai/implementations/blast-from-the-past/updates/archive/`. Weeklies are never archived. + +Monthly: refresh active aspect `## Current understanding` sections and bump `last_reviewed`. + +Pruning signal: files absent from every `memory_consulted` for four or more weeks are pruning candidates. + +## Secrets + +Never copy credentials, tokens, API keys, connection strings, or private keys into `.ai/`. Reference locations by path only. `validate-memory.py` fails on detected secret patterns. diff --git a/.ai/implementations/blast-from-the-past/README.md b/.ai/implementations/blast-from-the-past/README.md new file mode 100644 index 000000000..9438c603d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/README.md @@ -0,0 +1,27 @@ +# .ai/ - Memory for blast-from-the-past + +Tracks the development of one implementation - **blast-from-the-past** - added to this repository. Additive: nothing outside `.ai/` changed at install except this repository's permitted root pointer and the permitted pre-commit hook. + +## For Humans + +- `implementation.md` - central spec, including the integration boundary and amendments log. +- `AGENTS.md` - operating contract every agent follows. +- `current.md` - slim live status, regenerated every closeout. +- `aspects//` - context, open questions, issues, and notes for each dimension of the work. +- `decisions/graph.md` - generated from ADR frontmatter; never hand-edit it or `index.json`. +- `experiments/` - tracked runs, baselines, and validation studies. +- `references/` - annotated literature, human guidance, and API references. + +## Validation + +Run: + +```bash +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +``` + +The same command runs from the installed pre-commit hook. + +## When This Implementation Is Done + +Archive `.ai/implementations/blast-from-the-past/` into the host's permanent docs or under the implementation's directory. Do not delete it: the decision graph and validation history are part of the artifact. diff --git a/.ai/implementations/blast-from-the-past/aspects/cython-boundary/context.md b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/context.md new file mode 100644 index 000000000..8ccb7781d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/context.md @@ -0,0 +1,38 @@ +--- +aspect: cython-boundary +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-15T08:34:00 CET +status: active +--- + +# Aspect: cython-boundary + +## What this aspect covers + +What remains in `.pxd/.pyx`, what can be wrapped or bypassed, and how to preserve Cython ABI/API expectations while experimenting with Warp. + +## Current understanding + +ParticleArray is a Cython extension type declared in `pysph/base/particle_array.pxd`, and existing code can rely on cpdef/cdef methods plus `BaseArray` returns from `get_carray()`. The first Warp migration should preserve the existing ParticleArray public API and host carray compatibility unless a separate boundary amendment approves broader ABI/API changes. + +## Key sub-topics + +- `.pxd` declaration compatibility. +- Cython extension build constraints. +- Host boundary amendments. +- Compatibility with `BaseArray`/NumPy readback paths. + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: cython-boundary` and `scope: global`. + +## Cross-aspect dependencies + +- Depends on: `host-integration` - build/test constraints. +- Influences: all host code changes. diff --git a/.ai/implementations/blast-from-the-past/aspects/cython-boundary/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/known-issues.md new file mode 100644 index 000000000..649ba8e7d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - cython-boundary + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/cython-boundary/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/open-questions.md new file mode 100644 index 000000000..fc414ecd2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/cython-boundary/open-questions.md @@ -0,0 +1,4 @@ +# Open Questions - cython-boundary + +- [open] Which `.pxd` declarations are stable public contracts versus internal implementation details? +- [open] What level of ABI compatibility must be maintained for downstream users? diff --git a/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md new file mode 100644 index 000000000..fda10649b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md @@ -0,0 +1,241 @@ +--- +aspect: gpu-nnps +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-18T16:00:00 CEST +status: active +--- + +# Aspect: gpu-nnps + +## What this aspect covers + +Neighbor-search design and performance around `GPUNNPS`, GPU neighbor caches, and GPU neighbor-list construction. + +## Current understanding + +NNPS is the next migration layer after ParticleArray. It owns the geometric +neighbor-query contract between particle storage and generated equation loops. + +The solver-agnostic NNPS spec now lives under +`.ai/implementations/blast-from-the-past/spec/nnps/`. It covers domain/bounds +state, update timeline, source/destination query semantics, pairwise +smoothing-length inclusion, cache behavior, boundary ghosts, MPI/Zoltan +boundaries, host selection, variant ordering, verification fixtures, and a Warp +porting plan seed. + +Existing PySPH surfaces observed for this spec: + +- `DomainManager` selects CPU/GPU domain management and owns periodic/mirror + settings. +- `NNPSBase` owns particle arrays, wrappers, radius scale, caches, and query + context. +- CPU `NNPS.update()` computes bounds, refreshes structure storage, bins each + particle array, and refreshes caches. +- Existing `GPUNNPS` uses Compyle/OpenCL/CUDA helpers and `GPUNeighborCache`; + it is not Warp-native. +- Application setup currently chooses `OctreeGPUNNPS` or `ZOrderGPUNNPS` for + existing OpenCL/CUDA modes. + +First Warp implementation: + +- `pysph/base/warp_nnps.py` defines `BruteForceWarpNNPS`. +- It uses Warp kernels for the pairwise distance test and returns neighbors + through `UIntArray`. +- It supports source/destination array pairs, 1D/2D/3D coordinate selection, + variable source/destination `h`, sorted-gid output, and update after host + ParticleArray mutation. +- It supports an uncached per-query flags path and a cached flat-neighbor-list + path. +- It is intentionally not the final performance target: the cached path avoids + per-destination launch/readback but remains brute-force O(N^2). + +Uniform-grid implementation: + +- `UniformGridWarpNNPS` builds per-source device-side cell ids, cell counts, + exclusive-scan cell starts, and flat cell-particle arrays. +- Neighbor caches are built by scanning adjacent cells and applying the same + pairwise `h_i`/`h_j` inclusion rule. +- The first grid path supports 1D/2D/3D, multiple particle arrays, variable + `h`, and update after mutation in focused tests. +- It still materializes host-side neighbor arrays for the existing `UIntArray` + query contract; equation-kernel integration should avoid that readback. +- `warp_grid_device` benchmarks bulk device neighbor-cache construction without + the per-particle `UIntArray` query loop. At 1,000,000 particles on + PrediQT-02, it measured `88.288x` CPU speed while matching average neighbor + count. + +Device-consumption proof: + +- `UniformGridWarpNNPS.compute_neighbor_sum(src_index, dst_index, prop)` builds + the device-resident neighbor cache and runs a Warp kernel that sums a scalar + source property over neighbors for each destination particle. +- This is intentionally a narrow equation-like consumer, not the final solver + loop. It proves the cache can feed useful GPU work before any host + `UIntArray` materialization. +- At 1,000,000 particles on PrediQT-02, `warp_grid_reduce` measured `145.583x` + CPU speed for a neighbor mass sum on Intel(R) Core(TM) Ultra 7 155H versus + NVIDIA GeForce RTX 4060 Laptop GPU. The average neighbor sum matched to the + reported precision (`25.568`); the aggregate checksum differed by `6` over + roughly `25.6M` contributions. + +First SPH equation proof: + +- `pysph/base/warp_sph.py` defines `compute_summation_density()` and Warp + CubicSpline density kernels for float32/float64. +- The kernel mirrors PySPH `SummationDensity` with + `HIJ = 0.5*(d_h[d_idx] + s_h[s_idx])` and + `rho_i = sum_j m_j * W(XIJ, RIJ, HIJ)`. +- Focused tests compare Warp density values against a CPU `CubicSpline` + reference in 2D and cross-array 3D and verify `rho` can be pulled back to the + host ParticleArray. +- At 10,000,000 particles on PrediQT-02, `warp_grid_density` measured + `69.084x` CPU/Cython speed versus PySPH `SPHEvaluator` with + `SummationDensity`, `CubicSpline(dim=2)`, and `LinkedListNNPS`. + +EOS and continuity proof: + +- `pysph/base/warp_sph.py` now also defines `compute_isothermal_eos()` and + `compute_continuity()`. +- EOS mirrors PySPH `IsothermalEOS`: `p = p0 + c0^2*(rho-rho0)`. +- Continuity mirrors PySPH `ContinuityEquation` with `VIJ . DWIJ` and the same + CubicSpline gradient convention at `HIJ`. +- Focused tests compare EOS and continuity against CPU references in same-array + 2D and cross-array 3D fixtures, including host pullback of `p` and `arho`. +- The EOS+continuity benchmark is capped at 5M particles. At 5M on PrediQT-02, + `warp_grid_eos_cont` measured `72.583x` CPU/Cython speed versus PySPH + `SPHEvaluator`. + +Pressure-gradient proof: + +- `pysph/base/warp_sph.py` now defines `compute_pressure_gradient()` for the + inviscid pressure-gradient portion of WCSPH momentum. +- The kernel computes + `a_i = -sum_j m_j * (p_i/rho_i^2 + p_j/rho_j^2) * grad(W_ij)` using the + same CubicSpline gradient convention at `HIJ`. +- Focused tests compare same-array 2D and cross-array 3D accelerations against + CPU references, including host pullback of `au`, `av`, and `aw`. +- The pgrad benchmark is capped at 5M particles. At 5M on PrediQT-02, + `warp_grid_pgrad` measured `38.722x` CPU/Cython speed versus a pure Cython + pressure-gradient equation. + +Repeated-step proof: + +- `UniformGridWarpNNPS.update(push=False)` can rebuild bounds, grids, and + caches from device-resident `x/y/z/h` values without pushing stale host + ParticleArray coordinates over the device state. +- `pysph/base/warp_sph.py` now has device-side leapfrog kick/drift kernels, + periodic position wrapping, and a minimal `wc_sph_leapfrog_step()`. +- Focused tests compare the KDK step against CPU reference density, EOS, + pressure-gradient acceleration, and final state. The Warp SPH/NNPS suite + passes with `29 passed`. +- Periodic behavior was initially position wrapping only; true periodic + minimum-image distance + wrapped cell lookup are now implemented for the + grid-direct path (see the periodic minimum-image note below). + +Artificial-viscosity proof: + +- `pysph/base/warp_sph.py` now adds Monaghan-style artificial viscosity through + the same device-resident `UniformGridWarpNNPS` neighbor cache used by density, + continuity, and pressure-gradient kernels. +- The viscosity kernel is additive over existing `au/av/aw`, so the pressure + gradient path can remain the owner of resetting acceleration before optional + stabilizing terms contribute. +- The current implementation uses per-particle sound speed `cs` when available, + with a constant-`c0` fallback for callers that have not run Tait EOS. + +Tait EOS proof: + +- `pysph/base/warp_sph.py` now adds a Warp `TaitEOS` path that writes both + pressure `p` and sound speed `cs`. +- The WCSPH step helpers keep `eos='isothermal'` as the compatibility default + and accept `eos='tait'`, `gamma=7.0` for the elliptical-drop path. +- Artificial viscosity now consumes `cs` through the same device-resident + neighbor cache and computes `cij = 0.5*(d_cs + s_cs)`. + +Continuity-density repeated-step proof: + +- `wc_sph_leapfrog_step(..., density_mode='continuity')` now routes to a + PySPH `WCSPHStep`-style PEC path. It saves reference position/velocity/rho + state on device, computes Tait EOS, pressure-gradient/artificial-viscosity + acceleration, `ContinuityEquation` density rate, and XSPH correction from the + device-resident uniform-grid neighbor cache, applies stage1, rebuilds NNPS + from device positions, recomputes equations, and applies stage2. +- The original KDK summation-density step remains the compatibility/default + path. The continuity path is the one used for PySPH Application parity in the + resolved elliptical-drop comparison. + +Grid-direct neighbor traversal proof (ADR-0004): + +- The WCSPH continuity hot path no longer materializes a flat CSR neighbor list. + `compute_wcsph_accel_continuity` (fused equations) and the adaptive CFL + `_wcsph_dt_factors` kernel now walk the uniform-grid cell list directly, + applying the support cutoff (`rij2 < (radius_scale*h_i)^2 or + rij2 < (radius_scale*h_j)^2`) inline so they visit exactly the neighbor set + the flat list contained. `build_neighbor_cache_gpu` is never called on the + continuity path; only the cheap `_build_grid` cell list remains. +- The generator gained `neighbor_mode='grid'` (signature swaps + `starts/lengths/neighbors` for the cell list + bounds + `radius_scale`; the + geometry is split pre/post cutoff; the cell-block walk wraps the equation + snippets). `neighbor_mode='flat'` (default in the generator) is unchanged and + still backs the host `get_nearest_particles` query API, the per-equation + oracle, summation density, and `compute_neighbor_sum`. +- Million-particle (`nx=565`) per-step wall fell ~25-35% (the flat cache build + was ~45-50% of the step and is gone; the equation kernel absorbed the single + cutoff traversal). Adaptive `nx=100` resolved kept exactly `1393` steps with + fp32-scale deltas, validating both grid-direct consumers over a long run. +- ADR-0003 follow-up (generator migration): the duplicated flat/grid + `_wcsph_dt_factors` hand kernels are de-duplicated into one generated + `WcsphCflFactor` block (flat+grid via `neighbor_mode`), and the standalone + equation/summation-density helpers are generator-backed too. The grid-direct + continuity path is byte-identical (same fused grid kernel), so this is + perf-neutral; the flat `build_neighbor_cache_gpu` still backs the host query + API, the summation path, and `compute_neighbor_sum`. +- "Grid-direct everywhere": the summation Euler (`wc_sph_euler_step`) and KDK + leapfrog step paths were also moved to grid-direct (the 5 standalone helpers + gained `neighbor_mode`, defaulting flat; the summation step paths pass + `'grid'`). No device step path builds a flat CSR neighbor cache anymore; + `build_neighbor_cache_gpu` is now used only by the host `get_nearest_particles` + query API, `compute_neighbor_sum`, and the flat-mode oracle/cross-array tests. +- Periodic minimum-image (ADR-0004 follow-up): `UniformGridWarpNNPS.set_periodic_box` + tiles a (cubic) periodic box exactly; the cell-id binning kernels wrap (not + clamp) the cell index in periodic dims so out-of-box positions bin into their + image cell; the generator's `periodic=True` grid variant walks wrapped cells + and uses minimum-image separations. `_run_equation_group` auto-detects + periodicity from `_bounds`. Validated vs a CPU minimum-image reference + (5.5e-6, incl. out-of-box) and by periodic-lattice uniformity; non-periodic + path byte-identical. Constraints (raise): equal-length periodic dims, + `floor(L/radius_scale*h) >= 3`. The flat host-query path is not yet periodic. + +## Key sub-topics + +- Existing `GPUNeighborCache` behavior. +- Existing brute-force, Z-order, stratified SFC, and octree GPU NNPS surfaces. +- Correctness and performance baselines. +- Warp brute-force correctness baseline. +- Warp cell-list performance prototype. +- Cached flat neighbor list generation. +- Optimize uniform-grid/cell-list structure. +- Device-resident equation-kernel consumption of grid neighbor lists. +- Reusable Warp equation-loop contract. +- Warp SPH equation kernels. +- Tiny Euler/PEC-style integrator loop. +- Device-authoritative NNPS refresh after position updates. +- Minimal KDK leapfrog step and periodic position wrapping. +- Additive artificial-viscosity momentum term. +- Tait EOS and per-particle sound-speed path. +- Grid-direct neighbor traversal on the WCSPH continuity hot path (ADR-0004). + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: gpu-nnps` and `scope: global`. + +## Cross-aspect dependencies + +- Depends on: `particle-memory` - particle/device arrays. +- Influences: `validation-benchmarks` - neighbor-search benchmark cases. diff --git a/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md new file mode 100644 index 000000000..69df6159e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - gpu-nnps + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md new file mode 100644 index 000000000..207d50a85 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md @@ -0,0 +1,4 @@ +# Open Questions - gpu-nnps + +- [open] Which existing GPU NNPS path is the baseline for the first Warp comparison? +- [open] What neighbor-list correctness metric will gate performance claims? diff --git a/.ai/implementations/blast-from-the-past/aspects/host-integration/context.md b/.ai/implementations/blast-from-the-past/aspects/host-integration/context.md new file mode 100644 index 000000000..6b99e1f92 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/host-integration/context.md @@ -0,0 +1,50 @@ +--- +aspect: host-integration +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-15T09:45:00 CET +status: active +--- + +# Aspect: host-integration + +## What this aspect covers + +CLI/build/test integration, compatibility with existing OpenCL/CUDA/Compyle paths, and keeping the implementation boundary truthful. + +## Current understanding + +PySPH is installed editable in the active PQT venv at `/home/kunalp/.pqt_venv_e0b41259`. Zoltan `v3.901` was built from `sandialabs/Zoltan` under `/home/kunalp/prediqt/zoltan`, then PyZoltan `1.1.1` was installed with `ZOLTAN=/home/kunalp/prediqt/zoltan` and `--no-build-isolation`. + +Persistent rebuild configuration lives in `/home/kunalp/.compyle/config.py`, with `ZOLTAN='/home/kunalp/prediqt/zoltan'` and MPI flags from the PQT OpenMPI Spack view. Setuptools was installed into the venv so Python 3.14 can import `distutils` through `setuptools._distutils`. + +Validation showed plain imports work for `pysph`, `pyzoltan`, `pysph.parallel.parallel_manager`, and the Warp ParticleArray path; `has_mpi()`, `has_zoltan()`, and `in_parallel()` all return `True`. + +The top-level `CODEBASE_UNDERSTANDING.md` has been worked into +`.ai/implementations/blast-from-the-past/spec/codebase-understanding/` as a +curated implementation-facing digest. The spec now has an index at +`.ai/implementations/blast-from-the-past/spec/README.md` linking the codebase, +ParticleArray, and NNPS layers. + +## Key sub-topics + +- Existing build/test commands. +- Optional GPU dependencies. +- Boundary amendments and review integrity. +- Output/restart/dummy-particle compatibility. +- Local PQT editable install and Zoltan/PyZoltan rebuild reproducibility. +- Codebase-level architecture context for staged GPU migration. + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: host-integration` and `scope: global`. + +## Cross-aspect dependencies + +- Depends on: `cython-boundary` - approved host files. +- Influences: all implementation plans and reviews. diff --git a/.ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md new file mode 100644 index 000000000..bc0974d3b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - host-integration + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md new file mode 100644 index 000000000..2ddfa3d6d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md @@ -0,0 +1,4 @@ +# Open Questions - host-integration + +- [open] Should Warp be an optional dependency, an extra, or only an experiment dependency at first? +- [open] Which CI or local validation tier should eventually exercise Warp? diff --git a/.ai/implementations/blast-from-the-past/aspects/particle-memory/context.md b/.ai/implementations/blast-from-the-past/aspects/particle-memory/context.md new file mode 100644 index 000000000..e1c330e7b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/particle-memory/context.md @@ -0,0 +1,62 @@ +--- +aspect: particle-memory +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-15T08:34:00 CET +status: active +--- + +# Aspect: particle-memory + +## What this aspect covers + +ParticleArray/device data ownership, transfer semantics, dtype/precision, and compatibility with existing PySPH device helpers. + +## Current understanding + +ParticleArray currently owns host `BaseArray` storage for properties and constants, while non-cython GPU backends attach `DeviceHelper` as a mirror. The important invariants for a Warp port are: + +- Property storage is structure-of-arrays: one flat array per property, with optional fixed per-particle stride. +- `tag`, `pid`, and `gid` are baseline properties; `tag` drives Local/Remote/Ghost behavior. +- `align_particles()` partitions Local particles first and updates `num_real_particles`. +- Constants are fixed-size named arrays and do not resize with particle count. +- Serialization/dummy-particle creation depends on property type/default/stride metadata. + +See `.ai/implementations/blast-from-the-past/spec/particle-array/`. + +The current Warp elliptical-drop path keeps repeated-step particle state on +device. `wc_sph_leapfrog_step(push=False)` avoids host pushes during the loop; +adaptive dt pulls only the reduced scalar timestep needed as the next launch +parameter, and the runner pulls full arrays only for explicit final metrics, +checkpoint `.npz` output, and side-by-side plots. + +The PySPH Application parity path now also keeps density evolution +device-authoritative. `density_mode='continuity'` stores `x0/y0/z0`, +`u0/v0/w0`, and `rho0` on device, computes `arho` on device, and updates `rho` +through WCSPH PEC-style stages without full host pulls/pushes inside the +repeated stepping loop. The only per-step host handoff remains the scalar +adaptive timestep. The PySPH-like `n_damp` and output-time landing policy is +applied to that scalar only; it does not introduce particle-array transfers +inside the step loop. + +## Key sub-topics + +- ParticleArray property ownership. +- Device helper compatibility. +- Float/double precision choices. +- Strided property gather/scatter behavior. +- Host/device sync authority for `get()`, output, and Cython callers. +- Scalar-only adaptive timestep handoff versus full-array checkpoint pulls. + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: particle-memory` and `scope: global`. + +## Cross-aspect dependencies + +- Influences: `warp-backend`, `gpu-nnps`, and `validation-benchmarks`. diff --git a/.ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md new file mode 100644 index 000000000..962d2f1b7 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - particle-memory + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md new file mode 100644 index 000000000..aba948b31 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md @@ -0,0 +1,6 @@ +# Open Questions - particle-memory + +- [open] Should Warp arrays mirror existing device helpers or become a separate backend-owned representation? +- [open] What host-device synchronization points are required for current PySPH outputs? +- [open] Should constants be Warp arrays, host-only metadata, or mirrored both ways? +- [open] What exact post-mutation ordering guarantees should the Warp backend promise for strided/tagged arrays? diff --git a/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md new file mode 100644 index 000000000..55195812b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md @@ -0,0 +1,367 @@ +--- +aspect: validation-benchmarks +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-18T11:00:00 CEST +status: active +--- + +# Aspect: validation-benchmarks + +## What this aspect covers + +Baselines, timings, correctness checks, acceptance thresholds, experiment handoff, and validation evidence for fast particle dynamics. + +## Current understanding + +The first correctness baseline is ParticleArray and DeviceHelper behavior parity: construction, scalar broadcast, strided properties, constants, push/pull, alignment, add/remove/extract/append, serialization metadata, and GPU ordering allowances. Performance criteria are now split into correctness gates for the current prototype and timing evidence for the next optimization step. + +The active Python can import Warp `1.14.0`. The venv initially lacked `compyle`, `cyarray`, and PySPH's compiled `pysph.base.particle_array` extension; installing the declared requirements and rebuilding `particle_array` narrowly with `pyximport` made the focused tests runnable. Isolated `WarpArray.aligned()` probes passed on `cuda:0` for float64 strided data and int64 tag data. + +Later host-integration work installed PySPH editable into the PQT venv with PyZoltan/Zoltan enabled, so tests now run against the installed editable package instead of only the narrow `pyximport` build. + +Current passing checks: + +- `python -m pytest -q pysph/base/tests/test_warp_device_helper.py` - 20 passed. +- Python-launched CPU sanity slice for constructor, alignment, add-property, constants, remove, add, and extract - 7 passed. +- Plain import validation for PySPH/PyZoltan/Zoltan/parallel manager - pass; `has_zoltan()` and `in_parallel()` are `True`. + +Active experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_mutation_benchmark.sh`. +- Smoke result is recorded in the ParticleArray experiment doc; Warp add/remove/extract are currently slower than CPU because the prototype still uses host-side rebuilds/readback for structural mutations. + +Next benchmark family should target NNPS: + +- CPU-vs-Warp neighbor set correctness. +- NNPS update time. +- all-particle query time. +- cache build time. +- readback time separated from device computation. +- average neighbor count and smoothing-length mode recorded with each run. + +First NNPS experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_benchmark.sh`. +- Smoke result is recorded in the experiment doc; CPU, uncached Warp, cached + Warp, and Warp grid average neighbor counts match at 128 particles. Cached + Warp brute force is much faster than the per-query path but remains an O(N^2) + bridge; Warp grid is the first cell-list baseline. +- The smoke benchmark now records CPU/GPU hardware and CPU-relative speedup. On + Intel(R) Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX 4060 Laptop GPU, the + 128-particle smoke run shows `warp_grid` at `0.041x` CPU speed. +- A 1,000,000-particle host-facing benchmark on the same hardware shows + `warp_grid` at `4.269x` CPU speed with matching average neighbor count + (`25.568`). +- A 1,000,000-particle device-oriented benchmark shows `warp_grid_device` at + `88.288x` CPU speed with matching average neighbor count (`25.568`). This is + the relevant GPU-side result because it avoids the per-particle + `get_nearest_particles()`/`UIntArray` loop. + +Device-consumption NNPS experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_benchmark.sh`. +- Focused Warp NNPS correctness now includes direct packed-GPU-cache index + parity tests and device-side neighbor-sum tests, and passes with `17 passed`. +- A 1,000,000-particle benchmark on Intel(R) Core(TM) Ultra 7 155H versus + NVIDIA GeForce RTX 4060 Laptop GPU shows `warp_grid_reduce` at `145.583x` + CPU speed for a neighbor mass sum. Average neighbor sum matches to reported + precision (`25.568`), with aggregate checksum delta `6` over roughly `25.6M` + contributions. + +Warp summation-density experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_benchmark.sh`. +- Focused correctness checks `compute_summation_density()` against a CPU + `CubicSpline` reference in 2D and cross-array 3D and passes with + `19 passed` across Warp SPH and NNPS tests. +- The CPU baseline is PySPH `SPHEvaluator` with Cython backend, + `SummationDensity`, `CubicSpline(dim=2)`, and `LinkedListNNPS`. +- A 1M-to-10M sweep on Intel(R) Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX + 4060 Laptop GPU shows matching checksums to reported precision and speedups: + `152.508x` at 1M, `227.555x` at 2M, `64.964x` at 5M, and `69.084x` at 10M. + +Warp EOS+continuity experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_benchmark.sh`. +- Focused correctness checks Warp `IsothermalEOS` and `ContinuityEquation` + against CPU references and passes with `22 passed` across Warp SPH and NNPS + tests. +- The CPU baseline is PySPH `SPHEvaluator` with Cython backend, + `IsothermalEOS`, `ContinuityEquation`, `CubicSpline(dim=2)`, and + `LinkedListNNPS`. +- The benchmark is capped at 5M particles. A 1M/2M/5M sweep on Intel(R) + Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX 4060 Laptop GPU shows + pressure checksums matching to reported precision and speedups: `161.063x` + at 1M, `136.886x` at 2M, and `72.583x` at 5M. + +Warp pressure-gradient experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Timing wrapper: `run_benchmark.sh`. +- Focused correctness checks Warp inviscid pressure gradient against CPU + references and passes with `24 passed` across Warp SPH and NNPS tests. +- The CPU baseline is PySPH `SPHEvaluator` with Cython backend, a pure + `PressureGradientOnly` equation, `CubicSpline(dim=2)`, and `LinkedListNNPS`. +- The benchmark is capped at 5M particles. A 1M/2M/5M sweep on Intel(R) + Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX 4060 Laptop GPU shows + speedups: `148.884x` at 1M, `129.854x` at 2M, and `38.722x` at 5M. + +Warp WCSPH Euler-step experiment: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md` +- Correctness wrapper: `run_correctness.sh`. +- Focused correctness checks Warp Euler stepping directly and the chained + `wc_sph_euler_step()` path against CPU reference density, pressure, + pressure-gradient acceleration, and final position/velocity state. +- The focused Warp SPH/NNPS suite passes with `26 passed`. +- This is a one-step correctness milestone. Repeated-step benchmarking should + wait for a device-aware NNPS refresh after positions move. + +Warp WCSPH leapfrog checkpoint: + +- The Euler-step experiment now also records a KDK leapfrog checkpoint. +- Focused tests cover device-coordinate NNPS refresh, direct leapfrog + kick/drift with periodic position wrapping, and `wc_sph_leapfrog_step()` + against CPU reference calculations. +- Current focused result: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +29 passed, 2 warnings in 5.82s +``` + +Warp artificial-viscosity checkpoint: + +- `pysph/base/warp_sph.py` now has an additive Monaghan-style artificial + viscosity kernel for the WCSPH momentum path. It now uses pair-averaged + per-particle sound speed `cs` when available, with constant `c0` as a + compatibility fallback. +- Focused tests compare the artificial-viscosity acceleration against a CPU + CubicSpline reference and verify that the viscosity term adds onto existing + acceleration arrays instead of replacing them. +- Current focused result: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +32 passed, 2 warnings in 4.14s +``` + +Warp Tait EOS checkpoint: + +- `compute_tait_eos()` matches PySPH `TaitEOS` for pressure and per-particle + sound speed `cs`. +- Focused tests cover direct Tait EOS output and a small WCSPH Euler step using + Tait pressure plus `cs`-based artificial viscosity. + +Warp elliptical-drop runner: + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` + contains the first application-style runner around the current Warp prototype. +- It creates the standard circular elliptical-drop initial patch and velocity + field, advances with `UniformGridWarpNNPS` and `wc_sph_leapfrog_step()`, pulls + final arrays once, and writes scalar metrics plus an `.npz` result. +- Smoke result with `nx=8`, `steps=2`, `dt=1.0e-5`, `c0=20.0`, `alpha=0.1`, + `beta=0.0`, `eos=tait`, `gamma=7.0`: 204 particles, `all_finite=true`, + final time `2e-05`, `rho_min=0.5834615230560303`, + `rho_max=0.9999511241912842`, `cs_min=3.9725253582000732`, + `cs_max=19.997066497802734`, kinetic energy `8078.17389338273`. +- Ramp results stayed finite through `nx=24`, 1808 particles, 10 steps at + `dt=5.0e-6`, with `rho_min=0.633074939250946`, + `rho_max=0.9999793767929077`, and kinetic energy `7840.533230601928`. +- Artificial-viscosity ramp check with `nx=16`, 805 particles, 5 steps, + `dt=1.0e-5`, `alpha=0.1`, `beta=0.0` stayed finite with + `rho_min=0.6330116391181946`, `rho_max=0.9999754428863525`, and kinetic + energy `7868.737673401772`. +- Tait EOS ramp check with `nx=16`, 805 particles, 5 steps, `dt=1.0e-5`, + `alpha=0.1`, `beta=0.0`, `gamma=7.0` stayed finite with + `rho_min=0.6329819560050964`, `rho_max=0.9999754428863525`, + `cs_min=5.072288990020752`, `cs_max=19.99852752685547`, and kinetic energy + `7868.739071212255`. +- XSPH/Gaussian/adaptive-dt checkpoint: + `pysph/base/warp_sph.py` now supports Gaussian kernel selection, + `compute_xsph_correction()`, `leapfrog_drift_xsph()`, and + `compute_wcsph_adaptive_timestep()`. Adaptive dt computes `dt_cfl` and + `dt_force` on device, reduces them on device, and pulls only the final scalar + timestep per step. +- Focused Warp SPH result after this checkpoint: + +```text +python -m pytest pysph/base/tests/test_warp_sph.py -q +18 passed, 2 warnings in 3.50s +``` + +- Updated smoke wrapper now exercises Gaussian + Tait + artificial viscosity + + XSPH + adaptive dt. Result with `nx=8`, 204 particles, 2 steps: + `all_finite=true`, `kernel=gaussian`, `radius_scale=3.0`, + `xsph_eps=0.5`, `adaptive_dt=true`, `rho_min=0.534595251083374`, + `rho_max=0.9998562335968018`, `dt_min_used=9.999999747378752e-06`, + `dt_max_used=9.999999747378752e-06`, kinetic energy + `8078.179766857993`. +- New comparison script + `compare_warp_pysph_elliptical_drop.py` runs the Warp path and a CPU + PySPH-primitive baseline using `LinkedListNNPS`, `Gaussian`, Tait EOS, + artificial viscosity, XSPH, and adaptive dt formulas. It writes CPU/Warp + `.npz` outputs plus `comparison-smoke.png` with side-by-side speed-colored + scatter plots. Smoke comparison result: CPU and Warp both finite with 204 + particles; CPU `rho_min=0.534595094929311`, Warp + `rho_min=0.534595251083374`; CPU kinetic energy `8078.179846214378`, Warp + `8078.179766857993`. +- Resolved `nx=100` Application-backed comparison: + `resolved_elliptical_drop_comparison.py` runs PySPH's + `elliptical_drop_no_scheme.py` Application baseline and the Warp runner at + `t=0.0008` and `t=0.0038`, writes side-by-side images with exact ellipse + overlays, and records timing/shape/density/energy metrics. +- The first resolved run used Warp summation density while PySPH evolved + density through `ContinuityEquation`/`WCSPHStep`; it is now diagnostic only. + That mismatch caused larger density/pressure excursions and many small Warp + adaptive substeps: Warp took 4807 steps through `t=0.0038`, with density at + `t=0.0008` ranging from `0.9505811929702759` to `1.0439165830612183`. +- The continuity-density parity run uses the new Warp PEC-style density path + (`density_mode='continuity'`). At `nx=100`, 31417 particles, PySPH CPU took + `228.25765374601178` s / 1393 steps, and Warp took + `30.008050591000938` s / 1804 steps, for `7.606547218180963x` wall-time + speedup. At `t=0.0038`, major-axis delta was + `1.5947661098358878e-06`, minor-axis delta was + `2.1943316564909665e-06`, `rho_min` delta was + `-4.816405699936688e-06`, `rho_max` delta was + `8.755722542552746e-07`, and kinetic-energy delta was + `-0.00043376772100600647`. +- The old continuity-density run's remaining step-count difference was caused + by timestep policy, not the density formulation: the Warp runner capped + adaptive `dt` to the initial value while PySPH allowed the damped timestep to + grow after the `n_damp` ramp. +- The timestep-policy parity run adds a PySPH-like policy to the resolved + runner: damp early timesteps with `n_damp`, allow adaptive growth through an + undamped `warp_dt_max`, and apply checkpoint landing caps only to the current + step. At `nx=100`, 31417 particles, PySPH CPU took + `233.97314716299297` s / 1393 steps, and Warp took + `23.629107111992198` s / 1393 steps, for + `9.901903870258701x` wall-time speedup. At `t=0.0038`, major-axis delta was + `-3.1258252297661215e-07`, minor-axis delta was + `1.3598666296354978e-06`, `rho_min` delta was + `-8.228944999855159e-07`, `rho_max` delta was + `9.947815438060559e-07`, and kinetic-energy delta was + `-0.0004318240680731833`. The very small Warp `dt_min` is an output-time + landing step and does not permanently shrink later adaptive steps. +- Precision note for the resolved Warp runner: initial host arrays are created + as `float64`, but `WarpDeviceHelper` casts floating properties to + `compyle.config.get_config().use_double`. On the active machine this config + is `False`, so the current Warp device execution path is fp32. An explicit + Warp-only `nx=100` rerun under that fp32 config took + `26.5732471299998` s / 1393 steps, versus the committed run's + `23.629107111992198` s / 1393 steps; both are the same fp32 device path, so + the timing difference is treated as run-to-run/module-cache variance. +- Million-particle fixed-step comparison before cache reuse: + `nx=565`, 1,002,885 particles, 10 fixed steps. PySPH CPU Application took + 57.48 s, Warp GPU took 7.17 s, for `8.01673640167364x` wall-time speedup. + Final shape/density/energy deltas were tiny and both outputs were finite. +- Profiling that million-particle path showed the continuity-density PEC step + was building eight same-array neighbor caches per step. Each cache had about + 45M neighbor entries, and cache construction dominated the Warp step time. +- Cache-reuse slice: `pysph/base/warp_sph.py` helpers now accept optional + prebuilt neighbor caches, and the continuity-density PEC path builds one + cache per half-stage. Focused tests assert the full step builds two equation + caches instead of eight. +- Million-particle fixed-step comparison after cache reuse: + the same PySPH CPU Application baseline remains 57.48 s, while Warp improved + to 4.51 s / 10 steps, for `12.7450110864745x` speedup and + `1.58980044345898x` improvement over the prior Warp run. The segmented + cache profile now shows two cache builds per step and step wall times around + `0.098335` to `0.138290` s before runner output/setup overhead. +- Equation-fusion slice (ADR-0003 dynamic code generation): the four + continuity-stage neighbor-loop equations are fused into one generated kernel + per PEC half-stage. The segmented million-particle profile drops equation + launches from 8 to 2 per step, equation-kernel time from ~0.064-0.088 s to + 0.011-0.014 s per step (~5-6x), and steady-state step wall from + 0.098-0.138 s to 0.076-0.098 s (~25%); the neighbor-cache build (~0.034-0.046 + s) is now the dominant per-step cost. The 10-step headline wall is + overhead/IO-bound and noisy (warm samples 3.77-6.04 s, best 3.77 s = + `15.25x` vs CPU), so per-step compute is the meaningful metric. Numerical + parity is essentially exact versus the prior separate-kernel Warp run + (positions/density/pressure identical to fp32 print precision, kinetic-energy + delta `-6.4e-09`); CPU deltas match the cache-reuse run. The new focused suite + is `python -m pytest -q pysph/base/tests/test_warp_codegen.py + pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` + -> `47 passed`, including a generated-vs-hand-helper parity test and a + single-fused-launch-per-stage count test. +- Adaptive `nx=100` resolved guard with the fused path: reached `t=0.0038` in + `1393` steps (identical to the committed run), all finite, with shape deltas + `~4.8e-07`, density deltas `~1e-06`, and kinetic-energy delta `1.45e-04` + versus committed Warp metrics. The adaptive path now runs + fused(1)+dt_factors(1)=2 traversals per stage instead of 5, and its Warp wall + fell from the committed `23.63 s` to `10.83-14.33 s` (cross-session, same step + count) -- a cleaner demonstration of the fusion because that run is + per-step-compute-bound. +- Grid-direct neighbor traversal slice (ADR-0004): the continuity hot path + stops materializing a flat CSR neighbor list; both consumers walk the cell + list directly. Segmented million-particle (`nx=565`) profile via + `profile_grid_direct_neighbors.py`: `build_neighbor_cache_gpu` called 0 times + on the continuity path; the dominant flat-cache-build term (~0.034-0.046 + s/step) is gone; grid build now ~0.0004-0.0007 s; equation kernel rises + 0.011-0.014 -> 0.023-0.025 s/launch (it absorbs the single cutoff traversal); + step wall (steady) 0.076-0.098 -> 0.059-0.064 s (~25-35% lower); KE delta vs + flat fused `-1.99e-06`, all finite. Adaptive `nx=100` resolved guard kept + exactly `1393` steps with fp32-scale deltas (KE relative `1.5e-08`, shape + `~2.4e-7/6.6e-7`, density `~9e-7`) and Warp wall `7.82 s`. Focused suite + `50 passed` (adds grid-vs-flat fused parity, grid cache-distinct + single-cell + numeric parity, and a zero-flat-cache-build assertion). Summary folder + `million-cpu-gpu-grid-direct/`. + +## Key sub-topics + +- Baseline selection. +- Hardware/runtime recording. +- Correctness tolerance and performance thresholds. +- ParticleArray/DeviceHelper parity suite. +- Performance benchmarks for structural mutations and device sync. +- NNPS benchmark fixtures and timing thresholds. +- Warp grid optimization and device-resident neighbor-list metrics. +- Device-consumption benchmark metrics. +- SPH equation-kernel correctness and operation speedup. +- EOS/continuity capped benchmark metrics. +- Pressure-gradient capped benchmark metrics. +- One-step WCSPH chain correctness. +- Device-aware repeated-step refresh criteria. +- KDK leapfrog correctness. +- Periodic position wrapping correctness. +- Application-style Warp elliptical-drop smoke metrics. +- Artificial-viscosity acceleration correctness and smoke metrics. +- Tait EOS and per-particle sound-speed correctness and smoke metrics. +- Gaussian kernel correctness. +- XSPH leapfrog correction correctness. +- Device-reduced adaptive timestep correctness. +- CPU PySPH-primitive side-by-side image comparison. +- Optional parallel/Zoltan test slice after commit readiness. +- 3D dam-break (Lobovsky no-obstacle, ADR-0005): two-tier CPU parity -- hand-rolled + CPU EPEC (`LinkedListNNPS(dim=3)` + WendlandQuintic) field-by-field, and the real + `dam_break_3d_lobovsky.py` Application at matched checkpoints. Headline validators + are aggregate observables (KE, surge-front x, max height, density) which match to + fp32; near-rest pressure is at the fp32 Tait-EOS cancellation floor + (`rho0*c0^2*2^-23`, absolute tolerance) and recovers to ~1% relative once + developed. Packet: `experiments/2026-06-18_warp-dam-break-3d-runner/`. + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: validation-benchmarks` and `scope: global`. + +## Cross-aspect dependencies + +- Depends on: `gpu-nnps`, `particle-memory`, and `warp-backend`. +- Influences: success criteria and review evidence. diff --git a/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md new file mode 100644 index 000000000..6eae1f0e7 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - validation-benchmarks + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md new file mode 100644 index 000000000..b591bcad7 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md @@ -0,0 +1,9 @@ +# Open Questions - validation-benchmarks + +- [open] What does "blazing fast" mean in concrete speedup, throughput, and hardware terms? +- [open] What correctness and timing thresholds should promote elliptical drop + from smoke workload to first published particle-dynamics benchmark? +- [closed 2026-06-17] For apples-to-apples resolved Application comparisons, + the Warp runner should use PySPH-like adaptive timestep policy: `n_damp` + growth and temporary output-time landing caps. The old initial-`dt` capped + policy remains available as `--warp-timestep-policy current` for diagnostics. diff --git a/.ai/implementations/blast-from-the-past/aspects/warp-backend/context.md b/.ai/implementations/blast-from-the-past/aspects/warp-backend/context.md new file mode 100644 index 000000000..4b1225d8b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/warp-backend/context.md @@ -0,0 +1,150 @@ +--- +aspect: warp-backend +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: 2026-06-15T07:19:08 CET +last_reviewed: 2026-06-18T13:30:00 CEST +status: active +--- + +# Aspect: warp-backend + +## What this aspect covers + +NVIDIA Warp API choices, kernel model, memory layout assumptions, and how Warp could map onto PySPH's existing GPU abstractions. + +## Current understanding + +The first likely Warp integration boundary is a DeviceHelper-like mirror, not a replacement of ParticleArray host storage. The spec identifies required Warp primitives: device array creation, selective/full push and pull, Local-first partition/alignment, strided gather/scatter, resize/fill, add/remove/extract/append, and min/max if parity with current helper is desired. + +Warp imports successfully in the active environment as version `1.14.0`. Before code, decide whether Warp appears as a new `backend='warp'`, a CUDA backend variant, or a separate helper. + +ADR-0002 accepted the DeviceHelper-like mirror direction. The prototype adds `pysph/base/warp_device_helper.py`, exposes `backend='warp'` through ParticleArray backend resolution, and uses Warp gather kernels for alignment over scalar and strided properties. It now also supports remove, remove-tagged, add, append, extend, and extract through ParticleArray public methods. Focused Warp helper tests cover the main prototype surface and pass against a rebuilt `pysph.base.particle_array` extension. + +Warp equation kernels now cover the current elliptical-drop smoke formulation: +CubicSpline/Gaussian kernel selection, summation density, isothermal/Tait EOS, +continuity, pressure-gradient acceleration, Monaghan artificial viscosity, +XSPH correction, KDK leapfrog, WCSPH PEC-style continuity-density staging, +periodic position wrapping, and device-reduced WCSPH adaptive timestep factors. +The leapfrog helper exposes scalar policy controls for adaptive timesteps: +`adaptive_dt_scale` applies PySPH-style damping after the device reduction and +`step_dt_max` caps only the current physical step, leaving full particle state +device-resident. + +## Key sub-topics + +- Warp version/API surface - Active environment has Warp `1.14.0`; confirm documentation set with team. +- Kernel launch model for partition/gather/scatter kernels - Confirm with prototype. +- Compatibility with existing PySPH GPU pathways. +- Backend naming and ownership ADR. +- Next kernel family decision: move add/remove/extract/append growth internals from host-side NumPy concatenation to fully device-side Warp kernels. +- Tutorial documentation added at `docs/source/tutorial/warp_particle_array.rst`. +- Adaptive timestep reductions currently transfer only the final scalar `dt` + back to Python because launch parameters remain host scalars. Runner-level + damping and checkpoint caps are scalar-only policy operations. +- The PySPH Application parity step uses device-side saved state plus + continuity-density PEC stages; the original summation-density KDK path remains + the compatibility default. +- ADR-0003 adopts dynamic Warp equation-group code generation + (`pysph/base/warp_codegen.py`), mirroring PySPH's equation/group transpilation + on the GPU. A `WarpEquation` block declares its source/dest/out arrays, the + shared per-pair quantities it needs (`dx`, `rij`, `hij`, `grad`, `wij`, + `vij*`), and `initialize`/`loop`/`post_loop` source snippets; a group unions + the signature, computes shared geometry once, inlines each block's per-pair + loop into one neighbor traversal accumulating into shared `_acc_` + registers, and emits one cached, JIT-compiled kernel per + `(ordered equation signatures, dtype)`. Kernels are materialized by templating + source, registering it in `linecache`, and wrapping with + `wp.Kernel(func=..., source=...)`; the generated namespace is seeded with the + module's device `wp.func`s so Warp resolves them. Fusion is thus a property of + grouping rather than hand-written, and the f32/f64 split collapses into a + dtype parameter. +- First consumer: the continuity-density PEC half-stage fuses pressure gradient, + Monaghan viscosity, continuity, and XSPH (blocks in + `_WCSPH_CONTINUITY_BLOCKS`) into one generated kernel via + `compute_wcsph_accel_continuity`. The summation-density path, the Euler step, + the standalone per-equation helpers (kept as the trusted oracle), and the + flat adaptive `_wcsph_dt_factors` traversal are unchanged; migrating them onto + the generator is the ADR-0003 follow-up. +- ADR-0004 adds `neighbor_mode='grid'` to the generator: the same fused kernel + body, but the flat `starts/lengths/neighbors` loop is replaced by a direct + uniform-grid cell-list walk with the support cutoff inline (geometry split + pre/post cutoff; the cell-block walk wraps the equation snippets via + `_reindent`; `neighbor_mode` is part of the structural cache key). The + continuity hot path (`compute_wcsph_accel_continuity` and + `compute_wcsph_adaptive_timestep` via hand-written grid-direct + `_wcsph_dt_factors_grid_{f32,f64}`) now defaults to grid mode and builds no + flat CSR neighbor list; `neighbor_mode='flat'` is retained for the oracle and + host-query paths. This is the gpu-nnps cache-build optimization landing on the + backend; see the gpu-nnps aspect for the parity/perf evidence. +- ADR-0003 follow-up (generator migration): `warp_codegen` is now the single + source for every neighbor-loop kernel; the ~14 duplicated hand `@wp.kernel`s + (`_summation_density`, `_continuity`, `_pressure_gradient`, + `_artificial_viscosity`, `_xsph_correction`, `_wcsph_dt_factors{,_grid}`, + f32+f64) are retired. New: `accumulate_outputs` (seed `_acc_` from the + existing `d_[i]` for read-modify-write composition, used by the standalone + additive viscosity); `SummationDensity` and `WcsphCflFactor` blocks (the CFL + `dt_cfl` is a free-form `wp.max` neighbor reduction, `dt_force` a per-particle + `post_loop`); a shared `_run_equation_group` launcher (flat/grid, accumulate, + cross-array). The standalone helpers keep their signatures/cross-array and now + reject non-canonical `out_prop`/`out_props`. The generated kernel `func_name` + is now a deterministic md5 of the structural cache key (was + `len(_KERNEL_CACHE)`), so the source is byte-stable across runs and Warp's + on-disk kernel cache hits instead of recompiling cold every session. EOS, + integrator, and dt init/reduce/finalize reduction kernels are unchanged. +- 3D dam-break WCSPH physics (ADR-0005, additive). New device funcs: Wendland + quintic value + dwdq (`dim==1/2/3` normalization) routed through the shared + `_kernel_value`/`_kernel_dwdq` routers as kernel id 2 (cubic 0 / gaussian 1 + branches untouched); `_apply_body_force` (ramped gravity into au/av/aw under + `dim>1`/`dim>2` guards); `_tait_eos_hg_correction` (clamp `rho>=rho0` so wall + `p>=0`). New host driver `wc_sph_dam_break_step`: an E-P-E-C (== reference + `EPECIntegrator`) continuity-density step that sums fluid accel + density over + `[fluid, walls]`, takes wall density from `[fluid]` only, XSPH from fluid only, + and holds walls fixed via zero accel through the shared `wcsph_pec_stage`. All + composed from the existing generated equation blocks with + `accumulate_outputs=True` over each source -- no new neighbor-loop code, no edit + to ids 0/1, the generated 2D source, or the single-array elliptical-drop step. + The 2D generated source is byte-identical (md5-pinned by + `test_2d_path_generated_source_is_byte_identical_to_golden`); the router gained + an additive `id==2` branch (logic-preserving; a one-time recompile can occur). + Wendland inflates every generated kernel's PTX (~10-13 MB), so cold compiles are + now multi-minute per large 3D fused kernel. +- Dam-break perf follow-up: the fluid acceleration+density blocks were **fused** + via `_WCSPH_DAM_BREAK_FLUID_BLOCKS` (PressureGradient + ArtificialViscosity + + ContinuityEquation) run as one generated kernel per source + (`accumulate_outputs=True`) instead of three separate single-block launches -- + one neighbour walk / one per-pair geometry for all three. 1.23x faster Warp + step at >1M (0.415 -> 0.337 s/step; ~13-15x vs single-thread CPU). XSPH + (fluid-only) and wall continuity (fluid->wall) stay separate (heterogeneous + source/destination), so it is not a single fused kernel like the single-array + elliptical path. New cache entry; the 2D generated source stays byte-identical + (guard `test_2d_path_generated_source_is_byte_identical_to_golden` passes). +- Floating-body rigid P2 (ADR-0006 amendment) is fully device-resident: + `WarpRigidBodyState` keeps the f64 reduction/finalize/COM/inertia/force/torque/ + RK2 linear+angular state on the GPU; standalone Warp kernels perform the + symmetric 3x3 angular solve, RK2 midpoint/full updates, rigid velocity, and + particle position stages. The production stage has no host finalize, pull, + `.numpy()`, or explicit synchronization. `_rigid_finalize_moments` remains a + NumPy parity oracle/explicit host-result helper. All additions are outside + generated equation source, so the 2D golden source remains byte-identical. +- Floating-body P3 (ADR-0006) adds deterministic fluid/rigid coupling without + source atomics: `LiuFluidAcceleration` accumulates body pressure acceleration + onto fluid destinations and `LiuBodyReaction` performs the reversed + fluid-to-body pass into destination force registers. A static + `RigidNumberDensity` pre-pass, device rigid density/body-force staging, and + sibling `wc_sph_dam_break_rigid_step` compose these with the fixed-wall EPEC + path and P2's GPU-resident 6-DOF RK2. The existing fixed-wall driver and 2D + generated source are unchanged; final Warp SPH suite `54 passed`. + +## References for this aspect + +- `.ai/implementations/blast-from-the-past/references/index.md` + +## Decisions affecting this aspect + +- Filter `.ai/implementations/blast-from-the-past/decisions/index.json` for `scope: warp-backend` and `scope: global`. + +## Cross-aspect dependencies + +- Depends on: `particle-memory` - device data ownership. +- Influences: `gpu-nnps` - backend-specific neighbor kernels. diff --git a/.ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md b/.ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md new file mode 100644 index 000000000..344c6cd4c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md @@ -0,0 +1,3 @@ +# Known Issues - warp-backend + +- (none yet) diff --git a/.ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md b/.ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md new file mode 100644 index 000000000..6d522a27a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md @@ -0,0 +1,6 @@ +# Open Questions - warp-backend + +- [open] Active environment imports Warp `1.14.0`; which NVIDIA Warp documentation set should be treated as authoritative? +- [open] What minimum NVIDIA GPU/driver/CUDA assumptions are acceptable? +- [open] Should Warp be exposed as `backend='warp'` or as a CUDA backend implementation detail? +- [open] Which Warp primitive should be implemented first: push/pull, alignment, or add/remove/extract kernels? diff --git a/.ai/implementations/blast-from-the-past/conventions.md b/.ai/implementations/blast-from-the-past/conventions.md new file mode 100644 index 000000000..aaa8a4356 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/conventions.md @@ -0,0 +1,16 @@ +# Conventions + +## Inherited from Host + +- Use existing PySPH build and test commands before introducing new tooling. +- Keep Cython declarations and implementations consistent across `.pxd` and `.pyx` files. +- Preserve existing public names and import surfaces unless an ADR explicitly approves a change. +- Treat slow and parallel tests as separate validation tiers. + +## Specific to blast-from-the-past + +- Keep Warp exploration behind explicit decisions until a stable integration path exists. +- Record performance claims as experiments with hardware, command, inputs, output numbers, and correctness checks. +- Do not call a benchmark "fast" without a baseline and repeated measurement. +- Use `Confirm with team` where Prabhu guidance or hardware assumptions are not yet documented. +- Never copy credentials or private machine paths that expose secrets into `.ai/`. diff --git a/.ai/implementations/blast-from-the-past/current.md b/.ai/implementations/blast-from-the-past/current.md new file mode 100644 index 000000000..5a3e034f8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/current.md @@ -0,0 +1,64 @@ +# Current - blast-from-the-past + +Updated: 2026-07-07T09:00:00 CEST by claude + +**Status:** The Warp backend has device-mirrored particle state, grid-direct +3D WCSPH, generated/fused equation groups, periodic neighbors, validated 2D +elliptical-drop and 3D dam-break cases, and ADR-0006 P0-P3 floating-rigid-body +support. P2 (`bb3843f7`) keeps moment reduction, 3x3 angular solve, RK2 state, +and rigid motion on the GPU. P3 adds deterministic two-pass Liu fluid/rigid +coupling, static rigid number density, device density/body-force staging, and +the sibling `wc_sph_dam_break_rigid_step` without modifying the fixed-wall +driver. + +**Active aspects:** warp-backend, gpu-nnps, particle-memory, +validation-benchmarks, host-integration. + +**In-flight experiment:** +`experiments/2026-06-19_warp-floating-body-rigid` (ADR-0006). P0-P3 are done; +P3 is approved by @prabhu and committed locally. P4 contact/long-horizon fidelity and the +photorealistic animation remain. + +`experiments/2026-07-06_warp-adaptive-particle-resolution-p0` is active under +the approved dynamic-APR plan. The first checkpoint proves the existing +multi-solid driver can run a fixed Kleefsman obstacle: a warm 1,000-fluid + +3,824-wall + 4-obstacle case ran 250 steps to `t=0.258455`, remained finite, +developed 150.147 kPa maximum obstacle pressure, and kept obstacle device +coordinates bit-identical. + +The multilevel-GPU-NNPS milestone (`plans/2026-07-06_warp-multilevel-gpu-nnps.md`, +status in-progress) has landed steps 1-3 and ADR-0007 (Proposed). New module +`pysph/base/warp_multilevel_nnps.py` provides `MultilevelGridWarpNNPS`: discrete +half-open smoothing-length levels, a flattened per-level global cell list, and +exact variable-stencil cross-level traversal. Construction is device-resident +(GPU level assignment + count/max-h/AABB reductions, only O(nlevels) scalar +readback). All eight kill-gate fixtures pass, a synthetic localized-refinement +fixture shows ~9x lower candidate work (197k vs 1.77M pairs) with identical +accepted sets, and `warp_codegen` gained `neighbor_mode='multilevel'` so +generated SPH equation groups consume the multilevel structure directly +(summation density matches the uniform grid in 2D and 3D). Multilevel + periodic +is refused. Remaining before ADR-0007 is Accepted: adaptive-timestep + fused- +group multilevel parity, fp64 exercise, and the dense-vs-sparse memory check. + +**Latest validation:** Final P3 Warp SPH suite: `54 passed, 2 warnings`. +The 7,458-particle coupled first-plunge transient ran 241 steps to `t=0.200603`, +remained finite with device error 0, moved/rotated the body from computed fluid +reaction, and preserved relative geometry to `1.90e-6`. The review image and +metrics are in +`reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md`. + +**Open approvals:** P3 and 3D dam-break reviews are approved by @prabhu. PR +#435 remains an upstream publication item, not a local review blocker. + +**Next action:** Continue the multilevel milestone: adaptive-timestep and fused +continuity/pressure/viscosity multilevel parity, an fp64 exercise, then the +dense-vs-sparse memory check to move ADR-0007 to Accepted. Run the three warp +test files SEPARATELY (`test_warp_nnps.py` 34, `test_warp_codegen.py` 10, +`test_warp_sph.py` 57) -- the combined single-process command hangs +pre-existingly on the WSL2 PTX-JIT. P0 stencil-convention reconciliation remains +a prerequisite for production APR weights; floating-body P4 remains queued. + +**Known validation limitation:** Compyle 0.9.1 on Python 3.14 cannot run the +shipped CPU rigid Application (`ast.Str` removal). P3 uses direct NumPy +primitive parity plus end-to-end GPU smoke/transient validation; the review +discloses that a monolithic hand-staged EPEC oracle was not added. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md new file mode 100644 index 000000000..dd4d7757f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md @@ -0,0 +1,42 @@ +--- +type: decision +id: ADR-0001 +date: 2026-06-15 +author: @kunalpuri-prediqt +scope: global +status: Accepted +supersedes: [] +relates_to: [] +depends_on: [] +conflicts_with: [] +--- + +# ADR-0001: Adopt .ai memory system for blast-from-the-past + +## Context + +`blast-from-the-past` needs persistent implementation-scoped memory for NVIDIA Warp exploration inside PySPH without turning the memory into a catalogue of the entire host project. + +## Decision + +Adopt the `.ai/` memory system for `blast-from-the-past`, with ADR frontmatter as the decision source of truth, generated decision index/graph files, validation scripts, closeout discipline, experiment tracking, references, and a pre-commit hook. + +## Rationale + +The implementation has open-ended design and validation work across GPU backend choices, NNPS, particle memory, Cython boundaries, benchmarks, and host integration. A scoped memory system keeps those threads explicit and mechanically validated. + +## Alternatives considered + +- Use ad hoc chat history only: rejected because decisions and benchmark evidence would be hard to audit. +- Add permanent host docs immediately: rejected because the implementation is exploratory and needs working memory before stable documentation. + +## Consequences + +- Positive: plans, ADRs, closeouts, experiments, and reviews have a consistent place. +- Positive: validation checks catch malformed memory, stale decision graph files, boundary drift, and possible secrets. +- Negative: small process overhead before implementation work begins. + +## Follow-ups + +- Define concrete benchmark success criteria for "blazing fast particle dynamics." +- Capture exact NVIDIA Warp documentation/version and Prabhu guidance as references. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md new file mode 100644 index 000000000..addb2ebf0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md @@ -0,0 +1,45 @@ +--- +type: decision +id: ADR-0002 +date: 2026-06-15 +author: @kunalpuri-prediqt +scope: particle-memory +status: Accepted +supersedes: [] +relates_to: [ADR-0001] +depends_on: [] +conflicts_with: [] +--- + +# ADR-0002: Warp Device Mirror For ParticleArray + +## Context + +The ParticleArray spec shows that `ParticleArray` currently owns host `BaseArray` storage, exposes Cython declarations, and delegates non-cython device behavior to `DeviceHelper`. + +The public contract includes `get_carray()`, NumPy/readback paths, pickle/output metadata, strided properties, constants, and Local-first tag alignment. + +Warp `1.14.0` imports in the active environment. + +## Decision + +Implement NVIDIA Warp first as a DeviceHelper-like mirror for ParticleArray rather than replacing host `BaseArray` ownership. + +## Rationale + +This keeps the existing Cython and host API stable while allowing Warp kernels to prove parity for device creation, push/pull, alignment, and particle mutation primitives. + +## Alternatives considered + +- Replace ParticleArray storage with Warp arrays. This may be faster eventually, but it risks breaking Cython callers and output/restart compatibility before the backend contract is proven. +- Hide Warp under the existing CUDA backend. This may reduce API surface, but it makes backend selection and comparison against current CUDA/Compyle behavior less explicit. + +## Consequences + +- A new helper or helper mode must preserve current push/pull and metadata semantics. +- Performance work begins with mirror overhead included. +- A later ADR can revisit authoritative Warp ownership after behavior parity and benchmarks exist. + +## Follow-ups + +- Implement the focused Warp mirror prototype plan. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md new file mode 100644 index 000000000..6095c57ad --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md @@ -0,0 +1,103 @@ +--- +type: decision +id: ADR-0003 +date: 2026-06-17 +author: @kunalpuri-prediqt +scope: warp-backend +status: Accepted +supersedes: [] +relates_to: [ADR-0002] +depends_on: [] +conflicts_with: [] +--- + +# ADR-0003: Adopt dynamic Warp equation-group code generation + +## Context + +PySPH's defining strength is composability: physics is written as small +`Equation` building blocks (`initialize` / `loop` / `post_loop`), bundled into +`Group`s, and the framework's transpiler generates one fused kernel per group +for each backend (Cython, OpenCL/CUDA via compyle). Fusion is a property of +grouping, not something authored by hand. + +The current Warp prototype instead hand-writes one `@wp.kernel` per equation in +both `f32` and `f64`. The cache-reuse slice exposed the cost: a single +continuity-density PEC half-stage launches four separate neighbor-loop kernels +(pressure gradient, artificial viscosity, continuity, XSPH) over the same +~45M-entry neighbor cache, each re-reading neighbor indices and recomputing the +same per-pair geometry. Hand-fusing them would help, but it is a one-off that +must be re-authored for every new equation set and doubles again for `f64`. + +A spike on the active machine (RTX 4060, Warp `1.14.0`) confirmed Warp supports +runtime kernel generation: a kernel body assembled at runtime from snippet +lists, materialized as a function (via `linecache` + `exec`) and wrapped with +`wp.Kernel(func=..., source=...)`, JIT-compiled in ~1.5 s (then hash-cached) and +produced correct device results. `wp.Kernel.__init__` exposes an explicit +`source=` parameter, which avoids fragile `inspect.getsource` behavior for +generated functions. + +## Decision + +Adopt dynamic, composable code generation for Warp SPH kernels as the backend's +kernel-construction model, mirroring PySPH's equation/group transpilation. + +A `WarpEquation` block declares the source/dest arrays it reads and the arrays +it writes, and contributes `initialize` / `loop` / `post_loop` source snippets. +A `WarpGroup` of blocks unions the array signature, computes shared per-pair +geometry once (`dx,dy,dz,rij,hij`, kernel gradient/value, velocity diffs), +inlines each block's `loop` snippet into a single neighbor traversal, and emits +one cached, JIT-compiled kernel per `(equation-set structure, dtype, dim)` +signature. The kernel id (cubic/gaussian) stays a runtime argument dispatched by +the existing device `wp.func`s. + +The first consumer is the continuity-density PEC half-stage: continuity + +pressure gradient + artificial viscosity + XSPH composed into one generated +kernel. + +## Rationale + +- Restores the PySPH composition model on the GPU: new equations and new + combinations fuse automatically instead of requiring a hand-written kernel. +- Fusion (one traversal instead of four) becomes a property of grouping, giving + the performance win as a side effect of the right abstraction. +- Collapses the `f32`/`f64` duplication into a dtype parameter of the generator. +- The mechanism is proven feasible on the target hardware before committing. + +## Alternatives considered + +- Hand-write a monolithic fused kernel per solver. Fast to a benchmark number + and lowest immediate risk, but a one-off that betrays the composition model, + must be re-authored per equation set, and keeps the `f32`/`f64` split. Likely + throwaway once the generator lands. +- Compose only via `wp.func` building blocks called from a static outer kernel. + Reuses device code but still requires a hand-written outer kernel that + enumerates the equations and recomputes shared geometry per call site; it does + not give automatic, group-driven fusion. +- Defer GPU fusion and rely on PySPH's existing OpenCL/CUDA transpiler. Out of + scope: the implementation goal is a Warp-backed path. + +## Consequences + +- A new code-generation module (e.g. `pysph/base/warp_codegen.py` or an + addition to `warp_sph.py`) becomes part of the boundary and must be tested: + generated kernels validated against the existing hand-written per-equation + kernels (trusted oracle) and CPU references. +- A first cut targets forward-only, single-group, self-interaction + (`src == dst`) physics with the shared quantities the four equations need. + Gradient/adjoint support, multi-group/iterated-group orchestration, and + cross-array source/dest fusion are explicitly deferred. +- Two representations of the four equations coexist transitionally: the new + blocks (used by the generator) and the existing hand-written kernels (kept as + oracle and for the summation-density path). A later slice migrates the + remaining helpers onto the generator and removes the duplication. +- Generated-source ergonomics (readable errors when a snippet is malformed, + stable cache keys) must be handled or debugging regresses. + +## Follow-ups + +- Implement plan `2026-06-17_warp-fuse-neighbor-loop-equations` (revised to the + generator design) with the continuity stage as the first consumer. +- Follow-up slice: express the standalone per-equation helpers and the + summation-density and adaptive-timestep-factor paths via the generator and + retire the duplicated hand-written kernels. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md new file mode 100644 index 000000000..13f68ed2e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md @@ -0,0 +1,119 @@ +--- +type: decision +id: ADR-0004 +date: 2026-06-18 +author: @kunalpuri-prediqt +scope: gpu-nnps +status: Accepted +supersedes: [] +relates_to: [ADR-0003] +depends_on: [ADR-0003] +conflicts_with: [] +--- + +# ADR-0004: Grid-direct neighbor traversal for the WCSPH continuity hot path + +## Context + +After ADR-0003 fused the continuity-density PEC half-stage into a single +generated neighbor-loop kernel, the segmented million-particle profile +(`nx=565`, 1,002,885 particles, 10 fixed steps) showed the equation-kernel time +collapse (~0.064-0.088 s -> 0.011-0.014 s per step) and the **neighbor-cache +build** become the dominant per-step cost (~0.034-0.046 s, ~45-50% of the step +wall). + +`UniformGridWarpNNPS.build_neighbor_cache_gpu` materializes a flat CSR neighbor +list per call: a **count** traversal of the 3x3(x3) cell block per destination +(`_grid_neighbor_lengths`), an exclusive `array_scan`, a host readback of the +full `lengths` array (~4 MB at 1M) plus a host `np.sum` to size the flat array +(forcing a device sync), a `wp.empty(total)` allocation, then a second **fill** +traversal of the same cells (`_grid_neighbor_fill`). The fused equation kernel +then walks that flat list a third time. + +The flat list paid off when a half-stage launched 4-8 separate equation kernels +over it (build once, read many). After ADR-0003 there is a single fused +equation launch per half-stage, so the list is built (two traversals + readback ++ allocation) only to be read once. + +Two consumers traverse neighbors on the continuity path: the fused equation +kernel (always) and the adaptive `_wcsph_dt_factors` CFL kernel (only when +`adaptive_dt` is on). The flat-list build also backs the summation-density +path, the per-equation oracle helpers, the host `get_nearest_particles` query +API, and `compute_neighbor_sum` -- none of which are on the continuity hot path. + +## Decision + +On the WCSPH continuity-density hot path, traverse the uniform-grid cell list +**directly** inside the consuming kernels instead of materializing a flat CSR +neighbor list. + +The ADR-0003 code generator gains a `neighbor_mode='grid'` that emits the same +fused kernel body, but with the flat `starts/lengths/neighbors` loop replaced by +the cell-block iteration (`_build_grid` cell list + bounds), guarded by the same +support cutoff (`rij2 < (radius_scale*h_i)^2 or rij2 < (radius_scale*h_j)^2`) so +the kernel sees exactly the neighbor set the flat list contained. The shared +geometry is split: `dx,dy,dz,rij2` are computed before the cutoff; `rij,hij`, +the kernel gradient/value, and the relative velocities inside it. Equation +blocks are unchanged. The adaptive `_wcsph_dt_factors` kernel gets a hand-written +grid-direct variant (transitional, per ADR-0003's duplication stance). + +`_wc_sph_pec_continuity_step` then builds only the grid (already cached per +`update()`), never the flat list. The grid build (cell-id/count kernel, scan, +scatter) stays -- it is the irreducible spatial index; what is removed is the +per-half-stage count traversal, scan, host readback, allocation, and fill +traversal. + +## Rationale + +- The flat list is now built to be read once; collapsing it into the consuming + traversal removes two of the three per-half-stage neighbor traversals plus a + forced host sync and a large allocation, directly attacking the dominant cost. +- `neighbor_mode` is a natural extension of the ADR-0003 generator: fusion of + the *neighbor search* into the equation kernel, the same way equation fusion + was a property of grouping. The flat path stays as the default for the host + query API and the oracle, so nothing regresses. +- The support cutoff inside the grid loop reproduces the flat list's membership + exactly, so generated-vs-oracle parity is preserved and testable. + +## Alternatives considered + +- **Incremental tuning of the flat build**: replace the 4 MB readback with a + single scalar total from the scan tail, reuse `lengths/starts` device buffers + across builds, single-pass fill. Lower risk but leaves all three traversals in + place -- a modest win that does not remove the dominant term. Kept as the + documented fallback if grid-direct regresses on the profile (register + pressure / divergence from the cell walk inside the equation kernel). +- **Keep the flat list, persist it across the two half-stages within a step**: + the coordinates move between half-stages (`update(push=False)` rebuilds), so a + persisted list would be stale; rejected. +- **Defer**: the cache build is now the single largest per-step cost; deferring + leaves the headline benchmark bottlenecked. + +## Consequences + +- A `neighbor_mode='grid'` code path in `warp_codegen.py` (signature, geometry + split, cell-block loop, cutoff) and a grid-direct launch path in + `compute_wcsph_accel_continuity`; the structural cache key gains the mode so + flat and grid variants compile independently. +- A second representation of the CFL dt-factors kernel (flat + grid-direct) + coexists transitionally, consistent with ADR-0003; the generator migration + later absorbs it. +- fp32 accumulation order is unchanged versus the flat fused kernel (same block + order, same per-pair math); the only numerical difference is neighbor + *visitation order* within a destination (cell order vs CSR order), which can + shift fp32 sums ~1e-7. Guarded by the adaptive `nx=100` resolved step-count + check and generated-vs-oracle parity. +- Grid-direct iterates every occupant of the 27 cells and applies the cutoff + per pair; the flat path applied the cutoff once at build time. The profile is + the arbiter of whether the removed build/readback/alloc outweighs revisiting + the cutoff inside the (now single) consuming traversal. + +## Follow-ups + +- ADR-0003 follow-up still stands: migrate the per-equation oracle helpers, the + summation-density path, and the dt-factors kernel onto the generator and + retire the duplicated hand kernels (the grid-direct dt-factors variant added + here is part of what that migration absorbs). +- Once the continuity path no longer uses `build_neighbor_cache_gpu`, evaluate + whether the flat-list build can be narrowed to the host query API / oracle and + the summation path moved to grid-direct too. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md new file mode 100644 index 000000000..037788ce3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md @@ -0,0 +1,188 @@ +--- +type: decision +id: ADR-0005 +date: 2026-06-18 +author: @kunalpuri-prediqt +scope: warp-backend +status: Accepted +supersedes: [] +relates_to: [ADR-0003, ADR-0004] +depends_on: [ADR-0003] +conflicts_with: [] +--- + +# ADR-0005: Adopt additive 3D WCSPH physics for the Warp dam-break benchmark + +## Context + +The Warp backend has one validation case: a 2D free-surface elliptical drop. We +want a second, well-known 3D case -- the dam-break (Lobovsky no-obstacle first, +SPHERIC/Kleefsman obstacle case second) -- to strengthen the implementation and +the upstream PR. + +A 5-area code investigation found that the **lower layers are already fully +3D-capable** and 3D-validated by existing tests, with no changes required: + +- NNPS (`warp_nnps.py`): 3D cell binning (`cid = ix + iy*nx + iz*nx*ny`), a true + 27-cell `range(-1,2)^3` walk, `nz`/`zmin`/ghost-padding under `dim>2`, a 3D + periodic box. Tests `test_warp_nnps.py:252,286` already exercise dim=3. +- Codegen (`warp_codegen.py`): emits `dx/dy/dz`, `vijx/vijy/vijz`, the 3D triple + cell loop and 3D `cid`, and per-dim periodic min-image -- all `dim`-gated. +- Physics (`warp_sph.py`): Gaussian/cubic kernel normalization carries the + correct `dim==3` constants (`pi^-3/2 / h^3`, `1/(pi h^3)`); every step path + (Euler/KDK/continuity-PEC) reads/writes `z` and `w` under `dim>2` guards. + Per-equation device kernels have passing dim=3 cross-array parity tests + (`test_warp_sph.py:361,512,604`). + +What a dam-break needs that the elliptical drop never exercised (confirmed +absent by grep): + +1. **Body force / gravity** -- there is no `gx/gy/gz` term anywhere; without + `gz=-9.81` the dam-break is identically static. +2. **Multi-array stepping** -- every driver passes `src==dst` (single array). + A dam-break needs fluid accelerations summed over `[fluid, wall(, obstacle)]` + and wall density from `[fluid]`. +3. **Solid walls** -- fixed (non-integrated) boundary particles whose pressure is + enforced via `TaitEOSHGCorrection` (clamp `p>=0`, recompute `rho` from `p`). + No such concept exists today. +4. **WendlandQuintic kernel** -- both CPU references use `WendlandQuintic(dim=3)`, + but `_kernel_id` (warp_sph.py:1066) only knows cubic(0)/gaussian(1). + +The hard constraint is **disk-cache stability**: Warp compiles generated kernels +and caches them on disk keyed by a deterministic md5 of the kernel source +(established when the order-dependent name bug was fixed). Any incidental edit to +an *existing* generated equation block's source string would silently force a +recompile and could perturb the committed 2D elliptical-drop baseline. + +## Decision + +Add the four missing WCSPH features as **purely additive extensions**, so that +the 2D elliptical-drop code path and its on-disk kernel cache remain +**byte-identical**: + +1. **WendlandQuintic as a new kernel id.** Add `value` + `dwdq` device functions + with correct `dim==1/2/3` normalization and map `'wendland'` to a new id. + Leave ids 0/1 (cubic/gaussian) and their emitted source unchanged. +2. **Gravity as a standalone additive velocity kernel** (`_apply_body_force_*`: + `u += gx*dt` always, `v += gy*dt` under `dim>1`, `w += gz*dt` under `dim>2`), + with a linear ramp over `n_damp` steps to match the CPU reference startup + (`n_damp=50`). Defaults `gx=gy=gz=0`. It is applied in the step driver, never + folded into `PressureGradient`/`ContinuityEquation`/the PEC-stage emitted + source. With `g=0` the existing step is unchanged. +3. **Multi-array (fluid + solid) stepping.** Add a driver that, per destination + array, sums neighbor contributions over a list of source arrays, reusing the + already-`src!=dst`-capable equation groups and NNPS query (no codegen/NNPS + change). Solid arrays are *fixed*: their `rho`/`p` update but their position + and velocity are not integrated. +4. **Solid pressure via a new `TaitEOSHGCorrection` device kernel** (clamp + `p>=0`, recompute `rho` from `p`) applied to solid arrays only, mirroring + `pysph/sph/wc/basic.py` / `scheme.py:422-426`. + +The 2D path keeps `g=0`, single-array stepping, ids 0/1, and no HG correction. +The precise invariant (verified, see Validation): the **generated group source** +for the cubic/gaussian 2D kernels is **byte-identical** (the kernel choice is a +runtime `kernel_id`, not source), enforced by +`test_2d_path_generated_source_is_byte_identical_to_golden`, and the cubic +(id 0) / gaussian (id 1) **numerical code paths** in the shared +`_kernel_value`/`_kernel_dwdq` routers are **unchanged** (only an additive +`if kernel_id == 2` branch was added). The router *device-function source* did +grow by that branch, so Warp's on-disk module hash for kernels that call the +router changes -- a one-time, **logic-preserving recompile** of the warm cache +can occur. It does not change the cubic/gaussian results and does not perturb +the committed 2D elliptical-drop baseline. (Earlier drafts said "on-disk cache +byte-identical"; the accurate statement is "emitted source byte-identical + +logic-preserving router extension".) + +The first target is the **Lobovsky no-obstacle** case (fluid + one wall array, +`tf=2.5 s`), then the SPHERIC obstacle case (three arrays, `tf=6.0 s`). + +## Rationale + +- Additive-only is the smallest correct change that satisfies the + disk-cache-stability constraint: new kernel id, separate gravity kernel, + separate multi-array driver, new solid-EOS kernel -- none edit existing + generated source. +- The expensive infrastructure (3D NNPS, 3D codegen, dim-correct kernels, 3D + step paths) is already present and validated, so the work is confined to the + physics features the 2D drop never needed plus a runner -- not a dimensional + rewrite. +- Cross-array per-equation kernels are already 3D-validated, so multi-array + stepping is driver wiring plus one solid-EOS kernel, not new neighbor-loop code. + +## Alternatives considered + +- **Fold gravity into the generated momentum/PEC source.** Rejected: it mutates + an existing generated kernel's source string, busting the 2D disk cache and + risking a perturbed baseline. A standalone `g`-kernel keeps the contract. +- **Reuse cubic/gaussian instead of adding Wendland.** Rejected for the parity + case: the CPU references use `WendlandQuintic(dim=3)`; matching the kernel is + needed for an honest CPU-vs-Warp comparison. (A cubic/gaussian dam-break could + be run as an extra, but the headline parity uses Wendland.) +- **Generalize the single-array step in place** rather than add a new driver. + Rejected: it would change the existing step's launch wiring and risk the 2D + path; a separate multi-array driver leaves the single-array path intact. +- **EPEC integrator parity.** The CPU references use `EPECIntegrator`; the Warp + backend implements a single PEC stage. Either run the CPU reference with + `PECIntegrator` for strict per-particle parity, or document EPEC-vs-PEC as a + known small difference. Decision deferred to the plan; aggregate observables + (KE, surge front, probe pressure vs experiment) are the primary validators + because fp32-vs-fp64 chaotic flow defeats long-horizon per-particle parity. + +## Consequences + +- New device functions and a kernel id in `warp_sph.py` (Wendland, body force, + HG correction) and a new multi-array dam-break step driver; all additive. +- A 3D dam-break experiment packet (runner + initial condition via + `DamBreak3DGeometry` + two-tier CPU-parity harness) and dim=3 tests for the + fused/grid-direct/adaptive paths plus the new kernels. +- The 2D elliptical-drop path keeps its kernel ids and its *generated source* + byte-identical (test-asserted); the cubic/gaussian numerical results are + unchanged. The shared kernel-id router gained an additive `id==2` branch, so a + one-time logic-preserving recompile of the on-disk cache can occur without + changing results. +- Validation shifts from an analytic locus (elliptical drop) to **aggregate / + experimental observables** (total KE, surge-front position, max height, + probe-point pressure vs `db_exp_data` Lobovsky/Kleefsman data), since the 3D + dam-break has no closed-form solution and fp32-vs-fp64 trajectories diverge. + +## Follow-ups + +- SPHERIC/Kleefsman obstacle case (third particle array) after Lobovsky lands. +- Optional: a cubic/gaussian dam-break variant for an additional cross-check. +- Performance characterization (per-step wall, cross-GPU) once correctness is + established -- separate from this correctness-focused ADR. +- Revisit whether the 27-cell stencil should special-case the `dzc` loop on + `dim` for 2D speed (explicitly out of scope here to keep the 2D path + byte-identical). + +## Validation (2026-06-19) + +Implemented and validated; experiment packet +`experiments/2026-06-18_warp-dam-break-3d-runner/`. + +- **Backend unit/parity (pytest):** the new-feature tests pass -- + WendlandQuintic(dim=3) summation density, ramped body force (+ 2D guard), + `TaitEOSHGCorrection`, and the two-array 3D dam-break step (walls fixed, all + finite, gravity drives the fluid down); plus the dim=3 codegen-grid, + fused/grid-direct, and adaptive-timestep pins; plus the new byte-identical + source guard. +- **Tier-1 (hand-rolled CPU EPEC):** fluid x/y/z/u/v/w + fluid/wall rho match + Warp (fp32) to ~1e-8 relative; pressure matches in absolute terms at the fp32 + Tait-EOS cancellation floor. +- **Tier-2 (real `dam_break_3d_lobovsky.py` Application):** at matched + checkpoints, KE / surge-front / max-height / density agree to fp32 scale; + pressure is ~13% relative near rest (fp32 floor) and recovers to ~1% relative + once the column collapse develops a real pressure field. + +Resolved deferrals from this ADR: + +- **EPEC vs PEC** (Alternatives): `wc_sph_dam_break_step` is E-P-E-C (re-evaluates + before the predictor), matching the reference `EPECIntegrator`. No + `PECIntegrator` substitution is needed; the reference runs with its native + integrator. +- **Gravity ramp vs `n_damp`** (Decision step 2): PySPH `n_damp` damps the + *timestep* (`0.5*(sin(pi*(-0.5+(count+1)/n_damp))+1)`), not gravity. The runner + matches PySPH by applying that factor to the adaptive `dt` (full gravity); the + additive `gravity_ramp` feature exists but is unused for this benchmark. +- **`c0`:** matches the reference scheme constant `10*sqrt(2*9.81*0.55) ~ 32.85` + (its initial-`dt` `get_max_speed` form `~44.29` is used only to cap `dt`). diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md b/.ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md new file mode 100644 index 000000000..95b616c76 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md @@ -0,0 +1,212 @@ +--- +type: decision +id: ADR-0006 +date: 2026-06-19 +author: @kunalpuri-prediqt +scope: warp-backend +status: Accepted +supersedes: [] +relates_to: [ADR-0003, ADR-0004, ADR-0005] +depends_on: [ADR-0005] +conflicts_with: [] +--- + +# ADR-0006: Adopt additive rigid-body coupling for the Warp floating-body benchmark + +## Context + +ADR-0005 gave the Warp backend a 3D dam-break (Lobovsky no-obstacle): fluid + +fixed solid walls. The next benchmark is a **floating / rigid body** -- the +dramatic 3D dam-break surge tossing a floating box (mirrors +`pysph/examples/rigid_body/dam_break3D_sph.py`) -- and the **deliverable is a +photorealistic animation** of a GPU (Warp) simulation. The animation is rendered +out-of-band (splashsurf -> Blender Cycles); this ADR is only about producing the +GPU simulation data. + +A fixed wall and a floating body differ by exactly one thing: the wall is *not +integrated* (the shared PEC stage leaves it in place while its density/pressure +respond to the fluid), whereas a floating body **moves under integrated 6-DOF +rigid-body dynamics**. So the floating body is the existing multi-array +dam-break path with "fixed" lifted: sum the fluid force + torque over the body's +particles, integrate the body's centre-of-mass linear + angular momentum, and +rebuild each body particle's position/velocity from the rigid transform. +Buoyancy is then emergent from the pressure field -- there is no explicit +buoyancy term. + +PySPH ships the reference machinery in `pysph/sph/rigid_body.py` +(`RigidBodyMoments`, `RigidBodyMotion`, `LiuFluidForce`/`AkinciRigidFluidCoupling`, +`RK2StepRigidBody`). Two constraints shape the design: + +1. **Disk-cache stability (inherited from ADR-0005).** Any edit to an existing + *generated* equation block's source string, or to the shared kernel-id + router source, risks busting the 2D elliptical-drop on-disk cache / + perturbing the committed baseline. The new work must be **purely additive**. +2. **No runnable CPU reference on this env.** `rigid_body.py`'s matrix + `declare()` statements hit `compyle 0.9.1`'s use of the Python-3.12-removed + `ast.Str` on the Python 3.14 venv, so the shipped CPU rigid-body Application + does not run here. Validation is therefore against a **faithful numpy + reimplementation** of `RigidBodyMoments`/`Motion` (the tier-1 style ADR-0005 + already used), with the full CPU Application (tier-2) deferred behind a + compyle upgrade. + +A P0 kill-test (`p0_rigid_reduce_kill_test.py`) de-risked the one genuinely new +GPU primitive before any backend edit: on an asymmetric synthetic body, an +`atomic_add` sum-reduction reproduced the 16-slot `RigidBodyMoments` `mi` vector +exactly, the host finalize matched numpy to ~6e-14, and the device transform was +exact -- but **fp32 `atomic_add` is non-deterministic** (~3e-6 run-to-run +spread, because it is order-dependent / non-associative, unlike the +order-independent `atomic_max` of the dt-reduce). Accumulating in f64 collapses +the spread to f64 round-off (~1e-16). + +## Decision + +Add rigid-body dynamics as **purely additive extensions**, so the 2D +elliptical-drop path and its on-disk kernel cache stay byte-identical (verified +by `test_2d_path_generated_source_is_byte_identical_to_golden`, which still +passes): + +1. **Force/torque SUM-reduction on the device** (`_rigid_moments_reduce_f32`/ + `_rigid_moments_reduce_f64`): per body, `atomic_add` the 16-slot `mi` vector + of `RigidBodyMoments.reduce` (total mass; `m*x/y/z` for the COM; six + second-moments/products of inertia about the ORIGIN; total force; torque + about the origin). **Accumulate in f64 on both paths** -- the f32 kernel + casts f32 particle data to f64 before accumulating. Standalone launch + kernels modeled on the existing `_wcsph_dt_reduce_*` `atomic_max` template; + they touch no generated source and no kernel-id router. +2. **Host (numpy) 6-DOF finalize** (`_rigid_finalize_moments`): COM, + parallel-axis inertia tensor about the COM, total force, COM acceleration, + torque about the COM, and `omega_dot = inv(I)(tau - omega x (I omega))`, + reusing PySPH's exact formulas (`rigid_body.py:128-207`). At one (few) body + the device->host round-trip is free (the backend already syncs every launch + and copies the dt scalar to host each step), and it reuses PySPH's verified + math rather than reimplementing an inertia-tensor inverse / quaternion update + in a kernel. +3. **Device rigid-transform** of body particle positions/velocities from the + integrated COM state (`v = vc + omega x r`, `x += ...`), validated exact in + P0; to be wired in P2. +4. **A new sibling step driver** `wc_sph_dam_break_rigid_step` (P3): the body is + **excluded from the shared PEC launch entirely** and advanced by the rigid + transform instead. `wc_sph_dam_break_step` (the fixed-wall path) is **never + edited**. +5. **Fluid<->body coupling as its own additive `WarpEquation` group** (P3): the + *real* reference coupling (`LiuFluidForce` -- a standard symmetric momentum + term with a clean equal-and-opposite body reaction), with a `NumberDensity` + volume pre-pass for the density path. Not a "wall pressure-gradient mirror": + validating against PySPH-Liu requires implementing Liu, and the equilibrium + draft alone is coupling-blind. + +### Amendment — 2026-06-20: production 6-DOF stays on the GPU + +The original P1/P2 split selected a host NumPy finalize and host integration +because one body's 3x3 solve is tiny and easy to validate. The implementation +owner challenged the architectural cost: even a tiny host solve creates a +synchronization/copy boundary, prevents a fully device-resident step and future +CUDA-graph capture, and scales poorly with body count. The direction change was +approved explicitly before the P2 plan. + +For the production path, decision items 2 and 3 are therefore amended: + +- the f64 16-slot reduction remains on-device and feeds a one-thread-per-body + Warp finalize kernel; +- mass, COM, inertia, force, acceleration, torque, angular acceleration, COM + velocity, angular velocity, and saved RK2 state live in persistent f64 device + arrays; +- the symmetric 3x3 solve for + `omega_dot = inv(I)(tau - omega x (I omega))`, RK2 midpoint/full body-state + updates, `v = vc + omega x r`, and particle position updates run in Warp; +- the production RK2 stage performs no `.numpy()`, ParticleArray pull, or + explicit device synchronization; host transfer is reserved for checkpoints + and validation; +- `_rigid_finalize_moments()` remains as the trusted NumPy oracle and explicit + host-result helper, not the stepping implementation. + +The additive/cache-stability, f64 reduction, Liu coupling, and sibling-driver +parts of this ADR are unchanged. + +## Rationale + +- The only new device primitive is a hand-written `atomic_add` reduction with + its own structural cache key; everything else is host numpy or an additive + equation group / driver. Nothing edits an existing generated block's source, + so the cache-stability contract holds (golden md5 test green). +- f64 accumulators are the cheap fix for the fp32 `atomic_add` non-associativity + the adversarial review flagged: ~6 orders tighter run-to-run, negligible cost + at body-particle counts. +- The standalone rigid kernels keep the project's "byte-identical 2D path" + mental model intact; the NumPy implementation remains the parity oracle for + the device 6-DOF math. + +## Alternatives considered + +- **Integrate 6-DOF on the device** (inertia-tensor inverse / quaternion in a + kernel). Rejected for now: at one body it buys nothing, adds fp32-atomic and + reimplementation risk, and complicates parity. Revisit only if many bodies. +- **f32 accumulators.** Rejected: order-dependent, ~3e-6 non-deterministic + (P0) -- breaks reproducibility for negligible saving. +- **Reuse the fused fluid momentum block with the body as destination** for the + coupling. Risk: the fused block writes continuity (`arho`) *and* momentum + together, so body density would be double-counted (also fed by the wall-style + continuity pass) -> wrong pressure -> wrong force. To be checked at P3; if + present, use a momentum-only generated *variant* (additive, new structural + cache key) rather than editing the fused block. +- **Mirror the wall pressure-gradient path as the body coupling.** Rejected: the + reference uses Liu; validating a different force model against PySPH-Liu is + uninterpretable, and equilibrium draft cannot catch the error. + +## Consequences + +- New standalone kernels (`_rigid_moments_reduce_f32/f64`, device finalize, + RK2 state, and rigid motion) plus persistent compact device state in + `warp_sph.py`; later a coupling group and sibling driver. All additive; the + single-array and fixed-wall paths are untouched. +- The 2D elliptical-drop generated source stays byte-identical (test-asserted); + the new kernels are not in `_WARP_DEVICE_FUNCS` and not seeded into generated + kernels, so the existing kernels' disk-cache hashes are unchanged. +- Validation is against a numpy `RigidBodyMoments`/`Motion` reimplementation + (tier-1); tier-2 vs the real CPU Application needs a compyle py3.14 fix. +- fp32 rigid reduction is ~deterministic (f64 round-off) but not bit-identical, + a small, documented relaxation of the "byte-identical" mental model for the + rigid path only. + +## Follow-ups + +- P2: device-resident 6-DOF finalize/integrate + device transform wired + together. (Completed 2026-06-20; see validation below.) +- P3: Liu coupling group + `NumberDensity` pre-pass + `wc_sph_dam_break_rigid_step`; + resolve the `arho` double-count; bolt-on `RigidBodyWallCollision` if the case + needs it. +- P4: EPEC fidelity (rigid update placement across both half-stages) + a + behaviour-stability guard for the existing driver. +- Assemble the 3D surge-tosses-a-box case; render via splashsurf -> Blender. +- Optional: compyle upgrade for tier-2 CPU parity. + +## Validation (2026-06-19) + +- **P0 kill-test** (`p0_rigid_reduce_kill_test.py`, RTX 4060): `atomic_add` + reduction reproduces `RigidBodyMoments` (2.7e-15 given f32 inputs; 8e-14 for + f64 data), host finalize matches numpy to 6e-14, device transform exact + (5.5e-17); fp32 accum spread 3.0e-6 vs f64 accum 1.4e-15 -> **decision: f64 + accumulators**. +- **P1 (this change):** `_rigid_moments_reduce_f32/f64` + `_rigid_finalize_moments` + + `compute_rigid_body_moments` in `warp_sph.py`, with tests + `test_rigid_body_moments_matches_reference_3d[False/True]` (two-body, + asymmetric; f32 and f64 paths match the numpy reference) and + `test_rigid_moments_f32_kernel_is_accurate_and_deterministic`. The golden + 2D-source guard still passes (cache-stability preserved). +- **P2 (2026-06-20 amendment):** persistent `WarpRigidBodyState`, device + finalize/symmetric-3x3 solve, device RK2 midpoint/full updates, and device + `RigidBodyMotion` equivalent. fp32/fp64 device results match the NumPy/PySPH + formulas; a guard monkeypatches host finalize, ParticleArray pull, and + `wp.synchronize_device` to fail if either production stage crosses the host + boundary. Focused rigid + 2D cache guard: `10 passed`; final full Warp SPH + regression: `49 passed`. +- **P3 (2026-06-20):** additive `RigidNumberDensity` plus deterministic + two-pass `LiuFluidAcceleration`/`LiuBodyReaction`, rigid density/body-force + staging, and sibling `wc_sph_dam_break_rigid_step`. The rigid body is excluded + from the pre-existing fused fluid block and contributes to fluid/body + continuity exactly once in each direction. fp32/fp64 Liu results match a + direct NumPy/Wendland reference with equal total reaction; the final suite is + `54 passed`. A 7,458-particle, 241-step first-plunge transient remained finite, + moved and rotated from computed fluid force, and preserved relative body + geometry to `1.90e-6`. Review: + `reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md`. diff --git a/.ai/implementations/blast-from-the-past/decisions/2026-07-07_adr-0007_device-built-multilevel-cell-list-nnps-for-adaptive-resolution.md b/.ai/implementations/blast-from-the-past/decisions/2026-07-07_adr-0007_device-built-multilevel-cell-list-nnps-for-adaptive-resolution.md new file mode 100644 index 000000000..435c21155 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/2026-07-07_adr-0007_device-built-multilevel-cell-list-nnps-for-adaptive-resolution.md @@ -0,0 +1,160 @@ +--- +type: decision +id: ADR-0007 +date: 2026-07-07 +author: @kunalpuri-prediqt +scope: gpu-nnps +status: Proposed +supersedes: [] +relates_to: [ADR-0003, ADR-0004] +depends_on: [ADR-0004] +conflicts_with: [] +--- + +# ADR-0007: device-built multilevel cell-list NNPS for adaptive resolution + +## Context + +`UniformGridWarpNNPS` sizes one cell (`cell_size = radius_scale * global_hmax`) +for the whole domain. That is correct for variable `h` but not scalable for +adaptive particle resolution (APR): a small number of coarse particles makes +cells coarse everywhere, so a dense fine region generates many candidate checks +per destination. The plan +`2026-07-06_warp-multilevel-gpu-nnps` set the checkpoint gate: an exact, +device-built multilevel cell-list NNPS whose accepted neighbor set matches +brute force exactly while candidate work drops materially on localized +refinement, and whose construction stays on the GPU. This ADR records the +representation and contracts chosen once the exact-set and candidate-scaling +kill tests passed; it is narrowed (per the plan) to the NNPS only. Runtime +split/merge, particle pools, target-level assignment, and periodic multilevel +domains are explicitly out of scope and receive later ADRs. + +The P0 Vacondio/PySPH daughter-stencil convention mismatch is unresolved, so +the level ratios here are provisional configuration, not a production default. + +## Decision + +Add `MultilevelGridWarpNNPS(UniformGridWarpNNPS)` as an additive prototype +alongside the uniform grid, with these choices: + +- **Level representation.** Discrete smoothing-length levels via `h_ref`, + `level_ratio`, `nlevels`. Level `k` covers the half-open range + `[h_ref*level_ratio**k, h_ref*level_ratio**(k+1))` with level 0 the finest; + the overall top edge is inclusive. Each source particle belongs to exactly + one level (so no pair is visited twice). Level edges are computed in the + device float precision so an `h` sitting on an edge in fp32 bins like the + edge rather than tripping the range guard by one ULP. `h` outside + `[edges[0], edges[nlevels]]` fails loudly; particles are never silently + clipped. Each populated level's conservative support bound is + `radius_scale * max(h in level)`. + +- **Flattened per-level grids.** Each populated level gets its own padded + origin (min-coordinate minus one cell), cell size (its support bound), and + `(nx,ny,nz)`. All levels are flattened into one global cell space via a + per-level `cell_offset`. The grid is built with the existing + count -> exclusive-scan -> scatter kernels over that global cell space. Empty + levels allocate no cells (`nx=0`) and are skipped. + +- **Device-built construction / permitted readback.** Level assignment and the + per-level count, max-`h`, and AABB (min/max per axis) reductions run in one + GPU kernel (`_ml_assign_reduce`). Only `O(nlevels * narrays)` scalar metadata + is read back to the host to size the dense level grids; per-particle + `x/y/z/h` never leave the device on a warm `update(push=False)` or during + traversal. Per-particle levels stay on the device; the host-facing + `level_grid_info` diagnostic reads them back lazily and is not on the query + path. + +- **Cross-level traversal.** For each destination and each level, the query + radius is `max(radius_scale*h_i, level_support[k])`. That radius is converted + to the necessary cell-index range for that level (a variable range, not a + fixed 3x3x3 stencil, with a +/-1 guard band), then the exact pairwise + symmetric cutoff `rij^2 < (radius_scale*h_i)^2 OR rij^2 < (radius_scale*h_j)^2` + is applied before a pair is accepted. The length and fill passes are + structurally identical so their counts can never diverge. + +- **Module isolation.** The multilevel `@wp.kernel`s and the class live in their + own module `pysph/base/warp_multilevel_nnps.py`, not in `warp_nnps.py`. Warp + compiles/loads an entire Python module's kernels together on first launch, so + co-locating them with the uniform-grid kernels would JIT them onto the device + for every WCSPH consumer of `UniformGridWarpNNPS` and inflate the process + module footprint. Isolation loads them only when the multilevel NNPS is used. + +Provisional configuration: initial tests use ratios 2 and 1.2 and up to four +levels spanning `h_max/h_min = 16`. No production ratio or split weight is +selected here. + +## Rationale + +- Per-level cell sizing removes the artificial candidate work that a single + global-`hmax` grid imposes on dense fine regions, while the exact symmetric + cutoff inside the traversal reproduces the brute-force accepted set, so + correctness is preserved and testable against the (independently verified) + numpy oracle and `BruteForceWarpNNPS`. +- Exactly-one-level membership plus non-overlapping per-level cell ranges give + the no-duplicate-visit guarantee for free. +- On-device assignment + reductions with only `O(nlevels)` readback keeps the + structure device-resident as APR requires, and is measurable/enforced by a + residency test that fails if `x/y/z/h` are pulled to the host. +- Housing the kernels in a separate module keeps the WCSPH hot path's device + footprint unchanged, which matters because the in-process PTX-JIT on the + WSL2 dev box is already near its module-accumulation ceiling. + +## Alternatives considered + +- **Keep the global-`hmax` uniform grid.** Correct but not scalable for APR: + the candidate-scaling kill test exists precisely because coarse particles + over-coarsen the whole grid. +- **Sparse hash / sort-based level storage** instead of dense per-level grids. + Retained as the documented fallback (see decision gate below): if dense + per-level grids consume more memory than the saved particle state, or exact + traversal needs unbounded work, on representative localized-refinement + distributions, switch to sparse storage before this ADR is Accepted. +- **Multilevel kernels inside `warp_nnps`.** Rejected: it hangs the combined + single-process WCSPH suite on the WSL2 PTX-JIT (module-footprint + accumulation); the separate-module form does not. +- **Host-side level assignment / AABB** (read `x/y/z/h` back, bin on the host). + Simpler, and was used as a transitional first cut, but violates the + device-residency gate; replaced by the on-device assign+reduce kernel. + +## Consequences + +- New module `pysph/base/warp_multilevel_nnps.py`: the assign+reduce kernel, + the flattened cell-id / variable-stencil length+fill traversal kernels + (fp32+fp64), `MultilevelGridWarpNNPS`, a device neighbor-cache oracle + (`build_neighbor_cache_gpu`), and host diagnostics (`level_grid_info`, + `candidate_pairs`). Pure-Python helpers (`assign_particle_levels`, + `brute_force_neighbor_sets`, `accepted_level_pair_counts`) live in + `warp_nnps.py` (no GPU code). +- Kill-gate fixtures (all passing) in `test_warp_nnps.py`: single-level parity, + four-level `h_max/h_min=16` cross-level parity + level-pair matrix, fp32 + per-level grid-boundary padding, empty interior levels, gradual ratio 1.2, + cross-array traversal + per-source ownership, particles at spatial bounds, + device residency (no coordinate host readback), and clustered refinement + (accepted sets identical to the oracle and the uniform grid; candidate work + `>= 4x` lower -- measured ~9x, 197k vs 1.77M candidate pairs on the fixture). +- Cross-scale work that is real is not removed: a coarse destination querying a + dense fine level legitimately spans many fine cells. The hierarchy removes + artificial fine-fine and empty-cell work, not real cross-scale neighbors. +- The permitted `O(nlevels*narrays)` metadata readback is a per-`update()` host + synchronization; it is a prototype allowance, NOT the production + device-residency contract. The eventual production APR path is expected to + eliminate it (e.g. a persistent max-levels allocation) and this constraint + must not be silently inherited. +- Periodic multilevel traversal is out of scope; it must raise a clear error + rather than silently use the non-periodic walk. (The generated multilevel + equation loop -- plan step 3 -- will carry that guard.) + +## Follow-ups + +- Plan step 3: `neighbor_mode='multilevel'` in `warp_codegen.py` and routing of + `_run_equation_group` / `compute_wcsph_adaptive_timestep` so real SPH equation + groups and the adaptive-timestep reduction consume the multilevel structure + directly, with fp32/fp64 output parity against the flat/uniform path. +- Reject periodic multilevel domains explicitly with an error in step 3. +- Decision gate before this ADR moves to Accepted: confirm on representative + localized-refinement cases that dense per-level grids do not consume more + memory than the saved particle state; otherwise adopt sparse hash/sort + storage. +- Eliminate the per-`update()` metadata readback for the production path. +- Select production level ratios / split policy only after the P0 stencil + convention mismatch is resolved (later ADRs). diff --git a/.ai/implementations/blast-from-the-past/decisions/graph.md b/.ai/implementations/blast-from-the-past/decisions/graph.md new file mode 100644 index 000000000..686d16b43 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/graph.md @@ -0,0 +1,47 @@ +# Decision Graph + +Generated from ADR frontmatter. Do not hand-edit. + +```mermaid +flowchart TD + subgraph global[global] + ADR_0001["ADR-0001
Accepted"] + end + subgraph gpu_nnps[gpu-nnps] + ADR_0004["ADR-0004
Accepted"] + ADR_0007["ADR-0007
Proposed"] + end + subgraph particle_memory[particle-memory] + ADR_0002["ADR-0002
Accepted"] + end + subgraph warp_backend[warp-backend] + ADR_0003["ADR-0003
Accepted"] + ADR_0005["ADR-0005
Accepted"] + ADR_0006["ADR-0006
Accepted"] + end + ADR_0002 -. relates_to .-> ADR_0001 + ADR_0003 -. relates_to .-> ADR_0002 + ADR_0004 -- depends_on --> ADR_0003 + ADR_0004 -. relates_to .-> ADR_0003 + ADR_0005 -- depends_on --> ADR_0003 + ADR_0005 -. relates_to .-> ADR_0003 + ADR_0005 -. relates_to .-> ADR_0004 + ADR_0006 -- depends_on --> ADR_0005 + ADR_0006 -. relates_to .-> ADR_0003 + ADR_0006 -. relates_to .-> ADR_0004 + ADR_0006 -. relates_to .-> ADR_0005 + ADR_0007 -- depends_on --> ADR_0004 + ADR_0007 -. relates_to .-> ADR_0003 + ADR_0007 -. relates_to .-> ADR_0004 + classDef Accepted fill:#d5f5d5,stroke:#2c7a2c; + classDef Proposed fill:#fff3bf,stroke:#9a7500; + classDef Superseded fill:#e5e7eb,stroke:#6b7280; + classDef Rejected fill:#ffd6d6,stroke:#b91c1c; + class ADR_0001 Accepted; + class ADR_0002 Accepted; + class ADR_0003 Accepted; + class ADR_0004 Accepted; + class ADR_0005 Accepted; + class ADR_0006 Accepted; + class ADR_0007 Proposed; +``` diff --git a/.ai/implementations/blast-from-the-past/decisions/index.json b/.ai/implementations/blast-from-the-past/decisions/index.json new file mode 100644 index 000000000..d51127203 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/decisions/index.json @@ -0,0 +1,125 @@ +{ + "edges": [ + { + "from": "ADR-0002", + "to": "ADR-0001", + "type": "relates_to" + }, + { + "from": "ADR-0003", + "to": "ADR-0002", + "type": "relates_to" + }, + { + "from": "ADR-0004", + "to": "ADR-0003", + "type": "relates_to" + }, + { + "from": "ADR-0004", + "to": "ADR-0003", + "type": "depends_on" + }, + { + "from": "ADR-0005", + "to": "ADR-0003", + "type": "relates_to" + }, + { + "from": "ADR-0005", + "to": "ADR-0004", + "type": "relates_to" + }, + { + "from": "ADR-0005", + "to": "ADR-0003", + "type": "depends_on" + }, + { + "from": "ADR-0006", + "to": "ADR-0003", + "type": "relates_to" + }, + { + "from": "ADR-0006", + "to": "ADR-0004", + "type": "relates_to" + }, + { + "from": "ADR-0006", + "to": "ADR-0005", + "type": "relates_to" + }, + { + "from": "ADR-0006", + "to": "ADR-0005", + "type": "depends_on" + }, + { + "from": "ADR-0007", + "to": "ADR-0003", + "type": "relates_to" + }, + { + "from": "ADR-0007", + "to": "ADR-0004", + "type": "relates_to" + }, + { + "from": "ADR-0007", + "to": "ADR-0004", + "type": "depends_on" + } + ], + "nodes": [ + { + "date": "2026-06-15", + "file": "decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md", + "id": "ADR-0001", + "scope": "global", + "status": "Accepted" + }, + { + "date": "2026-06-15", + "file": "decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md", + "id": "ADR-0002", + "scope": "particle-memory", + "status": "Accepted" + }, + { + "date": "2026-06-17", + "file": "decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md", + "id": "ADR-0003", + "scope": "warp-backend", + "status": "Accepted" + }, + { + "date": "2026-06-18", + "file": "decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md", + "id": "ADR-0004", + "scope": "gpu-nnps", + "status": "Accepted" + }, + { + "date": "2026-06-18", + "file": "decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md", + "id": "ADR-0005", + "scope": "warp-backend", + "status": "Accepted" + }, + { + "date": "2026-06-19", + "file": "decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md", + "id": "ADR-0006", + "scope": "warp-backend", + "status": "Accepted" + }, + { + "date": "2026-07-07", + "file": "decisions/2026-07-07_adr-0007_device-built-multilevel-cell-list-nnps-for-adaptive-resolution.md", + "id": "ADR-0007", + "scope": "gpu-nnps", + "status": "Proposed" + } + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/benchmark_particle_mutations.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/benchmark_particle_mutations.py new file mode 100755 index 000000000..1c5f7b3d4 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/benchmark_particle_mutations.py @@ -0,0 +1,182 @@ +"""Benchmark ParticleArray mutation and sync operations for CPU vs Warp. + +This script is intentionally lightweight: it measures the API surface used by +the current prototype rather than trying to be a general benchmark harness. +""" + +from __future__ import annotations + +import argparse +import statistics +import time +from dataclasses import dataclass +from typing import Callable + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +from pysph.base.utils import get_particle_array + + +@dataclass(frozen=True) +class Case: + backend: str | None + operation: str + particles: int + repeats: int + p50_ms: float + + +def _make_array(n: int, backend: str | None): + tag = np.zeros(n, dtype=np.int64) + tag[n // 2::5] = 1 + tag[n // 3::7] = 2 + kwargs = dict( + name=f"pa_{backend or 'cpu'}", + x=np.linspace(0.0, 1.0, n), + y=np.linspace(1.0, 2.0, n), + z=np.linspace(2.0, 3.0, n), + h=np.ones(n) * 0.1, + m=np.ones(n), + tag=tag, + ) + if backend is None: + return get_particle_array(**kwargs) + return get_particle_array(backend=backend, **kwargs) + + +def _sync(backend: str | None): + if backend == "warp": + import warp as wp + + wp.synchronize() + + +def _time_case( + backend: str | None, + operation: str, + particles: int, + repeats: int, + fn: Callable[[int, str | None], None], +) -> Case: + samples = [] + for _ in range(repeats): + _sync(backend) + start = time.perf_counter() + fn(particles, backend) + _sync(backend) + samples.append((time.perf_counter() - start) * 1000.0) + return Case( + backend=backend or "cpu", + operation=operation, + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + ) + + +def _case_add_particles(n: int, backend: str | None) -> None: + pa = _make_array(n, backend) + count = max(1, n // 10) + pa.add_particles( + x=np.linspace(4.0, 5.0, count), + y=np.linspace(5.0, 6.0, count), + z=np.linspace(6.0, 7.0, count), + tag=np.zeros(count, dtype=np.int64), + align=True, + ) + expected = n + count + actual = pa.get_number_of_particles() + if actual != expected: + raise AssertionError(f"add_particles expected {expected}, got {actual}") + + +def _case_remove_particles(n: int, backend: str | None) -> None: + pa = _make_array(n, backend) + remove = np.arange(1, n, 10, dtype=np.int64) + pa.remove_particles(remove, align=True) + expected = n - len(remove) + actual = pa.get_number_of_particles() + if actual != expected: + raise AssertionError(f"remove_particles expected {expected}, got {actual}") + + +def _case_extract_particles(n: int, backend: str | None) -> None: + pa = _make_array(n, backend) + indices = np.arange(0, n, 11, dtype=np.int64) + extracted = pa.extract_particles(indices, align=True) + expected = len(indices) + actual = extracted.get_number_of_particles() + if actual != expected: + raise AssertionError(f"extract_particles expected {expected}, got {actual}") + + +def _case_align_particles(n: int, backend: str | None) -> None: + pa = _make_array(n, backend) + pa.align_particles() + pa.gpu.pull("tag") if backend == "warp" else None + if pa.get_number_of_particles() != n: + raise AssertionError("align_particles changed particle count") + + +def _case_pull_after_device_write(n: int, backend: str | None) -> None: + pa = _make_array(n, backend) + if backend != "warp": + pa.x[:] = pa.x[:] + 2.0 + return + pa.gpu.x.fill(3.5) + pa.gpu.pull("x") + if not np.allclose(pa.x[:], 3.5): + raise AssertionError("device write did not pull back to host") + + +def _print_table(results: list[Case]) -> None: + print("backend operation particles repeats p50_ms") + for case in results: + print( + f"{case.backend:7s} " + f"{case.operation:24s} " + f"{case.particles:9d} " + f"{case.repeats:7d} " + f"{case.p50_ms:8.3f}" + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--sizes", type=int, nargs="+", default=[10_000, 100_000]) + parser.add_argument("--repeats", type=int, default=5) + args = parser.parse_args() + + operations: list[tuple[str, Callable[[int, str | None], None]]] = [ + ("add_particles", _case_add_particles), + ("remove_particles", _case_remove_particles), + ("extract_particles", _case_extract_particles), + ("align_particles", _case_align_particles), + ("pull_after_device_write", _case_pull_after_device_write), + ] + + results: list[Case] = [] + for particles in args.sizes: + for name, fn in operations: + for backend in (None, "warp"): + results.append( + _time_case( + backend=backend, + operation=name, + particles=particles, + repeats=args.repeats, + fn=fn, + ) + ) + + _print_table(results) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/experiment.md new file mode 100644 index 000000000..72b474035 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/experiment.md @@ -0,0 +1,184 @@ +--- +type: experiment +id: 2026-06-15_initial-warp-benchmark-placeholder +created: 2026-06-15T07:19:08 CET +author: @kunalpuri-prediqt +aspect: validation-benchmarks +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp ParticleArray Mutation And Sync Baseline + +## Purpose + +Establish the first concrete correctness and timing baseline for the +NVIDIA-Warp-backed ParticleArray device mirror. + +This experiment covers the ParticleArray operations that are most likely to +break when the backing storage moves from host arrays to device arrays: + +- construction with `backend="warp"` +- full and selective host/device push/pull +- add particles +- remove particles +- remove tagged particles +- append particle arrays +- extract particles into a new array +- align local/remote/ghost particles +- preserve scalar and strided properties +- preserve constants and default values + +## Setup + +Run from the repository root on `prediqt-02`: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh +``` + +The scripts expect the PQT environment to be available through: + +```bash +source "$HOME/prediqt/activate" +``` + +The current PQT environment has PySPH installed editable from this checkout, +Warp `1.14.0`, and standalone Zoltan installed under: + +```text +/home/kunalp/prediqt/zoltan +``` + +## Hypothesis + +Correctness should match the existing CPU ParticleArray semantics for all +covered mutation and synchronization operations. + +Performance expectations are intentionally split: + +- device-resident read/write operations should become the target for speedups; +- growth and deletion operations may not beat CPU yet, because the first + implementation still uses host round-trips for some structural mutations; +- this baseline should expose the cost of those round-trips and guide the next + Warp-kernel migration. + +## Execution + +### Correctness + +`run_correctness.sh` runs: + +- `pysph/base/tests/test_warp_device_helper.py` +- a focused CPU ParticleArray sanity slice for constructor, alignment, + add-property, constants, remove, add, and extract behavior + +Expected result: + +```text +20 passed +7 passed +``` + +Warnings from Warp's Python 3.14 ctypes usage are acceptable for this baseline. + +### Mutation Timing + +`run_mutation_benchmark.sh` runs `benchmark_particle_mutations.py`, which times: + +- add particles +- remove particles +- extract particles +- align particles +- full device-to-host pull after a device write + +The benchmark records CPU and Warp timings for multiple particle counts. The +primary output is a readable table on stdout; redirect it when capturing a run: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh \ + | tee .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/results-$(date +%Y%m%d-%H%M%S).txt +``` + +## Success Criteria + +This experiment succeeds when: + +- all focused Warp correctness tests pass; +- the CPU sanity slice still passes, proving the Warp path did not regress + existing host semantics; +- every benchmark case returns correct particle counts and values; +- timings are captured with particle count, operation name, backend, repeat + count, and p50 wall time; +- any operation that is slower on Warp is classified as either expected + structural-mutation overhead or a follow-up optimization target. + +This experiment does not yet require Warp mutation operations to be faster than +CPU. That threshold belongs to the next experiment after add/remove/extract are +moved away from host-side concatenation/readback and into device-side kernels. + +## Results + +Current known focused checks: + +```text +python -m pytest -q pysph/base/tests/test_warp_device_helper.py +20 passed + +CPU ParticleArray sanity slice +7 passed +``` + +Current smoke benchmark: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh --sizes 1000 --repeats 2 +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Expected/current smoke result: + +```text +backend operation particles repeats p50_ms +cpu add_particles 1000 2 0.386 +warp add_particles 1000 2 75.121 +cpu remove_particles 1000 2 0.461 +warp remove_particles 1000 2 16.717 +cpu extract_particles 1000 2 0.386 +warp extract_particles 1000 2 19.079 +cpu align_particles 1000 2 0.393 +warp align_particles 1000 2 7.831 +cpu pull_after_device_write 1000 2 0.417 +warp pull_after_device_write 1000 2 1.560 +``` + +Interpretation: + +- Correctness checks passed inside the benchmark cases. +- Warp structural mutation timings are slower in this prototype because + add/remove/extract still use host-side rebuilds/readback. +- Device write/readback is already measured separately so later device-kernel + work has a comparison point. + +## Conclusion + +The first Warp ParticleArray implementation has correctness coverage for +particle add/delete-style operations. The missing piece was experiment +documentation and runnable measurement scripts, not test coverage. + +## Follow-ups + +- Run larger benchmark sizes on `prediqt-02`. +- Add a second experiment for device-side structural mutation kernels. +- Add a third experiment for NNPS-facing access patterns once the integration + boundary moves from ParticleArray mirroring into neighbor search. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh new file mode 100755 index 000000000..0c054fcf5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_device_helper.py + +python - <<'PY' +import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +import pytest + +raise SystemExit(pytest.main([ + "-q", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_constructor", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_align_particles", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_add_property", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_that_constants_can_be_added", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_remove_particles", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_add_particles", + "pysph/base/tests/test_particle_array.py::ParticleArrayTestCPU::test_extract_particles_works_without_specific_props_without_dest", +])) +PY diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh new file mode 100755 index 000000000..7a6d667d0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/benchmark_particle_mutations.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/benchmark_eos_continuity.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/benchmark_eos_continuity.py new file mode 100644 index 000000000..207196fdb --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/benchmark_eos_continuity.py @@ -0,0 +1,212 @@ +"""Benchmark PySPH CPU/Cython and Warp EOS plus continuity kernels.""" + +from __future__ import annotations + +import argparse +import gc +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import dataclass + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +import warp as wp + +from pysph.base.kernels import CubicSpline +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import compute_continuity, compute_isothermal_eos +from pysph.sph.basic_equations import ContinuityEquation, IsothermalEOS +from pysph.tools.sph_evaluator import SPHEvaluator + + +@dataclass(frozen=True) +class Result: + backend: str + particles: int + repeats: int + p50_ms: float + p_checksum: float + arho_checksum: float + status: str = "ok" + + +def _run_text(cmd: list[str]) -> str: + try: + return subprocess.check_output( + cmd, stderr=subprocess.DEVNULL, text=True + ).strip() + except Exception: + return "unknown" + + +def _cpu_model() -> str: + text = _run_text(["lscpu"]) + for line in text.splitlines(): + if line.startswith("Model name:"): + return line.split(":", 1)[1].strip() + return platform.processor() or "unknown" + + +def _gpu_model() -> str: + text = _run_text([ + "nvidia-smi", + "--query-gpu=name,driver_version,memory.total", + "--format=csv,noheader", + ]) + return text.splitlines()[0] if text else "unknown" + + +def _make_particles(n: int, backend: str | None = None): + rng = np.random.default_rng(12345) + x = rng.random(n) + y = rng.random(n) + z = np.zeros(n) + dx = np.sqrt(1.0 / n) + h = np.ones(n) * 1.4 * dx + m = np.ones(n) * dx * dx + rho = 1000.0 + 10.0 * rng.random(n) + u = rng.random(n) - 0.5 + v = rng.random(n) - 0.5 + w = np.zeros(n) + kwargs = dict( + name='fluid', x=x, y=y, z=z, h=h, m=m, rho=rho, p=np.zeros(n), + arho=np.zeros(n), u=u, v=v, w=w + ) + if backend is None: + return [get_particle_array(**kwargs)] + return [get_particle_array(backend=backend, **kwargs)] + + +def _time_cpu(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles) + evaluator = SPHEvaluator( + arrays=arrays, + equations=[ + IsothermalEOS( + dest='fluid', sources=None, rho0=1000.0, c0=20.0, p0=5.0 + ), + ContinuityEquation(dest='fluid', sources=['fluid']), + ], + dim=2, + kernel=CubicSpline(dim=2), + backend='cython', + nnps_factory=LinkedListNNPS, + ) + samples = [] + p_checksum = 0.0 + arho_checksum = 0.0 + for _ in range(repeats): + arrays[0].p[:] = 0.0 + arrays[0].arho[:] = 0.0 + start = time.perf_counter() + evaluator.evaluate(0.0, 0.1) + samples.append((time.perf_counter() - start) * 1000.0) + p_checksum = float(np.sum(arrays[0].p)) + arho_checksum = float(np.sum(arrays[0].arho)) + return Result( + backend='cpu_cython', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + p_checksum=p_checksum, + arho_checksum=arho_checksum, + ) + + +def _time_warp(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles, backend='warp') + pa = arrays[0] + nnps = UniformGridWarpNNPS(dim=2, particles=arrays, radius_scale=2.0) + samples = [] + p_checksum = 0.0 + arho_checksum = 0.0 + for _ in range(repeats): + start = time.perf_counter() + p = compute_isothermal_eos(pa, rho0=1000.0, c0=20.0, p0=5.0, + device=nnps.device) + arho = compute_continuity(nnps, 0, 0) + samples.append((time.perf_counter() - start) * 1000.0) + p_checksum = float(wp.utils.array_sum(p.dev)) + arho_checksum = float(wp.utils.array_sum(arho.dev)) + return Result( + backend='warp_grid_eos_cont', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + p_checksum=p_checksum, + arho_checksum=arho_checksum, + ) + + +def _safe_time(fn, particles: int, repeats: int, backend: str) -> Result: + try: + return fn(particles, repeats) + except Exception as exc: + return Result( + backend=backend, + particles=particles, + repeats=repeats, + p50_ms=float('nan'), + p_checksum=float('nan'), + arho_checksum=float('nan'), + status=type(exc).__name__ + ": " + str(exc).splitlines()[0], + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument( + '--sizes', type=int, nargs='+', + default=[1000000, 2000000, 5000000], + ) + parser.add_argument('--repeats', type=int, default=1) + args = parser.parse_args() + + wp.init() + print(f"# host: {platform.node()}", flush=True) + print(f"# python: {sys.executable}", flush=True) + print(f"# cpu: {_cpu_model()}", flush=True) + print(f"# gpu: {_gpu_model()}", flush=True) + print(f"# warp: {wp.__version__}", flush=True) + print( + "backend particles repeats p50_ms p_checksum arho_checksum " + "speedup_vs_cpu status", + flush=True, + ) + for particles in args.sizes: + cpu = _safe_time(_time_cpu, particles, args.repeats, 'cpu_cython') + warp = _safe_time( + _time_warp, particles, args.repeats, 'warp_grid_eos_cont' + ) + for result in (cpu, warp): + if cpu.status != 'ok' or result.status != 'ok': + speedup = float('nan') + else: + speedup = cpu.p50_ms / result.p50_ms + print( + f"{result.backend:18s} " + f"{result.particles:9d} " + f"{result.repeats:7d} " + f"{result.p50_ms:8.3f} " + f"{result.p_checksum:14.6e} " + f"{result.arho_checksum:14.6e} " + f"{speedup:14.3f} " + f"{result.status}", + flush=True, + ) + gc.collect() + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/experiment.md new file mode 100644 index 000000000..b7349c906 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/experiment.md @@ -0,0 +1,147 @@ +--- +type: experiment +id: 2026-06-15_warp-eos-continuity +created: 2026-06-15T13:05:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp EOS And Continuity + +## Headline + +At 5,000,000 particles on PrediQT-02, `warp_grid_eos_cont` computes +IsothermalEOS plus ContinuityEquation in `1.799 s` versus PySPH CPU/Cython +`130.549 s`, a `72.583x` speedup, on Intel(R) Core(TM) Ultra 7 155H CPU and +NVIDIA GeForce RTX 4060 Laptop GPU. + +## Purpose + +Add the next two simple SPH equation kernels after summation density: + +- `IsothermalEOS`: `p = p0 + c0^2*(rho - rho0)`; +- `ContinuityEquation`: `arho_i = sum_j m_j * VIJ . DWIJ`. + +This experiment measures the paired operation against PySPH's CPU/Cython +execution path and intentionally caps the sweep at 5M particles. + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh --sizes 1000000 2000000 5000000 --repeats 1 +``` + +The wrappers use the active venv if one is already active; otherwise they +source: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +EOS should be memory-bandwidth friendly and continuity should behave similarly +to the summation-density benchmark while adding velocity and gradient work. The +Warp pair should match CPU/Cython checksums to useful aggregate precision and +provide large operation speedups at million-particle scales. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +``` + +`run_benchmark.sh` runs `benchmark_eos_continuity.py`, which records: + +- CPU model; +- GPU model, driver, and memory; +- Warp version; +- backend; +- particle count; +- repeat count; +- p50 operation time; +- pressure checksum; +- density-rate checksum; +- speedup relative to `cpu_cython`. + +CPU baseline: + +- PySPH `SPHEvaluator`; +- Cython backend; +- `IsothermalEOS`; +- `ContinuityEquation`; +- `CubicSpline(dim=2)`; +- `LinkedListNNPS`. + +Warp baseline: + +- `compute_isothermal_eos()`; +- `compute_continuity()`; +- `UniformGridWarpNNPS` device neighbor cache; +- Warp CubicSpline gradient kernel. + +## Success Criteria + +This experiment succeeds when: + +- focused tests compare Warp EOS and continuity values against CPU references; +- CPU and Warp benchmark checksums are close enough to catch major correctness + issues; +- the benchmark completes 1M, 2M, and 5M particles; +- no EOS+continuity run exceeds 5M particles; +- timing output reports CPU-relative speedup and hardware. + +## Results + +Focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +22 passed +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Current capped sweep: + +```text +backend particles repeats p50_ms p_checksum arho_checksum speedup_vs_cpu status +cpu_cython 1000000 1 19783.291 2.006988e+09 -3.157208e+05 1.000 ok +warp_grid_eos_cont 1000000 1 122.830 2.006988e+09 -3.157289e+05 161.063 ok +cpu_cython 2000000 1 43621.644 4.012550e+09 4.032652e+05 1.000 ok +warp_grid_eos_cont 2000000 1 318.672 4.012550e+09 4.032475e+05 136.886 ok +cpu_cython 5000000 1 130549.367 1.002678e+10 6.015853e+05 1.000 ok +warp_grid_eos_cont 5000000 1 1798.625 1.002678e+10 6.015562e+05 72.583 ok +``` + +Interpretation: + +- Pressure checksums match to reported precision. +- Continuity aggregate checksums are close but not exact, which is expected for + different parallel accumulation order and floating-point execution paths. + Focused tests compare per-particle values for deterministic small fixtures. +- The 5M cap was honored. No 10M EOS+continuity run was performed. +- The Warp path now exercises both a pure per-particle equation and a + gradient-based neighbor-loop equation. + +## Conclusion + +EOS and continuity are now running on the Warp path with focused correctness +coverage and large speedups through 5M particles. The next useful step is a +pressure-gradient momentum equation, which would complete the minimal density, +pressure, and acceleration chain needed for a simple WCSPH step. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh new file mode 100755 index 000000000..fa7b247b5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/benchmark_eos_continuity.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_correctness.sh new file mode 100755 index 000000000..ba9ec96a3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/benchmark_warp_nnps.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/benchmark_warp_nnps.py new file mode 100755 index 000000000..92618d996 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/benchmark_warp_nnps.py @@ -0,0 +1,171 @@ +"""Benchmark CPU linked-list NNPS against the first Warp brute-force NNPS.""" + +from __future__ import annotations + +import argparse +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import dataclass + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +from cyarray.carray import UIntArray + +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import BruteForceWarpNNPS, UniformGridWarpNNPS + + +@dataclass(frozen=True) +class Result: + backend: str + particles: int + repeats: int + p50_ms: float + avg_neighbors: float + + +def _run_text(cmd: list[str]) -> str: + try: + return subprocess.check_output( + cmd, stderr=subprocess.DEVNULL, text=True + ).strip() + except Exception: + return "unknown" + + +def _cpu_model() -> str: + text = _run_text(["lscpu"]) + for line in text.splitlines(): + if line.startswith("Model name:"): + return line.split(":", 1)[1].strip() + return platform.processor() or "unknown" + + +def _gpu_model() -> str: + text = _run_text([ + "nvidia-smi", + "--query-gpu=name,driver_version,memory.total", + "--format=csv,noheader", + ]) + return text.splitlines()[0] if text else "unknown" + + +def _warp_version() -> str: + try: + import warp as wp + return wp.__version__ + except Exception: + return "unknown" + + +def _make_particles(n: int, backend: str | None = None): + rng = np.random.default_rng(12345) + x = rng.random(n) + y = rng.random(n) + z = np.zeros(n) + dx = np.sqrt(1.0 / n) + h = np.ones(n) * 1.4 * dx + kwargs = dict(name='fluid', x=x, y=y, z=z, h=h) + if backend is None: + return [get_particle_array(**kwargs)] + return [get_particle_array(backend=backend, **kwargs)] + + +def _query_all(nnps, n: int): + nbrs = UIntArray() + total = 0 + nnps.set_context(0, 0) + for d_idx in range(n): + nnps.get_nearest_particles(0, 0, d_idx, nbrs) + total += nbrs.length + return total / n + + +def _time_backend(backend: str, particles: int, repeats: int) -> Result: + samples = [] + avg_neighbors = 0.0 + for _ in range(repeats): + arrays = _make_particles( + particles, backend='warp' if backend.startswith('warp') else None + ) + if backend == 'warp': + nnps = BruteForceWarpNNPS(dim=2, particles=arrays, + radius_scale=2.0) + elif backend == 'warp_cached': + nnps = BruteForceWarpNNPS(dim=2, particles=arrays, + radius_scale=2.0, cache=True) + elif backend in ('warp_grid', 'warp_grid_device'): + nnps = UniformGridWarpNNPS(dim=2, particles=arrays, + radius_scale=2.0) + else: + nnps = LinkedListNNPS(dim=2, particles=arrays, radius_scale=2.0) + + start = time.perf_counter() + if backend == 'warp_grid_device': + cache = nnps.build_neighbor_cache_gpu(0, 0) + avg_neighbors = cache['total_neighbors'] / particles + else: + avg_neighbors = _query_all(nnps, particles) + samples.append((time.perf_counter() - start) * 1000.0) + return Result( + backend=backend, + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + avg_neighbors=avg_neighbors, + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument('--sizes', type=int, nargs='+', default=[256, 1024]) + parser.add_argument('--repeats', type=int, default=3) + parser.add_argument( + '--backends', nargs='+', + default=['cpu', 'warp', 'warp_cached', 'warp_grid'], + choices=[ + 'cpu', 'warp', 'warp_cached', 'warp_grid', 'warp_grid_device' + ], + help='Backends to run. For large sizes, use: cpu warp_grid_device.', + ) + args = parser.parse_args() + if 'cpu' not in args.backends: + raise ValueError("cpu must be included to compute speedup_vs_cpu") + + print(f"# host: {platform.node()}") + print(f"# python: {sys.executable}") + print(f"# cpu: {_cpu_model()}") + print(f"# gpu: {_gpu_model()}") + print(f"# warp: {_warp_version()}") + print("backend particles repeats p50_ms avg_neighbors speedup_vs_cpu") + for particles in args.sizes: + cpu_result = _time_backend('cpu', particles, args.repeats) + results = [cpu_result] + for backend in args.backends: + if backend == 'cpu': + continue + results.append(_time_backend(backend, particles, args.repeats)) + for result in results: + speedup = cpu_result.p50_ms / result.p50_ms + print( + f"{result.backend:12s} " + f"{result.particles:9d} " + f"{result.repeats:7d} " + f"{result.p50_ms:8.3f} " + f"{result.avg_neighbors:13.3f} " + f"{speedup:14.3f}" + ) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/experiment.md new file mode 100644 index 000000000..2946f1010 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/experiment.md @@ -0,0 +1,214 @@ +--- +type: experiment +id: 2026-06-15_warp-nnps-bruteforce-baseline +created: 2026-06-15T10:05:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp NNPS Baselines + +## Headline + +At 1,000,000 particles on PrediQT-02, `warp_grid_device` reaches `88.288x` +CPU speed versus `LinkedListNNPS` while matching the average neighbor count +(`25.568`) on Intel(R) Core(TM) Ultra 7 155H CPU and NVIDIA GeForce RTX 4060 +Laptop GPU. + +## Purpose + +Establish the first Warp NNPS implementation baselines. + +The implementation uses Warp kernels for the geometric distance test and +returns source-local neighbor indices through PySPH's existing `UIntArray` +contract. It supports a per-query flags path, a cached flat-neighbor path, and +a uniform-grid/cell-list path. + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh +``` + +The wrappers use the active venv if one is already active; otherwise they +source: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +Warp brute force should match CPU linked-list NNPS neighbor sets for small, +deterministic fixtures covering: + +- 1D, 2D, and multiple particle arrays; +- source/destination smoothing-length inclusion; +- sorted-gid ordering; +- update after ParticleArray mutation. + +The uncached brute-force implementation is expected to have low CPU-relative +speedup for many queries because it launches a kernel and reads flags back for +each destination particle. The cached brute-force path avoids per-particle +launches but remains O(N^2). The uniform-grid path builds source cell lists and +scans adjacent cells, making it the first performance-relevant Warp NNPS +baseline. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_nnps.py +``` + +`run_benchmark.sh` runs `benchmark_warp_nnps.py`, which records: + +- CPU model; +- GPU model, driver, and memory; +- Warp version; +- backend; +- particle count; +- repeat count; +- all-particle query p50 time; +- average neighbor count; +- speedup relative to CPU. + +For large particle counts, avoid the uncached brute-force backends. Use +`warp_grid` for PySPH host-facing neighbor access and `warp_grid_device` for +bulk GPU cache construction: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid_device +``` + +## Success Criteria + +This experiment succeeds when: + +- all focused Warp NNPS correctness tests pass; +- benchmark cases complete without changing neighbor counts; +- timing output separates CPU linked-list, uncached Warp brute-force, and cached + Warp brute-force, and Warp grid paths; +- timing output reports CPU-relative speedup; +- sub-1.0 Warp speedups are classified as expected per-query launch/readback + cost or follow-up optimization targets. + +## Results + +Initial focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_nnps.py +12 passed +``` + +Current smoke benchmark: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 128 --repeats 1 +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Expected/current smoke result: + +```text +backend particles repeats p50_ms avg_neighbors speedup_vs_cpu +cpu 128 1 0.436 21.781 1.000 +warp 128 1 150.225 21.781 0.003 +warp_cached 128 1 22.861 21.781 0.019 +warp_grid 128 1 10.532 21.781 0.041 +``` + +Interpretation: + +- CPU, uncached Warp, cached Warp, and Warp grid report the same average + neighbor count for the smoke case. +- `speedup_vs_cpu` is `cpu_p50_ms / backend_p50_ms`; values below `1.0` mean + the backend has not yet reached CPU speed. +- Uncached Warp brute force is slower here because it launches one kernel per + destination particle and reads a source-length flags array back to host for + every query. +- Cached Warp brute force builds a flat neighbor list and is substantially + faster than the uncached path, but it remains an O(N^2) bridge. +- Warp grid builds source cell lists and scans adjacent cells. It is now the + first cell-list baseline, though the smoke run still includes host-facing + neighbor-list materialization. +- On this 128-particle smoke case, the best Warp path is `warp_grid` at + `0.041x` CPU speed. This is a baseline for optimization, not a claimed + acceleration result. + +Large host-facing benchmark: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid +``` + +Hardware and runtime are the same as above. + +```text +backend particles repeats p50_ms avg_neighbors speedup_vs_cpu +cpu 1000000 1 17253.885 25.568 1.000 +warp_grid 1000000 1 4041.391 25.568 4.269 +``` + +Interpretation: + +- At 1,000,000 particles, CPU and `warp_grid` report the same average neighbor + count. +- `warp_grid` reaches `4.269x` CPU speed on the current host-facing benchmark. +- This result still includes PySPH-style host-facing neighbor access, so a + device-resident equation-consumption path remains the next performance target. + +Large device-oriented benchmark: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid_device +``` + +Hardware and runtime are the same as above. + +```text +backend particles repeats p50_ms avg_neighbors speedup_vs_cpu +cpu 1000000 1 6236.716 25.568 1.000 +warp_grid_device 1000000 1 70.640 25.568 88.288 +``` + +Interpretation: + +- At 1,000,000 particles, CPU and `warp_grid_device` report the same average + neighbor count. +- `warp_grid_device` reaches `88.288x` CPU speed for bulk grid neighbor-cache + construction. +- This is the relevant GPU-side result. It avoids the per-particle + `get_nearest_particles()` loop and does not materialize every particle's + neighbors through `UIntArray`. +- The remaining integration target is to let equation kernels consume this + device-resident neighbor cache directly. + +## Conclusion + +Warp NNPS has a first correctness-oriented implementation, a cached flat +neighbor-list bridge, and a uniform-grid/cell-list baseline. The next useful +implementation work is optimizing the grid path and reducing host readback. + +## Follow-ups + +- Optimize uniform-grid/cell-list update and query kernels. +- Keep cell-list results device-resident for equation-kernel consumption. +- Add periodic boundary fixtures before Application integration. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh new file mode 100755 index 000000000..98096978a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/benchmark_warp_nnps.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_correctness.sh new file mode 100755 index 000000000..749d0197d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/benchmark_neighbor_sum.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/benchmark_neighbor_sum.py new file mode 100644 index 000000000..ecb4b03fb --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/benchmark_neighbor_sum.py @@ -0,0 +1,160 @@ +"""Benchmark device-side consumption of Warp NNPS neighbor caches.""" + +from __future__ import annotations + +import argparse +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import dataclass + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +import warp as wp +from cyarray.carray import UIntArray + +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS + + +@dataclass(frozen=True) +class Result: + backend: str + particles: int + repeats: int + p50_ms: float + avg_neighbor_sum: float + checksum: float + + +def _run_text(cmd: list[str]) -> str: + try: + return subprocess.check_output( + cmd, stderr=subprocess.DEVNULL, text=True + ).strip() + except Exception: + return "unknown" + + +def _cpu_model() -> str: + text = _run_text(["lscpu"]) + for line in text.splitlines(): + if line.startswith("Model name:"): + return line.split(":", 1)[1].strip() + return platform.processor() or "unknown" + + +def _gpu_model() -> str: + text = _run_text([ + "nvidia-smi", + "--query-gpu=name,driver_version,memory.total", + "--format=csv,noheader", + ]) + return text.splitlines()[0] if text else "unknown" + + +def _make_particles(n: int, backend: str | None = None): + rng = np.random.default_rng(12345) + x = rng.random(n) + y = rng.random(n) + z = np.zeros(n) + dx = np.sqrt(1.0 / n) + h = np.ones(n) * 1.4 * dx + m = np.ones(n) + kwargs = dict(name='fluid', x=x, y=y, z=z, h=h, m=m) + if backend is None: + return [get_particle_array(**kwargs)] + return [get_particle_array(backend=backend, **kwargs)] + + +def _cpu_neighbor_sum(arrays, n: int) -> tuple[float, float]: + nnps = LinkedListNNPS(dim=2, particles=arrays, radius_scale=2.0) + nbrs = UIntArray() + total = 0.0 + mass = arrays[0].m + nnps.set_context(0, 0) + for d_idx in range(n): + nnps.get_nearest_particles(0, 0, d_idx, nbrs) + total += float(np.sum(mass[nbrs.get_npy_array()[:nbrs.length]])) + return total / n, total + + +def _warp_neighbor_sum(arrays, n: int) -> tuple[float, float]: + nnps = UniformGridWarpNNPS(dim=2, particles=arrays, radius_scale=2.0) + out = nnps.compute_neighbor_sum(0, 0, 'm') + checksum = float(wp.utils.array_sum(out)) + return checksum / n, checksum + + +def _time_backend(backend: str, particles: int, repeats: int) -> Result: + samples = [] + avg_neighbor_sum = 0.0 + checksum = 0.0 + for _ in range(repeats): + arrays = _make_particles( + particles, backend='warp' if backend == 'warp_grid_reduce' else None + ) + start = time.perf_counter() + if backend == 'warp_grid_reduce': + avg_neighbor_sum, checksum = _warp_neighbor_sum(arrays, particles) + elif backend == 'cpu_reduce': + avg_neighbor_sum, checksum = _cpu_neighbor_sum(arrays, particles) + else: + raise ValueError("Unknown backend: %s" % backend) + samples.append((time.perf_counter() - start) * 1000.0) + return Result( + backend=backend, + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + avg_neighbor_sum=avg_neighbor_sum, + checksum=checksum, + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument('--sizes', type=int, nargs='+', default=[1000000]) + parser.add_argument('--repeats', type=int, default=1) + args = parser.parse_args() + + wp.init() + + print(f"# host: {platform.node()}") + print(f"# python: {sys.executable}") + print(f"# cpu: {_cpu_model()}") + print(f"# gpu: {_gpu_model()}") + print(f"# warp: {wp.__version__}") + print( + "backend particles repeats p50_ms avg_neighbor_sum checksum " + "speedup_vs_cpu" + ) + for particles in args.sizes: + cpu_result = _time_backend('cpu_reduce', particles, args.repeats) + warp_result = _time_backend( + 'warp_grid_reduce', particles, args.repeats + ) + for result in (cpu_result, warp_result): + speedup = cpu_result.p50_ms / result.p50_ms + print( + f"{result.backend:16s} " + f"{result.particles:9d} " + f"{result.repeats:7d} " + f"{result.p50_ms:8.3f} " + f"{result.avg_neighbor_sum:16.3f} " + f"{result.checksum:12.3f} " + f"{speedup:14.3f}" + ) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/experiment.md new file mode 100644 index 000000000..b2929ff4a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/experiment.md @@ -0,0 +1,139 @@ +--- +type: experiment +id: 2026-06-15_warp-nnps-device-consumption +created: 2026-06-15T11:05:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp NNPS Device Consumption + +## Headline + +At 1,000,000 particles on PrediQT-02, `warp_grid_reduce` reaches `145.583x` +CPU speed versus `LinkedListNNPS` for an equation-like neighbor mass sum on +Intel(R) Core(TM) Ultra 7 155H CPU and NVIDIA GeForce RTX 4060 Laptop GPU. + +## Purpose + +The previous NNPS baseline proved that `UniformGridWarpNNPS` can build a flat +neighbor cache on the GPU quickly. This experiment checks the next integration +question: can a kernel consume that cache directly without walking +`get_nearest_particles()` and `UIntArray` for every particle? + +The current consumer is intentionally simple. It computes, for each destination +particle, the sum of a scalar source property over all neighbors. With `m=1`, +the output checksum is the total neighbor count, so CPU and GPU checksums should +match the existing NNPS count benchmark. + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh --sizes 1000000 --repeats 1 +``` + +The wrappers use the active venv if one is already active; otherwise they +source: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +`warp_grid_reduce` should match `cpu_reduce` for average neighbor sum and +produce a near-identical checksum while running substantially faster at +1,000,000 particles, because the GPU path builds the grid cache and consumes it +on device before returning a single checksum. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_nnps.py +``` + +`run_benchmark.sh` runs `benchmark_neighbor_sum.py`, which records: + +- CPU model; +- GPU model, driver, and memory; +- Warp version; +- backend; +- particle count; +- repeat count; +- p50 time; +- average neighbor-property sum; +- checksum; +- speedup relative to `cpu_reduce`. + +## Success Criteria + +This experiment succeeds when: + +- focused Warp NNPS correctness tests pass; +- `cpu_reduce` and `warp_grid_reduce` report matching average neighbor sum and + a checksum delta small enough to classify as a boundary-sensitive floating + point difference; +- `warp_grid_reduce` reports a useful `speedup_vs_cpu` at 1,000,000 particles; +- the result lists CPU and GPU hardware. + +## Results + +Focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_nnps.py +17 passed +``` + +Large device-consumption benchmark: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh --sizes 1000000 --repeats 1 +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Current result: + +```text +backend particles repeats p50_ms avg_neighbor_sum checksum speedup_vs_cpu +cpu_reduce 1000000 1 10212.920 25.568 25568198.000 1.000 +warp_grid_reduce 1000000 1 70.152 25.568 25568204.000 145.583 +``` + +Interpretation: + +- `warp_grid_reduce` builds the device-resident uniform-grid neighbor cache, + runs a Warp kernel that sums source `m` over each destination particle's + neighbors, and reduces the output to one checksum. +- Focused correctness now checks the actual packed GPU neighbor indices against + `LinkedListNNPS` for deterministic random 2D and cross-array 3D fixtures, in + addition to the equation-like neighbor-sum consumer. +- Average neighbor sum matches to the reported precision: `25.568`. +- The checksum delta is `6` over roughly `25.6M` accumulated neighbor + contributions. This is small enough for the large random benchmark headline, + but exact CPU/GPU neighbor-set parity remains covered by focused deterministic + tests rather than inferred from this aggregate run. +- On this hardware, the device-consumption path reaches `145.583x` CPU speed. + +## Conclusion + +The Warp grid cache is now useful beyond construction: a small equation-like +kernel can consume it entirely on device. The next implementation target is to +turn this proof into a reusable equation-loop contract instead of a one-off +neighbor-sum helper. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh new file mode 100755 index 000000000..d3440c632 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/benchmark_neighbor_sum.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_correctness.sh new file mode 100755 index 000000000..749d0197d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/benchmark_pressure_gradient.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/benchmark_pressure_gradient.py new file mode 100644 index 000000000..51b8c3abe --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/benchmark_pressure_gradient.py @@ -0,0 +1,226 @@ +"""Benchmark PySPH CPU/Cython and Warp pressure-gradient kernels.""" + +from __future__ import annotations + +import argparse +import gc +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import dataclass + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +import warp as wp + +from pysph.base.kernels import CubicSpline +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import compute_pressure_gradient +from pysph.sph.equation import Equation +from pysph.tools.sph_evaluator import SPHEvaluator + + +class PressureGradientOnly(Equation): + """Pure inviscid pressure-gradient loop for CPU/Cython comparison.""" + + def initialize(self, d_idx, d_au, d_av, d_aw): + d_au[d_idx] = 0.0 + d_av[d_idx] = 0.0 + d_aw[d_idx] = 0.0 + + def loop(self, d_idx, s_idx, d_rho, d_p, d_au, d_av, d_aw, + s_m, s_rho, s_p, DWIJ): + rhoi21 = 1.0/(d_rho[d_idx]*d_rho[d_idx]) + rhoj21 = 1.0/(s_rho[s_idx]*s_rho[s_idx]) + tmp = d_p[d_idx]*rhoi21 + s_p[s_idx]*rhoj21 + d_au[d_idx] += -s_m[s_idx] * tmp * DWIJ[0] + d_av[d_idx] += -s_m[s_idx] * tmp * DWIJ[1] + d_aw[d_idx] += -s_m[s_idx] * tmp * DWIJ[2] + + +@dataclass(frozen=True) +class Result: + backend: str + particles: int + repeats: int + p50_ms: float + au_checksum: float + av_checksum: float + aw_checksum: float + status: str = "ok" + + +def _run_text(cmd: list[str]) -> str: + try: + return subprocess.check_output( + cmd, stderr=subprocess.DEVNULL, text=True + ).strip() + except Exception: + return "unknown" + + +def _cpu_model() -> str: + text = _run_text(["lscpu"]) + for line in text.splitlines(): + if line.startswith("Model name:"): + return line.split(":", 1)[1].strip() + return platform.processor() or "unknown" + + +def _gpu_model() -> str: + text = _run_text([ + "nvidia-smi", + "--query-gpu=name,driver_version,memory.total", + "--format=csv,noheader", + ]) + return text.splitlines()[0] if text else "unknown" + + +def _make_particles(n: int, backend: str | None = None): + rng = np.random.default_rng(12345) + x = rng.random(n) + y = rng.random(n) + z = np.zeros(n) + dx = np.sqrt(1.0 / n) + h = np.ones(n) * 1.4 * dx + m = np.ones(n) * dx * dx + rho = 1000.0 + 10.0 * rng.random(n) + p = 100.0 * rng.random(n) + zeros = np.zeros(n) + kwargs = dict( + name='fluid', x=x, y=y, z=z, h=h, m=m, rho=rho, p=p, + au=zeros.copy(), av=zeros.copy(), aw=zeros.copy() + ) + if backend is None: + return [get_particle_array(**kwargs)] + return [get_particle_array(backend=backend, **kwargs)] + + +def _time_cpu(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles) + evaluator = SPHEvaluator( + arrays=arrays, + equations=[PressureGradientOnly(dest='fluid', sources=['fluid'])], + dim=2, + kernel=CubicSpline(dim=2), + backend='cython', + nnps_factory=LinkedListNNPS, + ) + samples = [] + au_checksum = av_checksum = aw_checksum = 0.0 + for _ in range(repeats): + arrays[0].au[:] = 0.0 + arrays[0].av[:] = 0.0 + arrays[0].aw[:] = 0.0 + start = time.perf_counter() + evaluator.evaluate(0.0, 0.1) + samples.append((time.perf_counter() - start) * 1000.0) + au_checksum = float(np.sum(arrays[0].au)) + av_checksum = float(np.sum(arrays[0].av)) + aw_checksum = float(np.sum(arrays[0].aw)) + return Result( + backend='cpu_cython', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + au_checksum=au_checksum, + av_checksum=av_checksum, + aw_checksum=aw_checksum, + ) + + +def _time_warp(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles, backend='warp') + nnps = UniformGridWarpNNPS(dim=2, particles=arrays, radius_scale=2.0) + samples = [] + au_checksum = av_checksum = aw_checksum = 0.0 + for _ in range(repeats): + start = time.perf_counter() + au, av, aw = compute_pressure_gradient(nnps, 0, 0) + samples.append((time.perf_counter() - start) * 1000.0) + au_checksum = float(wp.utils.array_sum(au.dev)) + av_checksum = float(wp.utils.array_sum(av.dev)) + aw_checksum = float(wp.utils.array_sum(aw.dev)) + return Result( + backend='warp_grid_pgrad', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + au_checksum=au_checksum, + av_checksum=av_checksum, + aw_checksum=aw_checksum, + ) + + +def _safe_time(fn, particles: int, repeats: int, backend: str) -> Result: + try: + return fn(particles, repeats) + except Exception as exc: + return Result( + backend=backend, + particles=particles, + repeats=repeats, + p50_ms=float('nan'), + au_checksum=float('nan'), + av_checksum=float('nan'), + aw_checksum=float('nan'), + status=type(exc).__name__ + ": " + str(exc).splitlines()[0], + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument( + '--sizes', type=int, nargs='+', + default=[1000000, 2000000, 5000000], + ) + parser.add_argument('--repeats', type=int, default=1) + args = parser.parse_args() + + wp.init() + print(f"# host: {platform.node()}", flush=True) + print(f"# python: {sys.executable}", flush=True) + print(f"# cpu: {_cpu_model()}", flush=True) + print(f"# gpu: {_gpu_model()}", flush=True) + print(f"# warp: {wp.__version__}", flush=True) + print( + "backend particles repeats p50_ms au_checksum av_checksum " + "aw_checksum speedup_vs_cpu status", + flush=True, + ) + for particles in args.sizes: + cpu = _safe_time(_time_cpu, particles, args.repeats, 'cpu_cython') + warp = _safe_time(_time_warp, particles, args.repeats, + 'warp_grid_pgrad') + for result in (cpu, warp): + if cpu.status != 'ok' or result.status != 'ok': + speedup = float('nan') + else: + speedup = cpu.p50_ms / result.p50_ms + print( + f"{result.backend:15s} " + f"{result.particles:9d} " + f"{result.repeats:7d} " + f"{result.p50_ms:8.3f} " + f"{result.au_checksum:14.6e} " + f"{result.av_checksum:14.6e} " + f"{result.aw_checksum:14.6e} " + f"{speedup:14.3f} " + f"{result.status}", + flush=True, + ) + gc.collect() + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/experiment.md new file mode 100644 index 000000000..37998a0bd --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/experiment.md @@ -0,0 +1,145 @@ +--- +type: experiment +id: 2026-06-15_warp-pressure-gradient +created: 2026-06-15T14:05:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp Pressure Gradient + +## Headline + +At 5,000,000 particles on PrediQT-02, `warp_grid_pgrad` computes the inviscid +pressure-gradient operation in `2.988 s` versus CPU/Cython `115.695 s`, a +`38.722x` speedup, on Intel(R) Core(TM) Ultra 7 155H CPU and NVIDIA GeForce RTX +4060 Laptop GPU. + +## Purpose + +Add the minimal pressure-gradient acceleration kernel needed after density, +EOS, and continuity: + +```text +a_i = -sum_j m_j * (p_i/rho_i^2 + p_j/rho_j^2) * grad(W_ij) +``` + +This is the inviscid pressure portion of PySPH's WCSPH momentum equation, +without artificial viscosity, tensile correction, CFL bookkeeping, or body +force. Those pieces should be added separately so each part has clean tests and +benchmarks. + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_benchmark.sh --sizes 1000000 2000000 5000000 --repeats 1 +``` + +The wrappers use the active venv if one is already active; otherwise they +source: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +The Warp pressure-gradient kernel should match CPU per-particle reference +fixtures and provide large speedups for million-particle operation benchmarks, +while staying capped at 5M particles. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +``` + +`run_benchmark.sh` runs `benchmark_pressure_gradient.py`, which records: + +- CPU model; +- GPU model, driver, and memory; +- Warp version; +- backend; +- particle count; +- repeat count; +- p50 operation time; +- acceleration checksums; +- speedup relative to `cpu_cython`. + +CPU baseline: + +- PySPH `SPHEvaluator`; +- Cython backend; +- custom `PressureGradientOnly` equation containing the same inviscid pressure + operation; +- `CubicSpline(dim=2)`; +- `LinkedListNNPS`. + +Warp baseline: + +- `compute_pressure_gradient()`; +- `UniformGridWarpNNPS` device neighbor cache; +- Warp CubicSpline gradient kernel. + +## Success Criteria + +This experiment succeeds when: + +- focused tests compare Warp pressure-gradient values against CPU references; +- the benchmark completes 1M, 2M, and 5M particles; +- no pgrad run exceeds 5M particles; +- timing output reports CPU-relative speedup and hardware. + +## Results + +Focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +24 passed +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Current capped sweep: + +```text +backend particles repeats p50_ms au_checksum av_checksum aw_checksum speedup_vs_cpu status +cpu_cython 1000000 1 18838.161 -5.329071e-15 3.552714e-15 0.000000e+00 1.000 ok +warp_grid_pgrad 1000000 1 126.530 -6.198883e-06 -7.152557e-06 0.000000e+00 148.884 ok +cpu_cython 2000000 1 42210.247 -2.842171e-14 1.421085e-14 0.000000e+00 1.000 ok +warp_grid_pgrad 2000000 1 325.059 -1.096725e-05 2.288818e-05 0.000000e+00 129.854 ok +cpu_cython 5000000 1 115694.749 -9.947598e-14 0.000000e+00 0.000000e+00 1.000 ok +warp_grid_pgrad 5000000 1 2987.800 2.288818e-05 0.000000e+00 0.000000e+00 38.722 ok +``` + +Interpretation: + +- Same-array total acceleration checksums are near zero because the pressure + interaction is pair-symmetric; focused per-particle tests carry the primary + correctness signal. +- The 5M cap was honored. No 10M pressure-gradient run was performed. +- The Warp path now has the density, pressure, continuity, and inviscid + acceleration pieces needed for a minimal GPU dynamics step. + +## Conclusion + +The inviscid pressure-gradient kernel is now running on Warp and benchmarked +through 5M particles. The next useful step is a tiny Euler or PEC-style +integrator and a short drop-like GPU loop that wires NNPS, density, EOS, +pressure gradient, and position/velocity update together. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_benchmark.sh new file mode 100755 index 000000000..9f43a911e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/benchmark_pressure_gradient.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_correctness.sh new file mode 100755 index 000000000..ba9ec96a3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/benchmark_summation_density.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/benchmark_summation_density.py new file mode 100644 index 000000000..9897ac7de --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/benchmark_summation_density.py @@ -0,0 +1,200 @@ +"""Benchmark PySPH CPU/Cython and Warp summation-density kernels.""" + +from __future__ import annotations + +import argparse +import gc +import platform +import statistics +import subprocess +import sys +import time +from dataclasses import dataclass + +import numpy as np + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +import warp as wp + +from pysph.base.kernels import CubicSpline +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import compute_summation_density +from pysph.sph.basic_equations import SummationDensity +from pysph.tools.sph_evaluator import SPHEvaluator + + +@dataclass(frozen=True) +class Result: + backend: str + particles: int + repeats: int + p50_ms: float + checksum: float + status: str = "ok" + + +def _run_text(cmd: list[str]) -> str: + try: + return subprocess.check_output( + cmd, stderr=subprocess.DEVNULL, text=True + ).strip() + except Exception: + return "unknown" + + +def _cpu_model() -> str: + text = _run_text(["lscpu"]) + for line in text.splitlines(): + if line.startswith("Model name:"): + return line.split(":", 1)[1].strip() + return platform.processor() or "unknown" + + +def _gpu_model() -> str: + text = _run_text([ + "nvidia-smi", + "--query-gpu=name,driver_version,memory.total", + "--format=csv,noheader", + ]) + return text.splitlines()[0] if text else "unknown" + + +def _make_particles(n: int, backend: str | None = None): + rng = np.random.default_rng(12345) + x = rng.random(n) + y = rng.random(n) + z = np.zeros(n) + dx = np.sqrt(1.0 / n) + h = np.ones(n) * 1.4 * dx + m = np.ones(n) * dx * dx + kwargs = dict(name='fluid', x=x, y=y, z=z, h=h, m=m) + if backend is None: + return [get_particle_array(**kwargs)] + return [get_particle_array(backend=backend, **kwargs)] + + +def _time_cpu(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles) + evaluator = SPHEvaluator( + arrays=arrays, + equations=[SummationDensity(dest='fluid', sources=['fluid'])], + dim=2, + kernel=CubicSpline(dim=2), + backend='cython', + nnps_factory=LinkedListNNPS, + ) + samples = [] + checksum = 0.0 + for _ in range(repeats): + arrays[0].rho[:] = 0.0 + start = time.perf_counter() + evaluator.evaluate(0.0, 0.1) + samples.append((time.perf_counter() - start) * 1000.0) + checksum = float(np.sum(arrays[0].rho)) + return Result( + backend='cpu_cython', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + checksum=checksum, + ) + + +def _time_warp(particles: int, repeats: int) -> Result: + arrays = _make_particles(particles, backend='warp') + nnps = UniformGridWarpNNPS(dim=2, particles=arrays, radius_scale=2.0) + samples = [] + checksum = 0.0 + for _ in range(repeats): + start = time.perf_counter() + rho = compute_summation_density(nnps, 0, 0) + samples.append((time.perf_counter() - start) * 1000.0) + checksum = float(wp.utils.array_sum(rho.dev)) + return Result( + backend='warp_grid_density', + particles=particles, + repeats=repeats, + p50_ms=statistics.median(samples), + checksum=checksum, + ) + + +def _safe_time(fn, particles: int, repeats: int, backend: str) -> Result: + try: + return fn(particles, repeats) + except Exception as exc: + return Result( + backend=backend, + particles=particles, + repeats=repeats, + p50_ms=float('nan'), + checksum=float('nan'), + status=type(exc).__name__ + ": " + str(exc).splitlines()[0], + ) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument( + '--sizes', type=int, nargs='+', + default=[1000000, 2000000, 5000000, 10000000], + ) + parser.add_argument('--repeats', type=int, default=1) + parser.add_argument( + '--backends', nargs='+', + default=['cpu_cython', 'warp_grid_density'], + choices=['cpu_cython', 'warp_grid_density'], + ) + args = parser.parse_args() + + wp.init() + print(f"# host: {platform.node()}", flush=True) + print(f"# python: {sys.executable}", flush=True) + print(f"# cpu: {_cpu_model()}", flush=True) + print(f"# gpu: {_gpu_model()}", flush=True) + print(f"# warp: {wp.__version__}", flush=True) + print( + "backend particles repeats p50_ms checksum speedup_vs_cpu status", + flush=True, + ) + for particles in args.sizes: + results = {} + if 'cpu_cython' in args.backends: + results['cpu_cython'] = _safe_time( + _time_cpu, particles, args.repeats, 'cpu_cython' + ) + if 'warp_grid_density' in args.backends: + results['warp_grid_density'] = _safe_time( + _time_warp, particles, args.repeats, 'warp_grid_density' + ) + + cpu = results.get('cpu_cython') + cpu_time = cpu.p50_ms if cpu is not None and cpu.status == 'ok' else None + for backend in args.backends: + result = results[backend] + if cpu_time is None or result.status != 'ok': + speedup = float('nan') + else: + speedup = cpu_time / result.p50_ms + print( + f"{result.backend:17s} " + f"{result.particles:9d} " + f"{result.repeats:7d} " + f"{result.p50_ms:8.3f} " + f"{result.checksum:14.6e} " + f"{speedup:14.3f} " + f"{result.status}", + flush=True, + ) + gc.collect() + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/experiment.md new file mode 100644 index 000000000..3c49d207c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/experiment.md @@ -0,0 +1,163 @@ +--- +type: experiment +id: 2026-06-15_warp-summation-density +created: 2026-06-15T12:05:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp Summation Density + +## Headline + +At 10,000,000 particles on PrediQT-02, `warp_grid_density` computes SPH +summation density in `3.330 s` versus PySPH CPU/Cython `230.051 s`, a +`69.084x` speedup, with matching checksum on Intel(R) Core(TM) Ultra 7 155H CPU +and NVIDIA GeForce RTX 4060 Laptop GPU. + +## Purpose + +Port a first real SPH equation kernel to Warp and measure operation speedup +against the CPU path. + +The target equation is PySPH's standard +`pysph.sph.basic_equations.SummationDensity`: + +```text +rho_i = sum_j m_j * W_ij +``` + +The Warp implementation uses the device-resident `UniformGridWarpNNPS` neighbor +cache and the same generated-equation convention as PySPH: + +```text +HIJ = 0.5 * (d_h[d_idx] + s_h[s_idx]) +WIJ = CubicSpline(XIJ, RIJ, HIJ) +``` + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_correctness.sh +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh --sizes 1000000 2000000 5000000 10000000 --repeats 1 +``` + +The wrappers use the active venv if one is already active; otherwise they +source: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +The Warp path should match CPU density checksums and significantly outperform +PySPH CPU/Cython for million-particle uniform random 2D cases, because the +neighbor cache and density loop stay on the GPU. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +``` + +`run_benchmark.sh` runs `benchmark_summation_density.py`, which records: + +- CPU model; +- GPU model, driver, and memory; +- Warp version; +- backend; +- particle count; +- repeat count; +- p50 operation time; +- density checksum; +- speedup relative to `cpu_cython`. + +CPU baseline: + +- PySPH `SPHEvaluator`; +- `SummationDensity(dest='fluid', sources=['fluid'])`; +- `CubicSpline(dim=2)`; +- `LinkedListNNPS`; +- Cython backend. + +Warp baseline: + +- `UniformGridWarpNNPS`; +- `compute_summation_density()`; +- device-resident neighbor cache plus Warp CubicSpline density kernel. + +## Success Criteria + +This experiment succeeds when: + +- focused correctness tests compare Warp density values against a CPU + `CubicSpline` reference; +- CPU and Warp benchmark checksums match to reported precision; +- the benchmark completes the requested 1M-to-10M sweep; +- timing output reports CPU-relative speedup; +- CPU and GPU hardware are listed. + +## Results + +Focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +19 passed +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- Python executable: `/home/kunalp/.pqt_venv_e0b41259/bin/python` +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +1,000,000-particle benchmark: + +```text +backend particles repeats p50_ms checksum speedup_vs_cpu status +cpu_cython 1000000 1 16714.931 1.230047e+06 1.000 ok +warp_grid_density 1000000 1 109.601 1.230047e+06 152.508 ok +``` + +2M-to-10M sweep: + +```text +backend particles repeats p50_ms checksum speedup_vs_cpu status +cpu_cython 2000000 1 40101.093 2.463004e+06 1.000 ok +warp_grid_density 2000000 1 176.226 2.463004e+06 227.555 ok +cpu_cython 5000000 1 104574.437 6.159220e+06 1.000 ok +warp_grid_density 5000000 1 1609.723 6.159220e+06 64.964 ok +cpu_cython 10000000 1 230050.832 1.231664e+07 1.000 ok +warp_grid_density 10000000 1 3330.035 1.231664e+07 69.084 ok +``` + +Interpretation: + +- Warp density checksums match CPU/Cython to the reported precision for all + measured sizes. +- The 1M and 2M cases show especially high speedup because the Warp path fits + comfortably and the operation is dominated by parallel neighbor work. +- 5M and 10M still show large speedups, but the speedup is lower than 2M. This + likely reflects the larger flat neighbor cache, device memory pressure, and + cache construction cost on the 8 GiB laptop GPU. +- The 10M result is the current best proof that the pipeline is now doing real + SPH work on the GPU: Warp NNPS cache plus Warp CubicSpline summation density. + +## Conclusion + +The first real SPH equation kernel is now running on the Warp path and shows +large CPU-relative speedups through 10,000,000 particles. The next target is to +generalize this one-equation path into a reusable Warp equation execution +contract and then add the next equations needed for a minimal solver step. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh new file mode 100755 index 000000000..8cc77c809 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/benchmark_summation_density.py "$@" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_correctness.sh new file mode 100755 index 000000000..ba9ec96a3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md new file mode 100644 index 000000000..26619d183 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md @@ -0,0 +1,184 @@ +--- +type: experiment +id: 2026-06-15_warp-wcsph-euler-step +created: 2026-06-15T23:27:00 CET +author: @kunalpuri-prediqt +aspect: gpu-nnps +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp WCSPH Euler and Leapfrog Step + +## Headline + +On PrediQT-02, the Warp WCSPH prototype now supports a one-step Euler chain and +a minimal KDK leapfrog step that keeps density, pressure, pressure-gradient +acceleration, velocity update, position update, and periodic position wrapping +on the device. + +## Purpose + +Wire the already-ported Warp kernels into minimal dynamics steps: + +```text +rho <- summation density +p <- isothermal EOS +a <- inviscid pressure gradient +u <- u + dt*a +x <- x + dt*u +``` + +and: + +```text +a_n <- WCSPH acceleration(x_n) +u_half <- u_n + 0.5*dt*a_n +x_np1 <- x_n + dt*u_half +wrap x_np1 into periodic bounds when requested +refresh NNPS from device x_np1 +a_np1 <- WCSPH acceleration(x_np1) +u_np1 <- u_half + 0.5*dt*a_np1 +``` + +This is not yet a full PySPH integrator or EllipticDrop solver path. It is a +small correctness milestone proving that equation outputs can be consumed by +later Warp kernels without host readback between stages. + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh +``` + +The wrapper uses the active venv if one is already active; otherwise it sources: + +```bash +source "$HOME/prediqt/activate" +``` + +## Hypothesis + +The chained Warp step should produce the same density, pressure, acceleration, +velocity, and position values as CPU reference calculations for small fixtures. +The later kernels must not overwrite device-computed values with stale host +arrays. + +## What To Expect + +This experiment is a correctness case, not a benchmark. A successful run should +print a pytest summary like: + +```text +29 passed, 2 warnings +``` + +The warnings are currently Python/Warp ctypes deprecation warnings and are not +part of the pass/fail signal. + +The run includes these dynamics checks: + +- `test_warp_euler_step_updates_velocity_and_position_on_device`: direct Euler + update with known acceleration. +- `test_warp_wc_sph_euler_step_matches_cpu_expected_state`: full Euler WCSPH + chain: + +```text +rho <- summation density +p <- isothermal EOS +a <- inviscid pressure gradient +u <- u + dt*a +x <- x + dt*u +``` + +- `test_warp_leapfrog_kick_drift_and_wrap_update_device_state`: direct + leapfrog half-kick, drift, and periodic position wrap. +- `test_warp_wc_sph_leapfrog_step_matches_cpu_expected_state`: full KDK + leapfrog WCSPH chain. +- `test_uniform_grid_warp_nnps_can_rebuild_from_device_positions`: NNPS refresh + after device-side position changes without pushing stale host positions. + +Failure means either the device kernels disagree with the CPU reference values, +the device-updated state was overwritten by stale host arrays, or the NNPS +refresh did not see device-side coordinates. + +## Execution + +`run_correctness.sh` runs: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +``` + +The key tests are: + +- direct Euler velocity/position update; +- direct leapfrog kick, drift, and periodic wrap update; +- full `wc_sph_euler_step()` comparison against CPU-computed density, EOS, + pressure-gradient acceleration, and final state. +- full `wc_sph_leapfrog_step()` comparison against CPU-computed KDK density, + EOS, pressure-gradient acceleration, and final state. +- `UniformGridWarpNNPS.update(push=False)` rebuilds from device positions + instead of stale host coordinates. + +## Success Criteria + +This experiment succeeds when: + +- the Euler kernel updates velocity and position on the device; +- the leapfrog kick and drift kernels update velocity and position on the + device; +- periodic wrapping keeps drifted coordinates inside supplied device-side + bounds; +- `wc_sph_euler_step()` chains density, EOS, pgrad, and Euler update without + intermediate host pull/push; +- `wc_sph_leapfrog_step()` recomputes acceleration after drift through an NNPS + refresh that skips host pushes; +- focused tests compare actual state values, not just neighbor counts or + checksums; +- all values are finite. + +## Results + +Focused correctness: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +26 passed, 2 warnings +``` + +Repeated-step checkpoint: + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh +29 passed, 2 warnings in 3.05s +``` + +Hardware and runtime: + +- host: PrediQT-02 +- Python environment: PQT venv +- CPU: Intel(R) Core(TM) Ultra 7 155H +- GPU: NVIDIA GeForce RTX 4060 Laptop GPU, driver 595.79, 8188 MiB +- Warp: 1.14.0 +- PySPH: editable install from this checkout + +Interpretation: + +- The new `push=False` mode is required for chained Warp calls; otherwise EOS, + pgrad, Euler, or leapfrog refresh can clobber device-computed inputs with + stale host arrays. +- `UniformGridWarpNNPS.update(push=False)` is now the first device-aware refresh + path after positions move. +- Periodic support in this checkpoint is position wrapping. Periodic + minimum-image neighbor distances and periodic cell lookup remain follow-up + work. + +## Conclusion + +The first minimal Warp dynamics steps are correct for focused fixtures. Euler +and KDK leapfrog now have device-side correctness coverage, and NNPS can be +refreshed from device positions without forcing the ParticleArray host copy to +become the source of truth between steps. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh new file mode 100755 index 000000000..ba9ec96a3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/RESULTS_REPORT_HOWTO.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/RESULTS_REPORT_HOWTO.md new file mode 100644 index 000000000..34d2e7f1d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/RESULTS_REPORT_HOWTO.md @@ -0,0 +1,71 @@ +# Producing the production results report (run on a capable GPU machine) + +The headline runs (especially the million-particle CPU PySPH Application and any +longer resolved run) are CPU-heavy and best run on a capable machine. This +directory ships a driver that runs them fresh and assembles a Markdown report. + +## 1. Set up the environment + +Follow [`BUILD.md`](../../../../../BUILD.md) at the repo root: build the Cython +extensions and install `warp-lang`. Confirm the GPU is detected: + +```bash +python -c "import warp as wp; wp.init()" # should list your CUDA device +``` + +GPU compatibility (V100/A100/H100/RTX 5090/RTX PRO 6000 Blackwell) is covered in +BUILD.md section 10 -- no code changes are needed per GPU. + +## 2. Validate the pipeline (tiny, ~1-2 min) + +```bash +R=.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner +PYTHONPATH=$R python $R/generate_results_report.py --quick \ + --out-dir /tmp/report-smoke --gpu-label "smoke" +``` + +This runs nx=20 resolved + nx=30 / 5-step million and writes +`/tmp/report-smoke/RESULTS_REPORT.md`. If that looks right, run the full report. + +## 3. Full report + +```bash +PYTHONPATH=$R python $R/generate_results_report.py \ + --out-dir results-report --gpu-label "NVIDIA H100 80GB" +``` + +- Runs the **resolved nx=100 apples-to-apples** comparison (real PySPH CPU + Application vs Warp, identical step count) and the **million-particle / + 100-fixed-step** throughput comparison. +- For a fuller trajectory comparison add e.g. `--resolved-tf 0.0008,0.0038` + (default) or a larger final time -- the longer CPU run is the multi-hour part + that this machine is for. +- Writes `results-report/RESULTS_REPORT.md` plus the raw summary JSONs. + +## Cross-GPU performance sweep + +To compare GPUs, run `gpu_perf_sweep.py` on each one and paste the JSON block it +prints. It sweeps particle count (via `nx`) and reports the steady per-step wall +time and throughput (particle-steps/s) for the grid-direct continuity PEC step; +it's GPU-only (fast), warms the kernel once, and catches OOM per point so it +finds the capacity ceiling. + +```bash +PYTHONPATH=$R python $R/gpu_perf_sweep.py --label "" --output sweep-.json +# custom: --nx-list 100,200,400,565,1000,1400,1800,2600 --steps 16 --warmup 6 +``` + +`nx` maps to particle count via the disk fill (~`pi*nx^2`): nx=100 ~ 31k, +565 ~ 1.0M, 1000 ~ 3.1M, 1800 ~ 10M. The collected per-GPU JSONs become the +cross-GPU performance artifact. + +## Notes + +- The CPU baseline is the real single-threaded PySPH Cython Application; report + it as GPU-vs-single-threaded-CPU. +- The million-particle run uses fixed timesteps so both sides do identical work. +- Memory headroom (A100 80 GB / H100 / 96 GB Blackwell) allows scaling the + particle count well beyond the 8 GB dev box; raise `--million-nx` accordingly + (watch host RAM for the initial numpy mgrid). +- First Warp run on a new GPU cold-compiles the fused grid kernel once, then + loads from the on-disk cache. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py new file mode 100644 index 000000000..1594e74ea --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py @@ -0,0 +1,329 @@ +#!/usr/bin/env python3 +"""Compare Warp elliptical drop against a CPU PySPH-primitive baseline.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +import numpy as np + +from cyarray.carray import UIntArray + +from pysph.base.kernels import Gaussian +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array + +from warp_elliptical_drop_runner import WarpEllipticalDropRunner + + +def _neighbors(nnps, src_index, dst_index, d_idx): + nbrs = UIntArray() + nnps.get_nearest_particles(src_index, dst_index, d_idx, nbrs) + return nbrs.get_npy_array()[:nbrs.length] + + +def _create_cpu_particles(nx, rho0, hdx): + dx = 1.0 / nx + x, y = np.mgrid[-1.05:1.05 + 1.0e-4:dx, + -1.05:1.05 + 1.0e-4:dx] + condition = ~((x*x + y*y - 1.0) > 1.0e-10) + x = np.asarray(x[condition].ravel(), dtype=np.float64) + y = np.asarray(y[condition].ravel(), dtype=np.float64) + z = np.zeros_like(x) + m = np.ones_like(x) * dx * dx * rho0 + h = np.ones_like(x) * hdx * dx + rho = np.ones_like(x) * rho0 + p = np.zeros_like(x) + cs = np.zeros_like(x) + u = -100.0 * x + v = 100.0 * y + w = np.zeros_like(x) + zeros = np.zeros_like(x) + pa = get_particle_array( + name='fluid', x=x, y=y, z=z, h=h, m=m, rho=rho, p=p, cs=cs, + u=u, v=v, w=w, au=zeros.copy(), av=zeros.copy(), aw=zeros.copy(), + ax=zeros.copy(), ay=zeros.copy(), az=zeros.copy(), + dt_cfl=zeros.copy(), dt_force=zeros.copy(), + ) + return pa + + +def _summation_density(pa, kernel, radius_scale): + nnps = LinkedListNNPS(dim=2, particles=[pa], radius_scale=radius_scale) + rho = np.zeros_like(pa.rho) + for i in range(pa.get_number_of_particles()): + total = 0.0 + for j in _neighbors(nnps, 0, 0, i): + xij = [pa.x[i] - pa.x[j], pa.y[i] - pa.y[j], 0.0] + rij = np.sqrt(xij[0]*xij[0] + xij[1]*xij[1]) + hij = 0.5 * (pa.h[i] + pa.h[j]) + total += pa.m[j] * kernel.kernel(xij=xij, rij=rij, h=hij) + rho[i] = total + pa.rho[:] = rho + + +def _tait_eos(pa, rho0, c0, gamma, p0): + ratio = pa.rho / rho0 + pa.p[:] = p0 + (rho0*c0*c0/gamma) * (ratio**gamma - 1.0) + pa.cs[:] = c0 * ratio**(0.5 * (gamma - 1.0)) + + +def _pressure_gradient(pa, kernel, radius_scale): + nnps = LinkedListNNPS(dim=2, particles=[pa], radius_scale=radius_scale) + au = np.zeros_like(pa.au) + av = np.zeros_like(pa.av) + aw = np.zeros_like(pa.aw) + for i in range(pa.get_number_of_particles()): + rhoi21 = 1.0 / (pa.rho[i] * pa.rho[i]) + tmpi = pa.p[i] * rhoi21 + for j in _neighbors(nnps, 0, 0, i): + xij = [pa.x[i] - pa.x[j], pa.y[i] - pa.y[j], 0.0] + rij = np.sqrt(xij[0]*xij[0] + xij[1]*xij[1]) + hij = 0.5 * (pa.h[i] + pa.h[j]) + dwij = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + rhoj21 = 1.0 / (pa.rho[j] * pa.rho[j]) + fac = -pa.m[j] * (tmpi + pa.p[j] * rhoj21) + au[i] += fac * dwij[0] + av[i] += fac * dwij[1] + aw[i] += fac * dwij[2] + pa.au[:] = au + pa.av[:] = av + pa.aw[:] = aw + + +def _artificial_viscosity(pa, kernel, radius_scale, alpha, beta): + nnps = LinkedListNNPS(dim=2, particles=[pa], radius_scale=radius_scale) + for i in range(pa.get_number_of_particles()): + for j in _neighbors(nnps, 0, 0, i): + xij = [pa.x[i] - pa.x[j], pa.y[i] - pa.y[j], 0.0] + vij = [pa.u[i] - pa.u[j], pa.v[i] - pa.v[j], 0.0] + vdotx = vij[0]*xij[0] + vij[1]*xij[1] + if vdotx < 0.0: + rij2 = xij[0]*xij[0] + xij[1]*xij[1] + rij = np.sqrt(rij2) + hij = 0.5 * (pa.h[i] + pa.h[j]) + mu = hij * vdotx / (rij2 + 0.01*hij*hij) + rhoij1 = 2.0 / (pa.rho[i] + pa.rho[j]) + cij = 0.5 * (pa.cs[i] + pa.cs[j]) + piij = (-alpha*cij*mu + beta*mu*mu) * rhoij1 + dwij = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + fac = -pa.m[j] * piij + pa.au[i] += fac * dwij[0] + pa.av[i] += fac * dwij[1] + pa.aw[i] += fac * dwij[2] + + +def _xsph(pa, kernel, radius_scale, eps): + pa.ax[:] = 0.0 + pa.ay[:] = 0.0 + pa.az[:] = 0.0 + if eps is None or eps == 0.0: + return + nnps = LinkedListNNPS(dim=2, particles=[pa], radius_scale=radius_scale) + for i in range(pa.get_number_of_particles()): + for j in _neighbors(nnps, 0, 0, i): + xij = [pa.x[i] - pa.x[j], pa.y[i] - pa.y[j], 0.0] + vij = [pa.u[i] - pa.u[j], pa.v[i] - pa.v[j], 0.0] + rij = np.sqrt(xij[0]*xij[0] + xij[1]*xij[1]) + hij = 0.5 * (pa.h[i] + pa.h[j]) + wij = kernel.kernel(xij=xij, rij=rij, h=hij) + rhoij1 = 2.0 / (pa.rho[i] + pa.rho[j]) + tmp = -eps * pa.m[j] * wij * rhoij1 + pa.ax[i] += tmp * vij[0] + pa.ay[i] += tmp * vij[1] + + +def _adaptive_dt(pa, radius_scale, c0, cfl, dt_min, dt_max): + nnps = LinkedListNNPS(dim=2, particles=[pa], radius_scale=radius_scale) + pa.dt_cfl[:] = 0.0 + pa.dt_force[:] = pa.au*pa.au + pa.av*pa.av + pa.aw*pa.aw + for i in range(pa.get_number_of_particles()): + for j in _neighbors(nnps, 0, 0, i): + xij = [pa.x[i] - pa.x[j], pa.y[i] - pa.y[j], 0.0] + vij = [pa.u[i] - pa.u[j], pa.v[i] - pa.v[j], 0.0] + rij2 = xij[0]*xij[0] + xij[1]*xij[1] + if rij2 > 1.0e-12: + hij = 0.5 * (pa.h[i] + pa.h[j]) + vdotx = vij[0]*xij[0] + vij[1]*xij[1] + fac = abs(hij * vdotx / rij2) + c0 + pa.dt_cfl[i] = max(pa.dt_cfl[i], fac) + hmin = np.min(pa.h) + dt = dt_max + max_cfl = np.max(pa.dt_cfl) + max_force = np.max(pa.dt_force) + if max_cfl > 0.0: + dt = min(dt, cfl * hmin / max_cfl) + if max_force > 0.0: + dt = min(dt, cfl * np.sqrt(hmin / np.sqrt(max_force))) + return min(max(dt, dt_min), dt_max) + + +def _compute_acceleration(pa, kernel, radius_scale, rho0, c0, p0, gamma, + alpha, beta): + _summation_density(pa, kernel, radius_scale) + _tait_eos(pa, rho0, c0, gamma, p0) + _pressure_gradient(pa, kernel, radius_scale) + if alpha != 0.0 or beta != 0.0: + _artificial_viscosity(pa, kernel, radius_scale, alpha, beta) + + +def _run_cpu(args): + pa = _create_cpu_particles(args.nx, args.rho0, args.hdx) + kernel = Gaussian(dim=2) + radius_scale = 3.0 + dt_history = [] + time = 0.0 + for _ in range(args.steps): + _compute_acceleration( + pa, kernel, radius_scale, args.rho0, args.c0, args.p0, + args.gamma, args.alpha, args.beta + ) + if args.adaptive_dt: + dt = _adaptive_dt( + pa, radius_scale, args.c0, args.cfl, args.dt_min, + args.dt_max + ) + else: + dt = args.dt + pa.u[:] += 0.5 * dt * pa.au + pa.v[:] += 0.5 * dt * pa.av + pa.w[:] += 0.5 * dt * pa.aw + _xsph(pa, kernel, radius_scale, args.xsph_eps) + pa.x[:] += dt * (pa.u + pa.ax) + pa.y[:] += dt * (pa.v + pa.ay) + pa.z[:] += dt * (pa.w + pa.az) + _compute_acceleration( + pa, kernel, radius_scale, args.rho0, args.c0, args.p0, + args.gamma, args.alpha, args.beta + ) + pa.u[:] += 0.5 * dt * pa.au + pa.v[:] += 0.5 * dt * pa.av + pa.w[:] += 0.5 * dt * pa.aw + dt_history.append(dt) + time += dt + return pa, np.asarray(dt_history), time + + +def _metrics(pa, dt_history, time): + speed2 = pa.u*pa.u + pa.v*pa.v + pa.w*pa.w + radius = np.sqrt(pa.x*pa.x + pa.y*pa.y) + return { + 'particles': int(pa.get_number_of_particles()), + 'time': float(time), + 'dt_min_used': float(np.min(dt_history)), + 'dt_max_used': float(np.max(dt_history)), + 'rho_min': float(np.min(pa.rho)), + 'rho_max': float(np.max(pa.rho)), + 'radius_max': float(np.max(radius)), + 'kinetic_energy': float(0.5 * np.sum(pa.m * speed2)), + 'all_finite': bool(all(np.all(np.isfinite(getattr(pa, name))) + for name in ('x', 'y', 'rho', 'p', 'u', 'v'))), + } + + +def _save_cpu(path, pa, dt_history, metrics): + np.savez( + path, x=pa.x, y=pa.y, z=pa.z, h=pa.h, m=pa.m, rho=pa.rho, p=pa.p, + cs=pa.cs, u=pa.u, v=pa.v, w=pa.w, au=pa.au, av=pa.av, aw=pa.aw, + ax=pa.ax, ay=pa.ay, az=pa.az, dt_cfl=pa.dt_cfl, + dt_force=pa.dt_force, dt_history=dt_history, + metrics=json.dumps(metrics, sort_keys=True), + ) + + +def _plot_side_by_side(cpu_path, warp_path, image_path): + cpu = np.load(cpu_path) + warp = np.load(warp_path) + cpu_speed = np.sqrt(cpu['u']*cpu['u'] + cpu['v']*cpu['v']) + warp_speed = np.sqrt(warp['u']*warp['u'] + warp['v']*warp['v']) + vmax = max(float(cpu_speed.max()), float(warp_speed.max())) + xmin = min(float(cpu['x'].min()), float(warp['x'].min())) + xmax = max(float(cpu['x'].max()), float(warp['x'].max())) + ymin = min(float(cpu['y'].min()), float(warp['y'].min())) + ymax = max(float(cpu['y'].max()), float(warp['y'].max())) + pad = 0.05 * max(xmax - xmin, ymax - ymin) + fig, axes = plt.subplots(1, 2, figsize=(10, 4.6), constrained_layout=True) + for ax, data, speed, title in ( + (axes[0], cpu, cpu_speed, 'CPU PySPH baseline'), + (axes[1], warp, warp_speed, 'Warp GPU'), + ): + sc = ax.scatter(data['x'], data['y'], c=speed, s=8, vmin=0.0, + vmax=vmax, cmap='viridis') + ax.set_title(title) + ax.set_aspect('equal', adjustable='box') + ax.set_xlim(xmin - pad, xmax + pad) + ax.set_ylim(ymin - pad, ymax + pad) + ax.set_xlabel('x') + ax.set_ylabel('y') + fig.colorbar(sc, ax=axes, label='speed') + fig.savefig(image_path, dpi=180) + plt.close(fig) + + +def _parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--nx', type=int, default=8) + parser.add_argument('--steps', type=int, default=2) + parser.add_argument('--dt', type=float, default=1.0e-5) + parser.add_argument('--rho0', type=float, default=1.0) + parser.add_argument('--c0', type=float, default=20.0) + parser.add_argument('--p0', type=float, default=0.0) + parser.add_argument('--hdx', type=float, default=1.3) + parser.add_argument('--alpha', type=float, default=0.1) + parser.add_argument('--beta', type=float, default=0.0) + parser.add_argument('--gamma', type=float, default=7.0) + parser.add_argument('--xsph-eps', type=float, default=0.5) + parser.add_argument('--adaptive-dt', action='store_true') + parser.add_argument('--cfl', type=float, default=0.25) + parser.add_argument('--dt-min', type=float, default=1.0e-7) + parser.add_argument('--dt-max', type=float, default=1.0e-5) + parser.add_argument('--output-dir', default=None) + parser.add_argument('--prefix', default='comparison-smoke') + return parser.parse_args() + + +def main(): + args = _parse_args() + out_dir = Path(args.output_dir) if args.output_dir else Path(__file__).parent + out_dir.mkdir(parents=True, exist_ok=True) + cpu_path = out_dir / f'{args.prefix}-cpu.npz' + warp_path = out_dir / f'{args.prefix}-warp.npz' + image_path = out_dir / f'{args.prefix}.png' + + runner = WarpEllipticalDropRunner( + nx=args.nx, steps=args.steps, dt=args.dt, rho0=args.rho0, + c0=args.c0, p0=args.p0, hdx=args.hdx, alpha=args.alpha, + beta=args.beta, eos='tait', gamma=args.gamma, kernel='gaussian', + xsph_eps=args.xsph_eps, adaptive_dt=args.adaptive_dt, cfl=args.cfl, + dt_min=args.dt_min, dt_max=args.dt_max, output=warp_path + ) + warp_metrics = runner.run() + cpu_pa, cpu_dt_history, cpu_time = _run_cpu(args) + cpu_metrics = _metrics(cpu_pa, cpu_dt_history, cpu_time) + _save_cpu(cpu_path, cpu_pa, cpu_dt_history, cpu_metrics) + _plot_side_by_side(cpu_path, warp_path, image_path) + + metrics = { + 'cpu': cpu_metrics, + 'warp': warp_metrics, + 'cpu_output': str(cpu_path), + 'warp_output': str(warp_path), + 'image': str(image_path), + } + print(json.dumps(metrics, indent=2, sort_keys=True)) + if not cpu_metrics['all_finite']: + raise SystemExit("CPU baseline produced non-finite values") + if not warp_metrics['all_finite']: + raise SystemExit("Warp run produced non-finite values") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png new file mode 100644 index 000000000..fb49e5137 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md new file mode 100644 index 000000000..e49b8556b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md @@ -0,0 +1,720 @@ +--- +type: experiment +id: 2026-06-16_warp-elliptical-drop-runner +created: 2026-06-16T12:30:00 CEST +author: @kunalpuri-prediqt +aspect: validation-benchmarks +status: complete +last_checked: 2026-06-20T06:04:44 CEST +--- + +# Experiment: Warp Elliptical-Drop Runner + +## Headline + +First application-style runner for the elliptical-drop initial condition using +the current Warp NNPS and WCSPH leapfrog prototype. + +## Purpose + +Create the same circular patch and initial velocity field used by PySPH's +elliptical-drop example, then advance it with: + +```text +UniformGridWarpNNPS +wc_sph_leapfrog_step +``` + +This is now a GPU smoke/comparison run. It exercises the same core terms used +by the PySPH no-scheme elliptical-drop example: Gaussian kernel, Tait EOS, +artificial viscosity, XSPH correction, and adaptive timestep control. + +## Active Physics / Integration + +- Gaussian kernel with `radius_scale=3.0` by default. +- Tait EOS with per-particle sound speed `cs`. +- Pressure-gradient acceleration plus additive Monaghan artificial viscosity. +- XSPH correction in the device step path. +- Two density modes: + - `summation`, retained as the original Warp KDK smoke/default path; + - `continuity`, the PySPH Application parity path that computes `arho` and + advances `rho` through WCSPH PEC-style stages on device. +- Device-computed adaptive timestep factors `dt_cfl` and `dt_force`. +- One scalar `dt` transfer from device to host per adaptive step; no full + particle-array pulls during stepping. +- Final checkpoint/output pulls are explicit and used for metrics/plots. + +The smoke comparison script uses PySPH CPU primitives (`LinkedListNNPS`, +`Gaussian`, and the same equation formulas) as a baseline. The resolved +comparison script uses PySPH's no-scheme `Application/Solver` path as the CPU +baseline. + +## What To Expect + +A successful smoke run should: + +- create a non-empty circular particle patch; +- run a small number of Warp leapfrog steps; +- print JSON metrics; +- report `"all_finite": true`; +- record `"kernel": "gaussian"`; +- record `"xsph_eps": 0.5`; +- record `"adaptive_dt": true`; +- write `results-smoke.npz`. + +The default smoke settings are intentionally conservative: + +```text +nx=8 +steps=2 +dt=1.0e-5 +c0=20.0 +alpha=0.1 +beta=0.0 +eos=tait +gamma=7.0 +kernel=gaussian +xsph_eps=0.5 +adaptive_dt=true +cfl=0.25 +``` + +## Setup + +Run from the repository root: + +```bash +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +``` + +## Success Criteria + +This experiment succeeds when: + +- the wrapper exits with status 0; +- output file `results-smoke.npz` exists and is non-empty; +- metrics report `particles > 0`; +- metrics report `all_finite == true`; +- metrics record `alpha == 0.1` and `beta == 0.0`; +- metrics record `eos == "tait"` and `gamma == 7.0`; +- metrics record `kernel == "gaussian"`, `radius_scale == 3.0`, + `xsph_eps == 0.5`, and `adaptive_dt == true`; +- metrics record finite `dt_min_used`, `dt_max_used`, and `dt_last`; +- final scalar bounds and kinetic energy are printed for inspection. + +The comparison script succeeds when: + +- CPU and Warp `.npz` outputs exist; +- CPU and Warp metrics report `all_finite == true`; +- side-by-side image `comparison-smoke.png` exists and is non-empty. + +The resolved comparison succeeds when: + +- PySPH CPU Application and Warp GPU both reach the requested checkpoint times; +- metrics report `all_finite == true` at every checkpoint; +- runtime and average-step-time metrics are recorded; +- side-by-side images exist and include exact ellipse overlays. + +## Results + +Smoke run: + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 22.79 ms (cached) +Module pysph.base.warp_sph 1bd567e load on device 'cuda:0' took 9.35 ms (cached) +{ + "adaptive_dt": true, + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "cfl": 0.25, + "cs_max": 19.991374969482422, + "cs_min": 3.055661916732788, + "dt": 1e-05, + "dt_last": 9.999999747378752e-06, + "dt_max": 1e-05, + "dt_max_used": 9.999999747378752e-06, + "dt_min": 1e-07, + "dt_min_used": 9.999999747378752e-06, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "kinetic_energy": 8078.179766857993, + "nx": 8, + "p_max": -0.05748271942138672, + "p_min": -56.429779052734375, + "particles": 204, + "radius_max": 0.9978295868060059, + "radius_scale": 3.0, + "rho_max": 0.9998562335968018, + "rho_min": 0.534595251083374, + "steps": 2, + "time": 1.9999999494757503e-05, + "x_max": 0.9481551647186279, + "x_min": -0.9232051968574524, + "xsph_eps": 0.5, + "y_max": 0.951850950717926, + "y_min": -0.9268011450767517 +} +``` + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-smoke.npz +``` + +CPU/Warp side-by-side comparison smoke: + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --xsph-eps 0.5 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --dt-max 1.0e-5 --prefix comparison-smoke +{ + "cpu": { + "all_finite": true, + "dt_max_used": 1e-05, + "dt_min_used": 1e-05, + "kinetic_energy": 8078.179846214378, + "particles": 204, + "radius_max": 0.9978296023877065, + "rho_max": 0.9998561964891306, + "rho_min": 0.534595094929311, + "time": 2e-05 + }, + "warp": { + "all_finite": true, + "kernel": "gaussian", + "kinetic_energy": 8078.179766857993, + "particles": 204, + "radius_max": 0.9978295868060059, + "rho_max": 0.9998562335968018, + "rho_min": 0.534595251083374, + "time": 1.9999999494757503e-05, + "xsph_eps": 0.5 + } +} +``` + +Comparison outputs: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke-cpu.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke-warp.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png +``` + +Resolved PySPH Application vs Warp comparison, timestep-policy parity: + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-timestep-policy --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +``` + +Case: + +```text +nx=100 +particles=31417 +c0=1400.0 +Gaussian kernel +Tait EOS gamma=7.0 +alpha=0.1 beta=0.0 +XSPH eps=0.5 +density_mode=continuity +warp_timestep_policy=pysph +adaptive timestep cfl=0.3 n_damp=50 +checkpoints: 0.0008, 0.0038 +``` + +Performance: + +| Backend | Wall time (s) | Steps | Average step time (s) | dt_min | dt_mean | dt_max | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| PySPH CPU Application | 233.97314716299297 | 1393 | 0.16796349401507032 | 2.2023818173548364e-06 | 2.7404840979225977e-06 | 2.780917055777183e-06 | +| Warp GPU | 23.629107111992198 | 1393 | 0.016962747388364823 | 2.7459356128852786e-09 | 2.727925340990668e-06 | 2.7813784981844947e-06 | + +Overall wall-time speedup: `9.901903870258701x`. + +Checkpoint metrics: + +| Time | Backend | Major axis | Minor axis | Exact major | Exact minor | rho_min | rho_max | Kinetic energy | +| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 0.0008 | PySPH CPU | 1.0816640490130147 | 0.9220712094421513 | 1.0831034701687434 | 0.9232728243814081 | 0.9995725485493702 | 1.0055542309769985 | 7818.556269923258 | +| 0.0008 | Warp GPU | 1.0816634893417358 | 0.9220717549324036 | 1.0831034701687434 | 0.9232728243814081 | 0.9995719790458679 | 1.0055550336837769 | 7818.5528883068255 | +| 0.0038 | PySPH CPU | 1.4365264918961826 | 0.6964109650387659 | 1.4392190525454083 | 0.6948212631228 | 0.9975631121324761 | 1.002131063362072 | 7797.707446258537 | +| 0.0038 | Warp GPU | 1.4365261793136597 | 0.6964123249053955 | 1.4392190525454083 | 0.6948212631228 | 0.9975622892379761 | 1.0021320581436157 | 7797.707014434469 | + +CPU-vs-Warp deltas: + +| Time | Major axis delta | Minor axis delta | rho_min delta | rho_max delta | KE delta | +| ---: | ---: | ---: | ---: | ---: | ---: | +| 0.0008 | -5.596712788769054e-07 | 5.454902523016614e-07 | -5.695035022457162e-07 | 8.027067783800135e-07 | -0.0033816164323070552 | +| 0.0038 | -3.1258252297661215e-07 | 1.3598666296354978e-06 | -8.228944999855159e-07 | 9.947815438060559e-07 | -0.0004318240680731833 | + +Resolved outputs: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0008000.png +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0038000.png +``` + +Explicit Warp fp32 rerun: + +The runner creates host arrays as `float64`, but `WarpDeviceHelper` casts float +properties to `compyle.config.get_config().use_double`; in this environment the +config is `False`, so the active Warp device arrays are `float32`. The +comparison loaders cast checkpoint arrays to `float64` for metrics/plotting, +which can hide the device precision in the saved summary. + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-fp32-warp-only --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32 --skip-pysph-application --max-steps 10000000 +``` + +Result: + +```text +Warp fp32 wall time: 26.5732471299998 s +steps: 1393 +average step time: 0.019076272167982626 s +dt_min: 2.7459356128852786e-09 +dt_mean: 2.727925340990668e-06 +dt_max: 2.7813784981844947e-06 +all_finite: true at both checkpoints +``` + +Compared with the committed timestep-policy run's Warp timing +(`23.629107111992198` s), this explicit rerun is `1.1245980224328238x` +slower. Since both runs use the same fp32 device path, this is treated as +run-to-run/module-cache variance rather than a precision effect. Relative to +the committed PySPH CPU Application time (`233.97314716299297` s), the explicit +fp32 Warp-only rerun is `8.804838415808415x` faster. + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-summary.json +``` + +Million-particle fixed-step CPU/GPU comparison: + +```text +$ python pysph/examples/elliptical_drop_no_scheme.py --nx 565 --tf 0.000003732778967800475 --timestep 0.0000003732778967800475 --no-adaptive-timestep --n-damp 0 --pfreq 10 --fname million-pysph --directory .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/pysph --logfile '' --quiet +real 57.48 + +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/warp/million-warp.npz +real 7.17 +``` + +Case: + +```text +nx=565 +particles=1,002,885 +steps=10 +fixed dt=3.732778967800475e-07 +tf=3.732778967800475e-06 +c0=1400.0 +Gaussian kernel +Tait EOS gamma=7.0 +alpha=0.1 beta=0.0 +XSPH eps=0.5 +density_mode=continuity +``` + +Performance: + +| Backend | Wall time (s) | Steps | Average step time (s) | +| --- | ---: | ---: | ---: | +| PySPH CPU Application | 57.48 | 10 | 5.747999999999999 | +| Warp GPU | 7.17 | 10 | 0.717 | + +Overall wall-time speedup: `8.01673640167364x`. + +Final-state CPU-vs-Warp deltas: + +| Metric | Delta | +| --- | ---: | +| axis_x_abs | -1.2296967044633789e-07 | +| axis_y_abs | 4.773760275966765e-10 | +| rho_min | -9.119009991565008e-10 | +| rho_max | 1.4501548406542497e-08 | +| kinetic_energy | 8.523681572114583e-06 | + +Both final checkpoints were finite. The comparison is intentionally fixed-step +to make the short ten-step CPU/GPU timing apples-to-apples without adaptive +damping or output-time policy effects. + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/million-cpu-gpu-10step-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/pysph/million-pysph_00010.hdf5 +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/warp/million-warp.npz +``` + +Million-particle fixed-step cache-reuse rerun: + +After changing the Warp continuity-density PEC path to reuse one neighbor +cache per half-stage, the same 1,002,885-particle fixed-step benchmark was +rerun. The PySPH CPU Application baseline did not change, so the comparison +uses the previously recorded CPU wall time. + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/warp/million-warp.npz +real 4.51 +``` + +Performance: + +| Backend / run | Wall time (s) | Steps | Average step time (s) | Speedup vs CPU | +| --- | ---: | ---: | ---: | ---: | +| PySPH CPU Application | 57.48 | 10 | 5.747999999999999 | 1.0 | +| Warp GPU before cache reuse | 7.17 | 10 | 0.717 | 8.01673640167364 | +| Warp GPU after cache reuse | 4.51 | 10 | 0.45099999999999996 | 12.7450110864745 | + +Cache profile: + +| Metric | Before | After | +| --- | ---: | ---: | +| neighbor-cache builds per step | 8 | 2 | +| average neighbors per cache | 44.873 | 44.873 | +| segmented step wall-time range | 0.36-0.46 s | 0.098335-0.138290 s | +| segmented cache-time range | 0.27-0.33 s | 0.036122-0.052126 s | + +The new Warp result is `1.58980044345898x` faster than the previous Warp +million-particle run while keeping the same final metrics to the recorded +precision. + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/million-cpu-gpu-10step-cache-reuse-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/warp/million-warp.npz +``` + +Million-particle fused-equation (code-generation) rerun: + +Per ADR-0003 the four continuity-stage neighbor-loop equations (pressure +gradient, artificial viscosity, continuity, XSPH) are now expressed as +composable `WarpEquation` blocks and fused by a dynamic code generator +(`pysph/base/warp_codegen.py`) into one generated kernel per PEC half-stage. +The same 1,002,885-particle fixed-step benchmark and physics were rerun. The +PySPH CPU Application baseline is unchanged. + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .ai/.../million-cpu-gpu-10step-fused-eqns/warp/million-warp.npz +warm-cache wall samples: 6.04, 5.94, 4.89, 3.77, 4.05 (best 3.77; cold first-compile 6.15) +``` + +The 10-step headline wall is overhead/IO-bound (Warp init + 1M-particle mgrid +creation + 282MB npz write), so the wall is noisy and roughly flat versus the +4.51 s cache-reuse run (best case 3.77 s, `15.25x` versus CPU). The meaningful +gain is per-step, isolated by the segmented profile: + +| Metric | After cache reuse | After equation fusion | +| --- | ---: | ---: | +| equation-kernel launches per step | 8 | 2 | +| equation-kernel time per step | ~0.064-0.088 s | 0.011-0.014 s | +| segmented step wall-time range | 0.098335-0.138290 s | 0.075883-0.097561 s | +| segmented cache-time range | 0.036122-0.052126 s | 0.033743-0.045973 s | + +Equation-kernel time dropped roughly `5-6x` and steady-state step wall about +`25%`; register pressure from the single larger kernel did not reduce +throughput. The neighbor-cache build is now the dominant per-step cost +(~45-50% of step wall). + +Numerical parity (fused vs the prior separate-kernel Warp run): positions, +densities, and pressures are identical to fp32 print precision; kinetic energy +differs by `-6.4e-09`. Versus the recorded CPU baseline the deltas match the +cache-reuse run (`x ~1e-7`, `rho ~1e-8`, `kinetic_energy 8.5e-06`). Both final +states finite. + +Adaptive `nx=100` resolved guard (Warp-only) with the fused path reached +`t=0.0038` in `1393` steps (identical to the committed run), all finite, with +shape deltas `~4.8e-07`, density deltas `~1e-06`, and kinetic-energy delta +`1.45e-04` versus the committed Warp metrics. Because that run is +per-step-compute-bound (1393 steps), the fusion shows in wall time too: the +adaptive path now runs fused(1)+dt_factors(1)=2 traversals per stage instead of +5, and the Warp wall fell from the committed `23.63 s` to `10.83-14.33 s` +(cross-session, same step count). + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/million-cpu-gpu-10step-fused-eqns-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/warp/million-warp.npz +``` + +Million-particle adaptive GPU probe: + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 565 --steps 1 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --adaptive-dt --cfl 0.3 --dt-min 1.0e-10 --dt-max 0.0000003732778967800475 --density-mode continuity --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/full-nx565-gpu/adaptive-one-step-probe.npz +real 4.87 +``` + +The one-step adaptive probe was finite for 1,002,885 particles. A full +`nx=565`, `tf=0.0076` GPU-only run was not started after the user clarified +"no multi hour run". + +Earlier continuity-density-only diagnostic run: + +- Same CPU baseline and physics, but Warp used the old runner policy that capped + `dt_max` to the initial timestep. +- Warp took 1804 steps versus PySPH's 1393. +- Warp `dt_max=2.1090202153573046e-06`, while PySPH grew to + `dt_max=2.780917055777183e-06`. +- This explained the remaining step-count difference after density parity. + +Earlier summation-density diagnostic run: + +- Same CPU baseline and case settings. +- Warp used `density_mode=summation`, while PySPH CPU evolved density using + `ContinuityEquation` through `WCSPHStep`. +- Warp took 4807 steps, with `dt_min=3.664420711313454e-10` and + `dt_mean=7.905138339974642e-07`. +- At `t=0.0008`, Warp density ranged from `0.9505811929702759` to + `1.0439165830612183` versus PySPH's `0.9995725485493702` to + `1.0055542309769985`. +- This explained the earlier 2225/4807-step behavior: the comparison was not + using the same density evolution. + +Ramp runs: + +| Case | Particles | Steps | dt | Time | all_finite | rho_min | rho_max | radius_max | kinetic_energy | +| --- | ---: | ---: | ---: | ---: | --- | ---: | ---: | ---: | ---: | +| nx=12 | 455 | 5 | 1e-05 | 5e-05 | true | 0.6329907178878784 | 0.9999749660491943 | 1.0018477038507216 | 7943.001147793795 | +| nx=16 | 805 | 5 | 1e-05 | 5e-05 | true | 0.6330121159553528 | 0.9999753832817078 | 1.0018193926728525 | 7868.808903639647 | +| nx=16 | 805 | 20 | 5e-06 | 0.0001 | true | 0.6331153512001038 | 1.0000600814819336 | 1.0066060209042353 | 7868.821050761739 | +| nx=24 | 1808 | 10 | 5e-06 | 5e-05 | true | 0.633074939250946 | 0.9999793767929077 | 1.0033569350841507 | 7840.533230601928 | + +Artificial-viscosity ramp check: + +| Case | alpha | beta | Particles | Steps | dt | Time | all_finite | rho_min | rho_max | radius_max | kinetic_energy | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | ---: | ---: | ---: | +| nx=16 | 0.1 | 0.0 | 805 | 5 | 1e-05 | 5e-05 | true | 0.6330116391181946 | 0.9999754428863525 | 1.0018194069173603 | 7868.737673401772 | + +Tait EOS + per-particle sound-speed ramp check: + +| Case | eos | gamma | alpha | beta | Particles | Steps | dt | Time | all_finite | rho_min | rho_max | cs_min | cs_max | radius_max | kinetic_energy | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | +| nx=16 | tait | 7.0 | 0.1 | 0.0 | 805 | 5 | 1e-05 | 5e-05 | true | 0.6329819560050964 | 0.9999754428863525 | 5.072288990020752 | 19.99852752685547 | 1.0018218256790075 | 7868.739071212255 | + +Ramp output files: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-ramp-nx12-steps5.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-ramp-nx16-steps5.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-ramp-nx16-steps20.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-ramp-nx24-steps10.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-avisc-nx16-steps5.npz +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-tait-nx16-steps5.npz +``` + +### Grid-direct neighbor traversal (ADR-0004) + +Per ADR-0004 the WCSPH continuity hot path stops materializing a flat CSR +neighbor list. The fused equation kernel (and the adaptive CFL dt-factors +kernel) walk the uniform-grid cell list directly with the support cutoff +inline, so `build_neighbor_cache_gpu` is never called on the continuity path; +the only spatial index that remains is the cheap cell-list build (`_build_grid`). + +Segmented per-step profile (`nx=565`, 1,002,885 particles, 12 fixed steps, +2 warmup discarded, via `profile_grid_direct_neighbors.py`): + +```text +$ PYTHONPATH=.ai/.../2026-06-16_warp-elliptical-drop-runner \ + python .ai/.../2026-06-16_warp-elliptical-drop-runner/profile_grid_direct_neighbors.py --nx 565 --steps 12 --warmup 2 +flat_cache_builds_total: 0 (was 2 builds/step under the flat fused path) +grid_builds_per_step: 2.0 (cheap cell list; ~0.0004-0.0007 s each) +equation_launches/step: 2 (per-launch 0.023-0.025 s; absorbs the neighbor traversal) +step_wall_s steady: 0.059-0.064 (was 0.076-0.098 flat fused; ~25-35% lower) +kinetic_energy: 7854.1276 (delta vs flat fused -1.99e-06; all_finite True) +``` + +Neighbor work drops from three traversals per half-stage (count + fill to build +the flat list, then one equation read) to one (the equation cutoff walk). The +equation kernel's per-launch time rises because it now does the traversal that +the flat build used to do separately, but eliminating the two build traversals +plus the host readback and the large allocation nets a lower per-step wall. + +Adaptive `nx=100` resolved guard (Warp-only, pysph timestep policy) with the +grid-direct path reached `t=0.0038` in `1393` steps -- identical to the +committed run -- all finite, with shape deltas `~2.4e-07/6.6e-07`, density +deltas `~9e-07`, and a kinetic-energy delta `1.19e-04` (relative `~1.5e-08`) +versus the committed Warp metrics. The Warp wall fell to `7.82 s` (committed +`23.63 s`; prior fused `10.83-14.33 s`), the cache-build removal compounding +over 1393 steps. The grid-direct dt-factors kernel keeps the `rij2>1e-12` inner +guard and adds the support cutoff to reproduce the flat neighbor set exactly, +which is what holds the substep count at `1393`. + +Fresh same-session CPU-vs-Warp headlines (no reused numbers; CPU = +single-threaded PySPH Cython Application via `headline_million_100step.py`/the +resolved harness, Warp = grid-direct on RTX 4060 fp32): + +```text +nx=100 resolved (real PySPH Application vs Warp, adaptive, identical 1393 steps): + CPU 160.10 s (0.1149 s/step) | Warp 6.78 s (0.00487 s/step) | speedup 23.6x +1M particles, 100 fixed steps (n_damp=0, identical dt both sides): + CPU 344.50 s (3.445 s/step) | Warp 8.37 s total (2.39 setup + 5.98 step; 0.0598 s/step) + speedup 41.2x wall / 57.6x per-step | KE rel delta 1.6e-09 | all_finite True +``` + +The 1M / 100-step ratio is the more representative throughput number: at scale +the GPU parallelism dominates, and 100 steps dilute the one-time setup (Warp +setup is ~29% of its 8.37 s; pure stepping is 5.98 s). The earlier `~30x` figure +was an artifact of mixing a fresh Warp wall with a stale CPU baseline; these +same-session ratios supersede it. + +Output: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-cpu-gpu-grid-direct-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-segmented-grid-direct-profile.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/adaptive-nx100-grid-direct-summary.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-headline-speedups.json +``` + +### Production GPU result -- RTX PRO 6000 Blackwell (sm_120) + +Run by @kunalpuri-prediqt on a cloud box (NVIDIA RTX PRO 6000 Blackwell Server +Edition, 95 GiB, sm_120; Warp 1.14 / CUDA Toolkit 12.9 / driver 13.0), same +grid-direct fp32 code. Million-particle, 100 fixed steps, CPU single-threaded +PySPH Application vs Warp: + +```text +CPU 357.43 s total (3.574 s/step), KE 7854.038961 +Warp 1.32 s total (0.454 setup + 0.863 step; 0.008625 s/step), KE 7854.038955 +speedup 271.5x wall / 414.4x per-step | KE rel delta 8.0e-10 | all_finite True +segmented: flat_cache_builds 0; steady step wall ~8.0 ms; equation ~1.6 ms/launch; grid build ~0.2 ms +``` + +Notes: per-step ~8.0 ms vs the RTX 4060 laptop's ~60 ms (~7.4x faster on this +GPU); the single-threaded CPU stays ~3.5 s/step, so the headline jumps from +`41x/57x` (4060) to `271x/414x` (Blackwell). The fused grid kernel cold-compiled +once (38.6 s) then loaded cached (~6 ms) on subsequent runs, confirming the +deterministic-name on-disk kernel cache on a fresh machine. Blackwell (sm_120) +ran with no code/build changes (BUILD.md section 10). + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/blackwell-rtxpro6000-million-100step-summary.json +``` + +### Cross-GPU throughput sweep (`gpu_perf_sweep.py`) + +Per-GPU throughput vs particle count for the grid-direct continuity PEC step +(fp32), collected in `gpu-sweep/` (one JSON per GPU + a comparison `README.md`). +Four full sweeps recorded -- L40S (sm_89), RTX 5090 (sm_120), RTX PRO 6000 +Blackwell (sm_120), B300 SXM6 (sm_103) -- plus the 4060 1M anchor. + +**Headline speedup vs a single CPU core** at 1M (single-threaded PySPH Cython +~3.5 s/step; GPU per-step from the sweep): RTX 4060 ~`59x`, RTX 5090 ~`235x`, +L40S ~`321x`, RTX PRO 6000 ~`426x`, B300 ~`491x` (directly-measured CPU-vs-Warp +pairs corroborate: 4060 `57.6x`, RTX PRO 6000 `414x`). Speedup is ~flat with N +until the GPU knee, then falls. + +Throughput at **1M particles** (particle-steps/s): RTX 4060 `1.68e7` < RTX 5090 +`6.74e7` < L40S `9.19e7` < RTX PRO 6000 `1.22e8` < B300 `1.41e8`. Peak/sustained: +5090 ~`7.3e7`, L40S ~`9.9e7`, and **both Blackwell cards plateau at ~`1.43e8`** +(a likely bandwidth/occupancy ceiling -- the B300's edge is scale: it holds the +peak to 6M, reaches **78.5M particles**, and keeps a higher post-knee plateau). + +Open finding: every card shows a **super-linear knee** whose location is +non-monotonic with VRAM (RTX PRO 6000 95 GiB knees at 6M; B300 268 GiB at 10M; +5090 32 GiB at 10M; L40S 44 GiB at 21M) -- so it is not a capacity ceiling but +an algorithmic/grid effect to profile (grid-build vs equation time). All sweep +points finite. + +**Cost-of-compute lens ($)**: priced by NVIDIA Brev on-demand rates (2026-06-18: +B300 $9.49/hr, RTX PRO 6000 $2.63, L40S $1.06, RTX 5090 $0.78). `$ per billion +particle-steps` = `($/hr) / (throughput x 3600) x 1e9` at 1M: RTX 5090 **$0.0032** +(1.0x) ~= L40S **$0.0032** (1.0x) < RTX PRO 6000 **$0.0060** (1.9x) < B300 +**$0.0187** (5.8x). The cheap cards do the same SPH work for **~6x less money** +than the B300 -- the datacenter cards buy **scale and latency** (78.5M particles +in one box; ~2x faster single step), not cost-per-work. Pick by constraint: +throughput-bound batch -> RTX 5090 / L40S; biggest problem or fastest turnaround +-> B300 / RTX PRO 6000. + +**Two estimate-only lenses** (modeled from existing data; no measured power or +profiler run -- and none planned, so these are the final reported values, each +with its caveat): + +- _Energy-to-solution ($ lens's physical cousin)_: `kJ per billion particle-steps` + = `TDP_W / throughput x 1e6`, from datasheet board power (B300 1400 W, RTX PRO + 6000 600 W, 5090 575 W, L40S 350 W, 4060 115 W). At 1M: L40S **3.8** < RTX PRO + 6000 **4.9** < 4060 **6.9** < 5090 **8.5** < B300 **10.0** kJ. This **breaks the + 5090=L40S dollar tie**: same $0.0032/Gp-step, but the L40S uses **~2.2x fewer + joules** per unit work (350 W @ 9.19e7 vs 575 W @ 6.74e7), so on a power-capped + or owned fleet the L40S wins outright; the B300 is the least energy-efficient + (~2.6x the L40S). Caveat: the step is not FLOP-bound so cards won't pull full + TDP -- this is an upper-bound bracket, not a measured draw. +- _Roofline / memory-bandwidth utilization (analytic)_: `MBU = throughput x ~1.12 + KB/p-step / peak_BW` (B_eff = own state ~60 B + measured avg_neighbors 44.9 x + ~24 B; peaks B300 8 TB/s HBM3E, GDDR7 cards 1.79, L40S 0.864). At 1M: L40S + **~11.9%** > RTX PRO 6000 **~7.6%** > 4060 **~7.0%** > 5090 **~4.2%** > B300 + **~2.0%** of peak. **Partly corrects the earlier "bandwidth/occupancy-bound" + guess**: even this generous upper-bound byte model leaves every card well under + peak (B300 ~2% of 8 TB/s), so the ~1.43e8 Blackwell ceiling is **occupancy / + launch / grid-build bound, NOT a memory wall** -- large untapped headroom, + especially on the big-memory cards; the next perf win is occupancy/launch + tuning, not faster memory. Caveat: model-based whole-step upper bound (ignores + L2 reuse, includes grid-build), robust within ~2-3x; no profiler counter. + +Figures (`gpu-sweep/plot_gpu_sweep.py`, pure matplotlib from the JSONs): + +_Performance lens:_ + +![Speedup vs 1 CPU core @ 1M particles](gpu-sweep/speedup_vs_cpu_1M.png) + +![Throughput vs particle count](gpu-sweep/throughput_vs_particles.png) + +![Per-step wall time vs particle count](gpu-sweep/perstep_vs_particles.png) + +_Cost-of-compute lens ($):_ + +![Cost per billion particle-steps @ 1M](gpu-sweep/cost_per_billion_1M.png) + +![Cost per billion particle-steps vs particle count](gpu-sweep/cost_per_billion_vs_particles.png) + +_Estimate-only lenses (energy + roofline):_ + +![Energy per billion particle-steps @ 1M (TDP estimate)](gpu-sweep/energy_per_gpstep_1M.png) + +![Analytic memory-bandwidth utilization @ 1M](gpu-sweep/mbu_at_1M.png) + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/README.md +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-{l40s,rtx5090,rtxpro6000,b300}.json +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/{throughput_vs_particles,perstep_vs_particles,speedup_vs_cpu_1M,cost_per_billion_1M,cost_per_billion_vs_particles,energy_per_gpstep_1M,mbu_at_1M}.png +``` + +## Interpretation + +The resolved `nx=100` timestep-policy run is now an apples-to-apples +Application-backed comparison for the current prototype. Warp keeps the repeated +state device-authoritative, evolves density through `arho`, follows PySPH's +early `n_damp` timestep growth policy, and matches the PySPH CPU Application's +Application-backed comparison for the current prototype. Warp keeps the repeated +state device-authoritative, evolves density through `arho`, follows PySPH's +early `n_damp` timestep growth policy, and matches the PySPH CPU Application's +step count, shape, density, and kinetic-energy metrics to small +floating-point-scale deltas at both checkpoint times. + +The original summation-density resolved run is retained as diagnostic evidence, +not as a benchmark. It explains why Warp previously took thousands more +iterations: summation-density refreshes caused larger pressure/density +excursions and collapsed the force timestep. Continuity-density staging fixed +the physics mismatch, and PySPH-like timestep policy fixed the remaining step +count mismatch. + +The final resolved run gives exact step-count parity (`1393` CPU and `1393` +Warp steps) and `9.901903870258701x` wall-time speedup on this machine. The +tiny Warp `dt_min` is from an output-time landing step; it no longer affects +subsequent timestep growth. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-summary.json new file mode 100644 index 000000000..04d08d48b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-summary.json @@ -0,0 +1,96 @@ +{ + "case": { + "alpha": 0.1, + "beta": 0.0, + "c0": 1400.0, + "cfl": 0.3, + "dt": 2.1090201168072683e-06, + "gamma": 7.0, + "hdx": 1.3, + "n_damp": 50, + "nx": 100, + "output_times": [ + 0.0008, + 0.0038 + ], + "p0": 0.0, + "rho0": 1.0, + "warp_density_mode": "continuity", + "warp_dt_max": Infinity, + "warp_timestep_policy": "pysph", + "xsph_eps": 0.5 + }, + "hardware": { + "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "processor": "", + "python": "3.14.4", + "warp_device": "cuda:0", + "warp_version": "1.14.0" + }, + "warp": { + "average_step_time_s": 0.019076272167982626, + "backend": "warp", + "checkpoints": { + "t0p0008000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.0816634893417358, + "axis_minor_estimate": 0.9220718145370483, + "axis_x_abs": 0.9220718145370483, + "axis_y_abs": 1.0816634893417358, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.555910543130991e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.0831034701687434, + "exact_minor_axis": 0.9232728243814081, + "kinetic_energy": 7818.5529387218885, + "p_max": 11072.68359375, + "p_min": -836.4510498046875, + "particles": 31417, + "rho_max": 1.0055549144744873, + "rho_min": 0.9995721578598022, + "steps": 313, + "time": 0.0008, + "x_max": 0.9220717549324036, + "x_min": -0.9220718145370483, + "y_max": 1.0816634893417358, + "y_min": -1.0816634893417358 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-warp-t0p0008000.npz" + }, + "t0p0038000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.4365266561508179, + "axis_minor_estimate": 0.6964129209518433, + "axis_x_abs": 0.6964129209518433, + "axis_y_abs": 1.4365266561508179, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.4392190525454083, + "exact_minor_axis": 0.6948212631228, + "kinetic_energy": 7797.707070654135, + "p_max": 4208.27392578125, + "p_min": -4747.02099609375, + "particles": 31417, + "rho_max": 1.002132773399353, + "rho_min": 0.9975613951683044, + "steps": 1393, + "time": 0.0038, + "x_max": 0.6964129209518433, + "x_min": -0.6964129209518433, + "y_max": 1.4365266561508179, + "y_min": -1.4365260601043701 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-warp-t0p0038000.npz" + } + }, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "final_time": 0.0038, + "steps": 1393, + "wall_time_s": 26.5732471299998 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/generate_results_report.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/generate_results_report.py new file mode 100644 index 000000000..1e172f96e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/generate_results_report.py @@ -0,0 +1,215 @@ +#!/usr/bin/env python3 +"""Generate the production results report for the Warp WCSPH backend. + +Orchestrates the two headline comparisons and assembles a single Markdown report +(parity + speedup tables, method, hardware). Intended to be run on a capable GPU +machine (see ``BUILD.md`` for environment + GPU compatibility): + + PYTHONPATH= python generate_results_report.py \ + --out-dir results-report --gpu-label "NVIDIA H100 80GB" + +What it runs (both fresh, same machine -- no reused numbers): + +1. Resolved elliptical drop, nx=100, continuity density, PySPH timestep policy: + the apples-to-apples PySPH CPU Application vs Warp comparison (identical step + count), via ``resolved_elliptical_drop_comparison.py``. Gives parity at the + output checkpoints + wall-time speedup. +2. Million-particle (nx=565) 100 fixed steps: CPU PySPH Application vs grid-direct + Warp, via ``headline_million_100step.py``. Gives throughput speedup. + +Optionally (``--resolved-tf``) a longer resolved run to a larger final time for a +fuller trajectory comparison -- this is the multi-hour CPU run best done on the +capable machine. + +Use ``--quick`` first to validate the pipeline end-to-end at tiny resolution +before committing to the full (CPU-heavy) runs. + +The CPU baseline is the real single-threaded PySPH Cython Application +(``pysph/examples/elliptical_drop_no_scheme.py``); Warp runs the grid-direct +WCSPH path. Both honor the same physics (Gaussian kernel, Tait EOS, continuity +density, radius_scale=3). The Warp device path is fp32 (compyle use_double=False). +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +REPO_ROOT = HERE.parents[4] + + +def _run(cmd, env_pythonpath=None): + import os + env = dict(os.environ) + if env_pythonpath: + env['PYTHONPATH'] = env_pythonpath + os.pathsep + env.get('PYTHONPATH', '') + print('+ ' + ' '.join(str(c) for c in cmd), flush=True) + proc = subprocess.run(cmd, cwd=str(REPO_ROOT), env=env, text=True) + if proc.returncode != 0: + raise SystemExit('command failed (%d): %s' % (proc.returncode, cmd)) + + +def run_resolved(out_dir, nx, tf): + prefix = 'report-resolved-nx%d' % nx + cmd = [ + sys.executable, str(HERE / 'resolved_elliptical_drop_comparison.py'), + '--nx', str(nx), + '--warp-timestep-policy', 'pysph', '--warp-density-mode', 'continuity', + '--output-dir', str(out_dir), '--prefix', prefix, + ] + if tf is not None: + cmd += ['--output-times', tf] + _run(cmd, env_pythonpath=str(HERE)) + matches = sorted(out_dir.glob(prefix + '*summary*.json')) + return json.load(open(matches[-1])) if matches else None + + +def run_million(out_dir, nx, steps): + out_json = out_dir / ('report-million-nx%d-%dstep.json' % (nx, steps)) + cmd = [ + sys.executable, str(HERE / 'headline_million_100step.py'), + '--nx', str(nx), '--steps', str(steps), + '--out-dir', str(out_dir / 'million-work'), + '--output', str(out_json), + ] + _run(cmd, env_pythonpath=str(HERE)) + return json.load(open(out_json)) if out_json.exists() else None + + +def _fmt(v, nd=3): + try: + return ('%.{}g'.format(nd)) % float(v) + except (TypeError, ValueError): + return str(v) + + +def build_report(gpu_label, resolved, million): + L = [] + L.append('# Warp WCSPH backend -- production results report') + L.append('') + L.append('- GPU: **%s**' % gpu_label) + L.append('- CPU baseline: single-threaded PySPH Cython Application ' + '(`pysph/examples/elliptical_drop_no_scheme.py`)') + L.append('- Warp path: grid-direct WCSPH (fp32; compyle `use_double=False`)') + L.append('- Physics: Gaussian kernel, Tait EOS, continuity density, ' + 'radius_scale=3') + L.append('') + + if resolved is not None: + cpu = resolved.get('cpu', {}) + warp = resolved.get('warp', {}) + perf = resolved.get('performance', {}) + L.append('## 1. Resolved elliptical drop (nx=%d) -- apples-to-apples' + % resolved.get('case', {}).get('nx', '?')) + L.append('') + L.append('Real PySPH CPU Application vs Warp, identical adaptive ' + 'timestep policy and step count.') + L.append('') + L.append('| | steps | wall (s) | s/step |') + L.append('|---|---:|---:|---:|') + L.append('| CPU PySPH Application | %s | %s | %s |' % ( + cpu.get('steps'), _fmt(cpu.get('wall_time_s')), + _fmt(cpu.get('average_step_time_s'), 4))) + L.append('| Warp (grid-direct) | %s | %s | %s |' % ( + warp.get('steps'), _fmt(warp.get('wall_time_s')), + _fmt(warp.get('average_step_time_s'), 4))) + L.append('') + L.append('**Speedup (wall): %sx**' % _fmt(perf.get('speedup_wall_time'))) + L.append('') + comps = resolved.get('comparisons', {}) + if comps: + L.append('Final-state deltas (Warp vs CPU) at checkpoints:') + L.append('') + L.append('| checkpoint | KE delta | axis_major | axis_minor | ' + 'rho_min | rho_max |') + L.append('|---|---:|---:|---:|---:|---:|') + for label, c in sorted(comps.items()): + d = c.get('deltas', {}) + L.append('| %s | %s | %s | %s | %s | %s |' % ( + label, _fmt(d.get('kinetic_energy'), 3), + _fmt(d.get('axis_major_estimate'), 3), + _fmt(d.get('axis_minor_estimate'), 3), + _fmt(d.get('rho_min'), 3), _fmt(d.get('rho_max'), 3))) + L.append('') + + if million is not None: + warp = million.get('warp', {}) + cpu = million.get('cpu', {}) + L.append('## 2. Million particles (nx=%d, %d fixed steps) -- throughput' + % (million.get('case', {}).get('nx', '?'), + million.get('case', {}).get('steps', '?'))) + L.append('') + L.append('| | particles | total wall (s) | s/step |') + L.append('|---|---:|---:|---:|') + L.append('| CPU PySPH Application | %s | %s | %s |' % ( + cpu.get('particles'), _fmt(cpu.get('total_s')), + _fmt(cpu.get('per_step_s'), 4))) + L.append('| Warp (grid-direct) | %s | %s | %s |' % ( + warp.get('particles'), _fmt(warp.get('total_s')), + _fmt(warp.get('per_step_s'), 4))) + L.append('') + L.append('**Speedup: %sx wall / %sx per-step.** KE relative delta %s; ' + 'all finite: %s.' % ( + _fmt(million.get('speedup_total_wall')), + _fmt(million.get('speedup_per_step')), + _fmt(million.get('kinetic_energy_rel_delta'), 2), + warp.get('all_finite'))) + L.append('') + + L.append('## Method notes') + L.append('') + L.append('- No reused numbers: both CPU and Warp are measured fresh on this ' + 'machine in this run.') + L.append('- CPU is single-threaded PySPH Cython; quote that framing when ' + 'reporting the speedup.') + L.append('- The million-particle run uses fixed timesteps (`--no-adaptive-' + 'timestep --n-damp 0`) so both sides do identical work.') + L.append('- Warp first-run cold compile of the fused grid kernel is a ' + 'one-time per-machine cost (deterministic-name disk cache).') + L.append('') + return '\n'.join(L) + '\n' + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--out-dir', default='results-report') + parser.add_argument('--gpu-label', default='(unspecified GPU)') + parser.add_argument('--resolved-nx', type=int, default=100) + parser.add_argument('--resolved-tf', default=None, + help="output-times for a longer resolved run, e.g. " + "'0.0008,0.0038' (default) or a larger final time") + parser.add_argument('--million-nx', type=int, default=565) + parser.add_argument('--million-steps', type=int, default=100) + parser.add_argument('--skip-resolved', action='store_true') + parser.add_argument('--skip-million', action='store_true') + parser.add_argument('--quick', action='store_true', + help='tiny smoke run to validate the pipeline') + args = parser.parse_args() + + out_dir = Path(args.out_dir).resolve() + out_dir.mkdir(parents=True, exist_ok=True) + + resolved_nx = 20 if args.quick else args.resolved_nx + million_nx = 30 if args.quick else args.million_nx + million_steps = 5 if args.quick else args.million_steps + + resolved = None + if not args.skip_resolved: + resolved = run_resolved(out_dir, resolved_nx, args.resolved_tf) + million = None + if not args.skip_million: + million = run_million(out_dir, million_nx, million_steps) + + report = build_report(args.gpu_label, resolved, million) + report_path = out_dir / 'RESULTS_REPORT.md' + report_path.write_text(report) + print('\n=== wrote %s ===\n' % report_path) + print(report) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/README.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/README.md new file mode 100644 index 000000000..ab19743a8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/README.md @@ -0,0 +1,289 @@ +# Cross-GPU performance sweep -- Warp grid-direct WCSPH + +Per-GPU throughput sweeps for the grid-direct continuity-density PEC step (fp32), +produced by `../gpu_perf_sweep.py`. Run the script on each GPU and drop its JSON +here; this doc collects the curves and a cross-GPU comparison. + +**Metric:** `throughput = particles / per_step_median` (particle-steps per +second), the steady warm per-step over the elliptical-drop workload. Physics: +Gaussian kernel, Tait EOS, continuity density, radius_scale=3, fixed dt, fp32 +(`compyle use_double=False`). nx maps to particle count via the disk fill +(`particles ~ pi*nx^2`). + +## GPUs collected + +| GPU | arch | VRAM | data | file | +|---|---|---:|---|---| +| NVIDIA L40S | sm_89 (Ada) | 48 GiB | full sweep (31k-21M) | `sweep-l40s.json` | +| NVIDIA RTX 5090 | sm_120 (Blackwell consumer) | 32 GiB | full sweep (31k-21M) | `sweep-rtx5090.json` | +| NVIDIA RTX PRO 6000 Blackwell | sm_120 (Blackwell workstation) | 96 GiB | full sweep (31k-40M) | `sweep-rtxpro6000.json` | +| NVIDIA B300 SXM6 | sm_103 (Blackwell Ultra) | 268 GiB | full sweep (31k-78M) | `sweep-b300.json` | +| NVIDIA RTX 4060 Laptop | sm_89 (Ada) | 8 GiB | 1M point only* | `../million-cpu-gpu-grid-direct/fresh-headline-speedups.json` | + +\* full `gpu_perf_sweep.py` runs for Blackwell and the 4060 are pending; only the +single 1M-particle point is on record for those so far. + +## Figures + +Generated from the JSONs by `plot_gpu_sweep.py` (pure matplotlib, no GPU). + +**Speedup vs a single CPU core @ 1M particles** -- up to ~491x (B300): + +![speedup vs 1 CPU core at 1M](speedup_vs_cpu_1M.png) + +**Throughput vs particle count** -- the ramp, the ~1.43e8 Blackwell ceiling, and +the per-GPU super-linear knees: + +![throughput vs particles](throughput_vs_particles.png) + +**Per-step wall time vs particle count** (log-log) -- linear scaling up to each +card's knee: + +![per-step vs particles](perstep_vs_particles.png) + +### Cost-of-compute lens ($) + +The figures above measure *speed*; these measure *price of the work done*, using +the NVIDIA Brev on-demand hourly rates (USD/hr, 2026-06-18): B300 $9.49, RTX PRO +6000 $2.63, L40S $1.06, RTX 5090 $0.78. The metric is **$ per billion +particle-steps** = `($/hr) / (throughput x 3600) x 1e9` -- lower is cheaper. The +4060 is a laptop GPU (no cloud rate) and is excluded. + +**Cost of compute @ 1M particles** -- the cheap cards win the $/work race; B300 is +~6x the cost per unit work: + +![cost per billion particle-steps at 1M](cost_per_billion_1M.png) + +**Cost of compute vs particle count** -- cheapest in each card's plateau, then +rises sharply past its knee (you pay for idle silicon once throughput drops): + +![cost per billion particle-steps vs particles](cost_per_billion_vs_particles.png) + +### Energy-to-solution lens (estimate -- board TDP, not measured) + +> **Estimate, not a measurement.** We did not log GPU power, and there will be no +> profiled re-run, so this lens is **modeled** from each card's datasheet board +> power (TDP) and the recorded throughput. The continuity PEC step is *not* +> FLOP-bound, so the cards will not actually pull full TDP -- this **overstates** +> the energy of the high-TDP idle-headroom parts and should be read as an +> upper-bound *bracket* that orders the cards, not an exact joule count. + +Metric: **kJ per billion particle-steps** = `TDP_W / throughput x 1e6` +(equivalently energy-to-solution); its reciprocal is **particle-steps per watt**. +Board power used (datasheet): B300 SXM6 1400 W, RTX PRO 6000 600 W, RTX 5090 +575 W, L40S 350 W, RTX 4060 Laptop 115 W. + +![energy per billion particle-steps at 1M (TDP estimate)](energy_per_gpstep_1M.png) + +| GPU | TDP (W) | throughput @1M | kJ / billion p-steps | particle-steps / W | vs best | +|---|---:|---:|---:|---:|---:| +| L40S | 350 | 9.19e7 | **3.8** | 2.63e5 | 1.0x | +| RTX PRO 6000 Blackwell | 600 | 1.22e8 | **4.9** | 2.03e5 | 1.3x | +| RTX 4060 Laptop | 115 | 1.68e7 | **6.9** | 1.46e5 | 1.8x | +| RTX 5090 | 575 | 6.74e7 | **8.5** | 1.17e5 | 2.2x | +| B300 SXM6 | 1400 | 1.41e8 | **10.0** | 1.00e5 | 2.6x | + +**Why this lens matters: it breaks the 5090 = L40S dollar tie.** On rental cost +both sit at $0.0032 per billion particle-steps, but on energy the L40S does the +same SPH work for **~2.2x fewer joules** than the 5090 (350 W at 9.19e7 vs 575 W +at 6.74e7) -- so on a power-capped or *owned* fleet (where you pay the power bill, +not the cloud margin) the L40S is the clear pick. The datacenter B300 is the +*least* energy-efficient per unit work (~2.6x the L40S): it spends ~1.4 kW to hold +the same ~1.43e8 ceiling the 600 W RTX PRO 6000 reaches. Cloud $/hr hides this +because it bundles the provider's power, cooling, and margin into one number. + +### Roofline lens: memory-bandwidth utilization (analytic estimate) + +> **Analytic estimate, not a profiler counter.** With no Nsight/`ncu` run, we +> model the achieved DRAM bandwidth as `throughput x B_eff`, where `B_eff ~= 1.12 +> KB per particle-step` (own state read+write ~60 B + the measured `avg_neighbors +> = 44.9` x ~24 B per neighbor read). This counts *logical* reads and ignores L2 +> reuse, so it is an **upper bound** on true DRAM traffic -- the real utilization +> is at most this and likely lower. It is also a *whole-step* number (includes the +> grid build and launch overhead, not just the neighbor gather). Treat the +> percentages as an order-of-magnitude bracket, robust to the byte model within +> ~2-3x. + +Metric: **MBU** = `achieved_BW / peak_BW`. Datasheet peak DRAM bandwidth: B300 +HBM3E ~8 TB/s, RTX 5090 / RTX PRO 6000 GDDR7 ~1.79 TB/s, L40S GDDR6 ~0.864 TB/s, +RTX 4060 ~0.27 TB/s. + +![analytic memory-bandwidth utilization at 1M](mbu_at_1M.png) + +| GPU | peak BW | throughput @1M | achieved BW (est.) | MBU (est.) | +|---|---:|---:|---:|---:| +| L40S | 0.864 TB/s | 9.19e7 | ~103 GB/s | **~11.9%** | +| RTX PRO 6000 Blackwell | 1.79 TB/s | 1.22e8 | ~137 GB/s | **~7.6%** | +| RTX 4060 Laptop | 0.27 TB/s | 1.68e7 | ~19 GB/s | **~7.0%** | +| RTX 5090 | 1.79 TB/s | 6.74e7 | ~75 GB/s | **~4.2%** | +| B300 SXM6 | 8.0 TB/s | 1.41e8 | ~157 GB/s | **~2.0%** | + +**This sharpens (and partly corrects) the earlier "bandwidth/occupancy-bound" +guess.** Even with the *generous* upper-bound byte model, no card exceeds ~12% of +its peak DRAM bandwidth, and a more realistic multi-pass byte count (~2-3x) still +leaves every card well under ~30%. So the ~1.43e8 Blackwell ceiling is **not a +memory-bandwidth wall** -- it is **occupancy / launch / grid-build bound**, with +large untapped memory headroom. The B300 is the extreme case: it holds the same +peak as the 600 W RTX PRO 6000 while sitting at only **~2% of its 8 TB/s HBM3E**, +i.e. its flat plateau and poor $/work are an *un-tuned-kernel* artifact, not an +intrinsic hardware verdict. This is the single most actionable signal in the +sweep: the next perf win is occupancy/launch tuning (and the super-linear knee is +most likely a grid-build / cache-residency effect), not faster memory. + +> Caveat shared by both lenses: these are *single-machine, model-based* estimates +> meant to order the cards and frame the next optimization -- not validated +> against measured power or profiler counters. + +## Full sweep -- NVIDIA L40S (sm_89, 44 GiB usable) + +| nx | particles | per-step (s) | throughput (particle-steps/s) | finite | +|---:|---:|---:|---:|:--:| +| 100 | 31,417 | 0.001509 | 2.08e7 | yes | +| 200 | 125,629 | 0.002607 | 4.82e7 | yes | +| 400 | 502,625 | 0.006209 | 8.10e7 | yes | +| 565 | 1,002,885 | 0.010915 | 9.19e7 | yes | +| 1000 | 3,141,549 | 0.032157 | 9.77e7 | yes | +| 1400 | 6,157,477 | 0.063098 | 9.76e7 | yes | +| 1800 | 10,178,545 | 0.102492 | 9.93e7 | yes | +| 2600 | 21,236,953 | 0.356544 | 5.96e7 | yes | + +- Throughput ramps with particle count (better GPU saturation) and **plateaus at + ~9.8e7 particle-steps/s from ~1M to ~10M**, with per-step scaling roughly + linearly (1M -> 10M is ~9.4x particles for ~9.4x time). +- At **21M** throughput drops to 5.96e7 (per-step 3.5x higher for 2.1x more + particles) -- a super-linear knee, not OOM (44 GiB has headroom). Likely grid + build / cache-locality at very large N; worth profiling later. +- All points finite. Cold compile on this fresh box was ~89 s + (warp_nnps 20.9 s + the generated grid kernel 66.2 s), then disk-cached. + +## Full sweep -- NVIDIA RTX 5090 (sm_120 consumer Blackwell, 31 GiB) + +| nx | particles | per-step (s) | throughput (particle-steps/s) | finite | +|---:|---:|---:|---:|:--:| +| 100 | 31,417 | 0.002473 | 1.27e7 | yes | +| 200 | 125,629 | 0.003915 | 3.21e7 | yes | +| 400 | 502,625 | 0.009707 | 5.18e7 | yes | +| 565 | 1,002,885 | 0.014879 | 6.74e7 | yes | +| 1000 | 3,141,549 | 0.043096 | 7.29e7 | yes | +| 1400 | 6,157,477 | 0.084586 | 7.28e7 | yes | +| 1800 | 10,178,545 | 0.221873 | 4.59e7 | yes | +| 2600 | 21,236,953 | 0.511236 | 4.15e7 | yes | + +- Plateaus lower (**~7.3e7** from ~1M to ~6M) than the L40S, and the + super-linear knee comes **earlier, at ~10M** (vs the L40S's 21M) -- consistent + with the 5090's smaller 31 GiB (capacity pressure sooner) and consumer-card + power/clock limits. Same `sm_120` arch as the workstation Blackwell, but + capacity/bandwidth-shaped throughput on this fp32 SPH workload. + +## Full sweep -- NVIDIA RTX PRO 6000 Blackwell (sm_120 workstation, 95 GiB) + +| nx | particles | per-step (s) | throughput (particle-steps/s) | finite | +|---:|---:|---:|---:|:--:| +| 100 | 31,417 | 0.001683 | 1.87e7 | yes | +| 200 | 125,629 | 0.002368 | 5.30e7 | yes | +| 400 | 502,625 | 0.005003 | 1.00e8 | yes | +| 565 | 1,002,885 | 0.008223 | 1.22e8 | yes | +| 1000 | 3,141,549 | 0.021753 | **1.44e8** | yes | +| 1400 | 6,157,477 | 0.098554 | 6.25e7 | yes | +| 1800 | 10,178,545 | 0.163877 | 6.21e7 | yes | +| 2600 | 21,236,953 | 0.396781 | 5.35e7 | yes | +| 3600 | 40,714,821 | 0.790790 | 5.15e7 | yes | + +Peak **1.44e8** at 3M, then a sharp knee at 6M (per-step 0.022 -> 0.099 s). + +## Full sweep -- NVIDIA B300 SXM6 (sm_103 Blackwell Ultra, 268 GiB) + +| nx | particles | per-step (s) | throughput (particle-steps/s) | finite | +|---:|---:|---:|---:|:--:| +| 100 | 31,417 | 0.001025 | 3.07e7 | yes | +| 200 | 125,629 | 0.001594 | 7.88e7 | yes | +| 400 | 502,625 | 0.003614 | 1.39e8 | yes | +| 565 | 1,002,885 | 0.007132 | 1.41e8 | yes | +| 1000 | 3,141,549 | 0.022015 | **1.43e8** | yes | +| 1400 | 6,157,477 | 0.043366 | 1.42e8 | yes | +| 1800 | 10,178,545 | 0.128046 | 7.95e7 | yes | +| 2600 | 21,236,953 | 0.305089 | 6.96e7 | yes | +| 3600 | 40,714,821 | 0.583046 | 6.98e7 | yes | +| 5000 | 78,539,677 | 1.120131 | 7.01e7 | yes | + +Holds the **~1.43e8 plateau from ~0.5M to 6M**, knee at 10M, then a stable +~7.0e7 post-knee plateau out to **78.5M particles** (largest run; ~1.12 s/step). + +## Cross-GPU comparison + +At **1M particles (nx=565)** -- the common anchor. + +Headline speedup is **vs a single CPU core**: the single-threaded PySPH Cython +Application runs this step at **~3.5 s/step at 1M** (measured 3.445 s on the +4060 host, 3.574 s on the Blackwell host -- same workload, host-CPU dependent). +The speedup column below uses CPU = 3.5 s/step; the GPU-vs-GPU column is relative +to the 4060. + +| GPU | arch | VRAM | per-step (s) | throughput (p-steps/s) | **vs 1 CPU core** | vs RTX 4060 | +|---|---|---:|---:|---:|---:|---:| +| RTX 4060 Laptop | sm_89 | 8 GiB | 0.0598 | 1.68e7 | **~59x** | 1.0x | +| RTX 5090 | sm_120 | 32 GiB | 0.014879 | 6.74e7 | **~235x** | 4.0x | +| L40S | sm_89 | 48 GiB | 0.010915 | 9.19e7 | **~321x** | 5.5x | +| RTX PRO 6000 Blackwell | sm_120 | 96 GiB | 0.008223 | 1.22e8 | **~426x** | 7.3x | +| B300 SXM6 | sm_103 | 268 GiB | 0.007132 | 1.41e8 | **~491x** | 8.4x | + +Directly-measured CPU-vs-Warp pairs (from `headline_million_100step.py`, fresh +on the same host) corroborate the column: 4060 **57.6x** (CPU 3.445 / Warp +0.0598) and RTX PRO 6000 Blackwell **414x** (CPU 3.574 / Warp 0.008625, its +100-step fixed run). The single-thread CPU step is O(N) like the GPU, so the +speedup is roughly flat with particle count **until the GPU knee**, past which it +falls (e.g. B300 at 10M: CPU ~35 s/step vs Warp 0.128 s -> ~273x). + +### Cost of compute at 1M ($ lens) + +Same 1M anchor, priced by NVIDIA Brev on-demand rates (2026-06-18). `$ per +billion particle-steps` = `($/hr) / (throughput x 3600) x 1e9` -- the dollar cost +of the actual numerical work, independent of wall-clock. + +| GPU | $/hr (Brev) | throughput @1M (p-steps/s) | $ per billion particle-steps | vs cheapest | +|---|---:|---:|---:|---:| +| RTX 5090 | $0.78 | 6.74e7 | **$0.0032** | 1.0x | +| L40S | $1.06 | 9.19e7 | **$0.0032** | 1.0x | +| RTX PRO 6000 Blackwell | $2.63 | 1.22e8 | **$0.0060** | 1.9x | +| B300 SXM6 | $9.49 | 1.41e8 | **$0.0187** | 5.8x | + +The two cheap cards (5090 / L40S) do the same SPH work for **~6x less money** +than the B300, and ~3x less than the RTX PRO 6000. The expensive datacenter cards +do **not** win on cost-per-work on this fp32 SPH step -- their value is **scale +and latency**: the B300 fits 78.5M particles in one box and finishes any single +step ~2x faster than the 5090, but you pay a large premium for that wall-clock. +**Pick by constraint:** lowest $/work for throughput-bound batch jobs -> RTX 5090 +/ L40S; largest single problem or fastest turnaround -> B300 / RTX PRO 6000. + +Peak / sustained throughput and the super-linear knee: + +| GPU | peak throughput | peak at | knee at | post-knee plateau | max run | +|---|---:|---:|---:|---:|---:| +| RTX 5090 | ~7.3e7 | 3M | 10M | ~4.2e7 | 21M | +| L40S | ~9.9e7 | 10M | 21M | -- | 21M | +| RTX PRO 6000 | ~1.44e8 | 3M | 6M | ~5.2e7 | 41M | +| B300 SXM6 | ~1.43e8 | 1M-6M | 10M | ~7.0e7 | 78M | + +Observations (`*` = open question): +- **Both Blackwell cards hit the same peak ceiling (~1.43e8)** despite very + different class/VRAM -- not raw-FLOP-bound. The analytic roofline lens above + refines the earlier "bandwidth/occupancy-bound" guess: every card sits well + under peak DRAM bandwidth (B300 ~2% of 8 TB/s at the plateau), so the ceiling + is **occupancy / launch / grid-build bound, not a memory-bandwidth wall** -- + there is large untapped headroom, especially on the big-memory cards. The + **B300's edge is scale**: it sustains the peak to 6M, reaches **78.5M + particles**, and holds a higher post-knee plateau (~7.0e7 vs RTX PRO 6000 + ~5.2e7, 5090 ~4.2e7). +- On this fp32 SPH workload the **datacenter Ada L40S (9.19e7) beats the consumer + 5090 (6.74e7)** at 1M -- capacity/bandwidth/occupancy shaped, not FLOP shaped. +- `*` The **super-linear knee is non-monotonic with VRAM**: RTX PRO 6000 (95 GiB) + knees at 6M, B300 (268 GiB) at 10M, 5090 (32 GiB) at 10M, L40S (44 GiB) at + 21M. So it is **not** a capacity ceiling -- likely an algorithmic/grid effect + (cell-list build cost, occupancy, or Warp mempool behavior at scale). The + segmented profiler (`profile_grid_direct_neighbors.py`: grid-build vs equation + time) is the tool to diagnose it; treat the cause as unconfirmed. +- arch note: B300 reports **sm_103** (Blackwell Ultra) -- distinct from + datacenter Blackwell sm_100 and consumer/workstation sm_120; all ran on Warp + 1.14 / CUDA Toolkit 12.9 with no code or build changes (BUILD.md section 10). +- The 4060 1M point is from the fixed-step headline run, not a `gpu_perf_sweep.py` + sweep (close enough for the anchor; a full 4060 sweep would round out the low end). diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_1M.png new file mode 100644 index 000000000..d0899df3f Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_vs_particles.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_vs_particles.png new file mode 100644 index 000000000..2d591527a Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/cost_per_billion_vs_particles.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/energy_per_gpstep_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/energy_per_gpstep_1M.png new file mode 100644 index 000000000..5779505d1 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/energy_per_gpstep_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/mbu_at_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/mbu_at_1M.png new file mode 100644 index 000000000..a5ed1deb9 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/mbu_at_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/perstep_vs_particles.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/perstep_vs_particles.png new file mode 100644 index 000000000..a614bad16 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/perstep_vs_particles.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/plot_gpu_sweep.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/plot_gpu_sweep.py new file mode 100644 index 000000000..44fd31598 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/plot_gpu_sweep.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python3 +"""Plot the cross-GPU sweep. Reads gpu-sweep/sweep-*.json and writes PNGs here. + +Pure data plotting -- no GPU required; just matplotlib. Run: + + python .ai/.../2026-06-16_warp-elliptical-drop-runner/gpu-sweep/plot_gpu_sweep.py + +Figures (performance lens): +- throughput_vs_particles.png : throughput vs particle count (the headline curve) +- perstep_vs_particles.png : per-step wall time vs particle count (log-log) +- speedup_vs_cpu_1M.png : speedup vs a single CPU core at 1M particles + +Figures ($ cost-of-compute lens; Brev hourly pricing in BREV_COST_PER_HR): +- cost_per_billion_1M.png : $ per billion particle-steps at 1M (bar) +- cost_per_billion_vs_particles.png : $ per billion particle-steps vs count + +Figures (estimate-only lenses; modeled from datasheet TDP + a byte model, no +measured power/profiler data -- there will be no profiled re-run): +- energy_per_gpstep_1M.png : kJ per billion particle-steps at 1M (TDP estimate) +- mbu_at_1M.png : analytic memory-bandwidth utilization at 1M +""" + +import glob +import json +import os + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +HERE = os.path.dirname(os.path.abspath(__file__)) + +# Single-threaded PySPH Cython Application at 1M particles (continuity PEC step): +# ~3.5 s/step (measured 3.445 s on the 4060 host, 3.574 s on the Blackwell host). +CPU_REF_S_PER_STEP_AT_1M = 3.5 +ONE_MILLION = 1002885 + +# NVIDIA Brev on-demand hourly rates (USD/hr), as of 2026-06-18. The RTX 4060 is +# a laptop GPU (no cloud rate) and is excluded from cost plots. +BREV_COST_PER_HR = { + 'B300': 9.49, + 'RTX PRO 6000': 2.63, + 'L40S': 1.06, + 'RTX 5090': 0.78, +} + + +def cost_per_hr(name): + for key, val in BREV_COST_PER_HR.items(): + if key in name: + return val + return None + + +# --- Estimate-only lenses (no measured power/profiler data; modeled from +# --- datasheet board power and a byte-traffic model). These are ESTIMATES: +# --- there will be no profiled re-run, so they are reported with their caveats. + +# Datasheet board power / TDP (W). The continuity PEC step is NOT FLOP-bound, so +# the cards will not actually pull full TDP -- these overstate the energy of the +# high-TDP idle-headroom parts and are an upper-bound bracket, not a measurement. +BOARD_TDP_W = { + 'B300': 1400.0, # SXM6 module nameplate (Blackwell Ultra) + 'RTX PRO 6000': 600.0, # workstation/server max + 'RTX 5090': 575.0, # FE TDP + 'L40S': 350.0, + 'RTX 4060': 115.0, # laptop part, max-perf config +} + +# Datasheet peak DRAM bandwidth (TB/s). +PEAK_BW_TBS = { + 'B300': 8.0, # HBM3E + 'RTX PRO 6000': 1.79, # GDDR7 + 'RTX 5090': 1.79, # GDDR7 + 'L40S': 0.864, # GDDR6 + 'RTX 4060': 0.27, # laptop GDDR6 +} + +# Effective DRAM traffic per particle-step (bytes), analytic model: +# own-state read+write (~60 B) + avg_neighbors(=44.9, measured) x ~24 B per +# neighbor read. ~1.12 KB. This counts logical reads and IGNORES L2 reuse, so +# achieved-BW from it is an UPPER bound on true DRAM traffic -> MBU below is an +# UPPER bound on true utilization (a generous multi-pass count would be ~2-3x +# larger, still well under peak on every card). +B_EFF_BYTES = 1120.0 + + +def _lookup(table, name): + for key, val in table.items(): + if key in name: + return val + return None + + +def energy_kj_per_gpstep(throughput, tdp_w): + """Estimated kJ to compute 1e9 particle-steps at board TDP.""" + return tdp_w / throughput * 1e6 + + +def perf_per_watt(throughput, tdp_w): + """particle-steps/s per watt of board TDP.""" + return throughput / tdp_w + + +def mbu_frac(throughput, peak_bw_tbs): + """Analytic memory-bandwidth utilization (fraction of peak DRAM BW).""" + return throughput * B_EFF_BYTES / (peak_bw_tbs * 1e12) + +# RTX 4060: single 1M point only (fixed-step headline run, not a full sweep). +ANCHOR_4060 = { + 'name': 'RTX 4060 Laptop (sm_89)', + 'points': [(ONE_MILLION, 0.0598, 1.677e7)], +} + + +def _label(hw): + name = hw['name'].replace('NVIDIA ', '').replace(' Server Edition', '') + return '%s (%s)' % (name, hw['arch']) + + +def load_gpus(): + gpus = [] + for f in sorted(glob.glob(os.path.join(HERE, 'sweep-*.json'))): + d = json.load(open(f)) + pts = [(r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s']) + for r in d['results'] if 'error' not in r] + gpus.append({'name': _label(d['hardware']), 'points': sorted(pts)}) + gpus.append(ANCHOR_4060) + # order by throughput at the largest common point (nicer legend ordering) + gpus.sort(key=lambda g: g['points'][-1][2]) + return gpus + + +def plot_throughput(gpus): + fig, ax = plt.subplots(figsize=(8, 5)) + for g in gpus: + xs = [p[0] for p in g['points']] + ys = [p[2] / 1e6 for p in g['points']] # million particle-steps/s + style = 'o' if len(xs) == 1 else 'o-' + ax.plot(xs, ys, style, label=g['name'], markersize=5) + ax.set_xscale('log') + ax.set_xlabel('particles') + ax.set_ylabel('throughput (million particle-steps / s)') + ax.set_title('Warp grid-direct WCSPH -- throughput vs particle count (fp32)') + ax.grid(True, which='both', alpha=0.3) + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'throughput_vs_particles.png'), dpi=130) + plt.close(fig) + + +def plot_perstep(gpus): + fig, ax = plt.subplots(figsize=(8, 5)) + for g in gpus: + xs = [p[0] for p in g['points']] + ys = [p[1] for p in g['points']] + style = 'o' if len(xs) == 1 else 'o-' + ax.plot(xs, ys, style, label=g['name'], markersize=5) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_xlabel('particles') + ax.set_ylabel('per-step wall time (s)') + ax.set_title('Warp grid-direct WCSPH -- per-step time vs particle count') + ax.grid(True, which='both', alpha=0.3) + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'perstep_vs_particles.png'), dpi=130) + plt.close(fig) + + +def plot_speedup(gpus): + rows = [] + for g in gpus: + at1m = [p for p in g['points'] if p[0] == ONE_MILLION] + if at1m: + rows.append((g['name'], CPU_REF_S_PER_STEP_AT_1M / at1m[0][1])) + rows.sort(key=lambda r: r[1]) # ascending -> largest bar at top + names = [r[0] for r in rows] + speedups = [r[1] for r in rows] + fig, ax = plt.subplots(figsize=(8.5, 5)) + bars = ax.barh(names, speedups, color='tab:green', alpha=0.85) + for b, s in zip(bars, speedups): + ax.text(b.get_width(), b.get_y() + b.get_height() / 2, + ' %.0fx' % s, va='center', fontsize=9) + ax.set_xlim(0, max(speedups) * 1.12) + ax.set_xlabel('speedup vs single CPU core (single-thread PySPH ~3.5 s/step at 1M)') + ax.set_title('GPU speedup vs 1 CPU core @ 1M particles\n(Warp grid-direct WCSPH, fp32)') + ax.grid(True, axis='x', alpha=0.3) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'speedup_vs_cpu_1M.png'), dpi=130) + plt.close(fig) + + +def cost_per_billion(throughput, cost_hr): + """USD to compute 1e9 particle-steps at this throughput (particle-steps/s).""" + return cost_hr / (throughput * 3600.0) * 1e9 + + +def plot_cost_bar(gpus): + rows = [] + for g in gpus: + cost = cost_per_hr(g['name']) + at1m = [p for p in g['points'] if p[0] == ONE_MILLION] + if cost and at1m: + rows.append((g['name'], cost_per_billion(at1m[0][2], cost))) + rows.sort(key=lambda r: r[1], reverse=True) # cheapest at top + names = [r[0] for r in rows] + vals = [r[1] for r in rows] + fig, ax = plt.subplots(figsize=(8.5, 4.6)) + bars = ax.barh(names, vals, color='tab:red', alpha=0.8) + for b, v in zip(bars, vals): + ax.text(b.get_width(), b.get_y() + b.get_height() / 2, + ' $%.4f' % v, va='center', fontsize=9) + ax.set_xlim(0, max(vals) * 1.18) + ax.set_xlabel('$ per billion particle-steps (lower = cheaper compute)') + ax.set_title('Cost of compute @ 1M particles (NVIDIA Brev hourly pricing)\n' + '(Warp grid-direct WCSPH, fp32; USD per 1e9 particle-steps)') + ax.grid(True, axis='x', alpha=0.3) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'cost_per_billion_1M.png'), dpi=130) + plt.close(fig) + + +def plot_cost_curve(gpus): + fig, ax = plt.subplots(figsize=(8, 5)) + for g in gpus: + cost = cost_per_hr(g['name']) + if not cost: + continue + xs = [p[0] for p in g['points']] + ys = [cost_per_billion(p[2], cost) for p in g['points']] + style = 'o' if len(xs) == 1 else 'o-' + ax.plot(xs, ys, style, label=g['name'], markersize=5) + ax.set_xscale('log') + ax.set_yscale('log') + ax.set_xlabel('particles') + ax.set_ylabel('$ per billion particle-steps') + ax.set_title('Cost of compute vs particle count (NVIDIA Brev hourly pricing)\n' + '(Warp grid-direct WCSPH, fp32; cost rises past each GPU knee)') + ax.grid(True, which='both', alpha=0.3) + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'cost_per_billion_vs_particles.png'), dpi=130) + plt.close(fig) + + +def plot_energy_bar(gpus): + rows = [] + for g in gpus: + tdp = _lookup(BOARD_TDP_W, g['name']) + at1m = [p for p in g['points'] if p[0] == ONE_MILLION] + if tdp and at1m: + rows.append((g['name'], energy_kj_per_gpstep(at1m[0][2], tdp))) + rows.sort(key=lambda r: r[1], reverse=True) # lowest energy at top + names = [r[0] for r in rows] + vals = [r[1] for r in rows] + fig, ax = plt.subplots(figsize=(8.5, 4.6)) + bars = ax.barh(names, vals, color='tab:orange', alpha=0.85) + for b, v in zip(bars, vals): + ax.text(b.get_width(), b.get_y() + b.get_height() / 2, + ' %.1f kJ' % v, va='center', fontsize=9) + ax.set_xlim(0, max(vals) * 1.15) + ax.set_xlabel('kJ per billion particle-steps (lower = less energy per work)') + ax.set_title('Energy-to-solution @ 1M particles (ESTIMATE from board TDP)\n' + '(Warp grid-direct WCSPH, fp32; not a measured power draw)') + ax.grid(True, axis='x', alpha=0.3) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'energy_per_gpstep_1M.png'), dpi=130) + plt.close(fig) + + +def plot_mbu_bar(gpus): + rows = [] + for g in gpus: + peak = _lookup(PEAK_BW_TBS, g['name']) + at1m = [p for p in g['points'] if p[0] == ONE_MILLION] + if peak and at1m: + rows.append((g['name'], mbu_frac(at1m[0][2], peak) * 100.0)) + rows.sort(key=lambda r: r[1]) # most-utilized at top + names = [r[0] for r in rows] + vals = [r[1] for r in rows] + fig, ax = plt.subplots(figsize=(8.5, 4.6)) + bars = ax.barh(names, vals, color='tab:purple', alpha=0.8) + for b, v in zip(bars, vals): + ax.text(b.get_width(), b.get_y() + b.get_height() / 2, + ' %.1f%% of peak' % v, va='center', fontsize=9) + ax.set_xlim(0, max(vals) * 1.25) + ax.set_xlabel('analytic memory-bandwidth utilization @ 1M (% of datasheet peak)') + ax.set_title('Roofline: bandwidth utilization @ 1M (ANALYTIC upper-bound estimate)\n' + '(throughput x ~1.12 KB/p-step / peak BW; no profiler counter)') + ax.grid(True, axis='x', alpha=0.3) + fig.tight_layout() + fig.savefig(os.path.join(HERE, 'mbu_at_1M.png'), dpi=130) + plt.close(fig) + + +def main(): + gpus = load_gpus() + plot_throughput(gpus) + plot_perstep(gpus) + plot_speedup(gpus) + plot_cost_bar(gpus) + plot_cost_curve(gpus) + plot_energy_bar(gpus) + plot_mbu_bar(gpus) + print('wrote throughput_vs_particles.png, perstep_vs_particles.png, ' + 'speedup_vs_cpu_1M.png, cost_per_billion_1M.png, ' + 'cost_per_billion_vs_particles.png, energy_per_gpstep_1M.png, ' + 'mbu_at_1M.png to %s' % HERE) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/speedup_vs_cpu_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/speedup_vs_cpu_1M.png new file mode 100644 index 000000000..4b2557d7f Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/speedup_vs_cpu_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-b300.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-b300.json new file mode 100644 index 000000000..644bef3a3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-b300.json @@ -0,0 +1,44 @@ +{ + "benchmark": "warp grid-direct WCSPH continuity PEC step (fp32)", + "hardware": { + "name": "NVIDIA B300 SXM6 AC", + "arch": "sm_103", + "memory_gib": 267.7, + "warp": "1.14.0", + "cuda_toolkit": "12.9", + "driver_cuda": "13.0", + "host": "brev-cw5dulrtv", + "run_by": "@kunalpuri-prediqt", + "date": "2026-06-18" + }, + "config": { + "dt": 3.732778967800475e-07, + "steps": 20, + "warmup_discarded": 8, + "physics": { + "rho0": 1.0, + "c0": 1400.0, + "p0": 0.0, + "alpha": 0.1, + "beta": 0.0, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "xsph_eps": 0.5, + "density_mode": "continuity" + }, + "cold_compile_s": 41.25 + }, + "results": [ + {"nx": 100, "particles": 31417, "setup_s": 0.0, "per_step_s_min": 0.001016, "per_step_s_median": 0.001025, "per_step_s_max": 0.001034, "throughput_particle_steps_per_s": 30663805.0, "all_finite": true}, + {"nx": 200, "particles": 125629, "setup_s": 0.0, "per_step_s_min": 0.001547, "per_step_s_median": 0.001594, "per_step_s_max": 0.001704, "throughput_particle_steps_per_s": 78822824.5, "all_finite": true}, + {"nx": 400, "particles": 502625, "setup_s": 0.0, "per_step_s_min": 0.003546, "per_step_s_median": 0.003614, "per_step_s_max": 0.003635, "throughput_particle_steps_per_s": 139075641.2, "all_finite": true}, + {"nx": 565, "particles": 1002885, "setup_s": 0.0, "per_step_s_min": 0.007055, "per_step_s_median": 0.007132, "per_step_s_max": 0.007165, "throughput_particle_steps_per_s": 140619314.7, "all_finite": true}, + {"nx": 1000, "particles": 3141549, "setup_s": 0.0, "per_step_s_min": 0.021866, "per_step_s_median": 0.022015, "per_step_s_max": 0.022182, "throughput_particle_steps_per_s": 142700285.6, "all_finite": true}, + {"nx": 1400, "particles": 6157477, "setup_s": 0.0, "per_step_s_min": 0.043108, "per_step_s_median": 0.043366, "per_step_s_max": 0.043954, "throughput_particle_steps_per_s": 141988790.2, "all_finite": true}, + {"nx": 1800, "particles": 10178545, "setup_s": 0.0, "per_step_s_min": 0.12741, "per_step_s_median": 0.128046, "per_step_s_max": 0.128974, "throughput_particle_steps_per_s": 79491280.9, "all_finite": true}, + {"nx": 2600, "particles": 21236953, "setup_s": 0.0002, "per_step_s_min": 0.30304, "per_step_s_median": 0.305089, "per_step_s_max": 0.306577, "throughput_particle_steps_per_s": 69608976.8, "all_finite": true}, + {"nx": 3600, "particles": 40714821, "setup_s": 0.0003, "per_step_s_min": 0.579547, "per_step_s_median": 0.583046, "per_step_s_max": 0.589412, "throughput_particle_steps_per_s": 69831207.8, "all_finite": true}, + {"nx": 5000, "particles": 78539677, "setup_s": 0.0005, "per_step_s_min": 1.114662, "per_step_s_median": 1.120131, "per_step_s_max": 1.131411, "throughput_particle_steps_per_s": 70116519.6, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-l40s.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-l40s.json new file mode 100644 index 000000000..a3abd8736 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-l40s.json @@ -0,0 +1,42 @@ +{ + "benchmark": "warp grid-direct WCSPH continuity PEC step (fp32)", + "hardware": { + "name": "NVIDIA L40S", + "arch": "sm_89", + "memory_gib": 44.4, + "warp": "1.14.0", + "cuda_toolkit": "12.9", + "driver_cuda": "12.7", + "host": "brev-f96p16hkk", + "run_by": "@kunalpuri-prediqt", + "date": "2026-06-18" + }, + "config": { + "dt": 3.732778967800475e-07, + "steps": 20, + "warmup_discarded": 8, + "physics": { + "rho0": 1.0, + "c0": 1400.0, + "p0": 0.0, + "alpha": 0.1, + "beta": 0.0, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "xsph_eps": 0.5, + "density_mode": "continuity" + }, + "cold_compile_s": 89.11 + }, + "results": [ + {"nx": 100, "particles": 31417, "setup_s": 0.0, "per_step_s_min": 0.001498, "per_step_s_median": 0.001509, "per_step_s_max": 0.001533, "throughput_particle_steps_per_s": 20814796.2, "all_finite": true}, + {"nx": 200, "particles": 125629, "setup_s": 0.0, "per_step_s_min": 0.002583, "per_step_s_median": 0.002607, "per_step_s_max": 0.003151, "throughput_particle_steps_per_s": 48191832.9, "all_finite": true}, + {"nx": 400, "particles": 502625, "setup_s": 0.0, "per_step_s_min": 0.006182, "per_step_s_median": 0.006209, "per_step_s_max": 0.006236, "throughput_particle_steps_per_s": 80950765.0, "all_finite": true}, + {"nx": 565, "particles": 1002885, "setup_s": 0.0, "per_step_s_min": 0.010867, "per_step_s_median": 0.010915, "per_step_s_max": 0.011021, "throughput_particle_steps_per_s": 91884908.4, "all_finite": true}, + {"nx": 1000, "particles": 3141549, "setup_s": 0.0, "per_step_s_min": 0.032031, "per_step_s_median": 0.032157, "per_step_s_max": 0.032343, "throughput_particle_steps_per_s": 97695297.7, "all_finite": true}, + {"nx": 1400, "particles": 6157477, "setup_s": 0.0002, "per_step_s_min": 0.062852, "per_step_s_median": 0.063098, "per_step_s_max": 0.063477, "throughput_particle_steps_per_s": 97586559.3, "all_finite": true}, + {"nx": 1800, "particles": 10178545, "setup_s": 0.0002, "per_step_s_min": 0.102138, "per_step_s_median": 0.102492, "per_step_s_max": 0.103182, "throughput_particle_steps_per_s": 99310562.3, "all_finite": true}, + {"nx": 2600, "particles": 21236953, "setup_s": 0.0083, "per_step_s_min": 0.354146, "per_step_s_median": 0.356544, "per_step_s_max": 0.358542, "throughput_particle_steps_per_s": 59563296.4, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtx5090.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtx5090.json new file mode 100644 index 000000000..82dba55c1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtx5090.json @@ -0,0 +1,42 @@ +{ + "benchmark": "warp grid-direct WCSPH continuity PEC step (fp32)", + "hardware": { + "name": "NVIDIA GeForce RTX 5090", + "arch": "sm_120", + "memory_gib": 31.4, + "warp": "1.14.0", + "cuda_toolkit": "12.9", + "driver_cuda": "12.8", + "host": "shadecloud", + "run_by": "@kunalpuri-prediqt", + "date": "2026-06-18" + }, + "config": { + "dt": 3.732778967800475e-07, + "steps": 20, + "warmup_discarded": 8, + "physics": { + "rho0": 1.0, + "c0": 1400.0, + "p0": 0.0, + "alpha": 0.1, + "beta": 0.0, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "xsph_eps": 0.5, + "density_mode": "continuity" + }, + "cold_compile_s": 75.25 + }, + "results": [ + {"nx": 100, "particles": 31417, "setup_s": 0.0, "per_step_s_min": 0.002399, "per_step_s_median": 0.002473, "per_step_s_max": 0.002613, "throughput_particle_steps_per_s": 12705362.1, "all_finite": true}, + {"nx": 200, "particles": 125629, "setup_s": 0.0, "per_step_s_min": 0.003839, "per_step_s_median": 0.003915, "per_step_s_max": 0.004302, "throughput_particle_steps_per_s": 32086312.7, "all_finite": true}, + {"nx": 400, "particles": 502625, "setup_s": 0.0, "per_step_s_min": 0.009502, "per_step_s_median": 0.009707, "per_step_s_max": 0.010451, "throughput_particle_steps_per_s": 51781505.3, "all_finite": true}, + {"nx": 565, "particles": 1002885, "setup_s": 0.0, "per_step_s_min": 0.014765, "per_step_s_median": 0.014879, "per_step_s_max": 0.017328, "throughput_particle_steps_per_s": 67401412.9, "all_finite": true}, + {"nx": 1000, "particles": 3141549, "setup_s": 0.0, "per_step_s_min": 0.042757, "per_step_s_median": 0.043096, "per_step_s_max": 0.044528, "throughput_particle_steps_per_s": 72895751.0, "all_finite": true}, + {"nx": 1400, "particles": 6157477, "setup_s": 0.0003, "per_step_s_min": 0.082842, "per_step_s_median": 0.084586, "per_step_s_max": 0.085141, "throughput_particle_steps_per_s": 72795819.8, "all_finite": true}, + {"nx": 1800, "particles": 10178545, "setup_s": 0.0002, "per_step_s_min": 0.219998, "per_step_s_median": 0.221873, "per_step_s_max": 0.22583, "throughput_particle_steps_per_s": 45875530.3, "all_finite": true}, + {"nx": 2600, "particles": 21236953, "setup_s": 0.0018, "per_step_s_min": 0.504624, "per_step_s_median": 0.511236, "per_step_s_max": 0.522709, "throughput_particle_steps_per_s": 41540394.7, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtxpro6000.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtxpro6000.json new file mode 100644 index 000000000..4e57e5217 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-rtxpro6000.json @@ -0,0 +1,43 @@ +{ + "benchmark": "warp grid-direct WCSPH continuity PEC step (fp32)", + "hardware": { + "name": "NVIDIA RTX PRO 6000 Blackwell Server Edition", + "arch": "sm_120", + "memory_gib": 95.0, + "warp": "1.14.0", + "cuda_toolkit": "12.9", + "driver_cuda": "13.0", + "host": "brev-xrsztl72t", + "run_by": "@kunalpuri-prediqt", + "date": "2026-06-18" + }, + "config": { + "dt": 3.732778967800475e-07, + "steps": 20, + "warmup_discarded": 8, + "physics": { + "rho0": 1.0, + "c0": 1400.0, + "p0": 0.0, + "alpha": 0.1, + "beta": 0.0, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "xsph_eps": 0.5, + "density_mode": "continuity" + }, + "cold_compile_s": 60.31 + }, + "results": [ + {"nx": 100, "particles": 31417, "setup_s": 0.0, "per_step_s_min": 0.001659, "per_step_s_median": 0.001683, "per_step_s_max": 0.001699, "throughput_particle_steps_per_s": 18667366.2, "all_finite": true}, + {"nx": 200, "particles": 125629, "setup_s": 0.0, "per_step_s_min": 0.002333, "per_step_s_median": 0.002368, "per_step_s_max": 0.002405, "throughput_particle_steps_per_s": 53049247.6, "all_finite": true}, + {"nx": 400, "particles": 502625, "setup_s": 0.0, "per_step_s_min": 0.004969, "per_step_s_median": 0.005003, "per_step_s_max": 0.005037, "throughput_particle_steps_per_s": 100459761.4, "all_finite": true}, + {"nx": 565, "particles": 1002885, "setup_s": 0.0, "per_step_s_min": 0.008164, "per_step_s_median": 0.008223, "per_step_s_max": 0.008496, "throughput_particle_steps_per_s": 121964330.0, "all_finite": true}, + {"nx": 1000, "particles": 3141549, "setup_s": 0.0, "per_step_s_min": 0.021682, "per_step_s_median": 0.021753, "per_step_s_max": 0.021824, "throughput_particle_steps_per_s": 144419891.4, "all_finite": true}, + {"nx": 1400, "particles": 6157477, "setup_s": 0.0, "per_step_s_min": 0.096972, "per_step_s_median": 0.098554, "per_step_s_max": 0.099153, "throughput_particle_steps_per_s": 62478112.3, "all_finite": true}, + {"nx": 1800, "particles": 10178545, "setup_s": 0.0, "per_step_s_min": 0.162373, "per_step_s_median": 0.163877, "per_step_s_max": 0.165923, "throughput_particle_steps_per_s": 62110771.7, "all_finite": true}, + {"nx": 2600, "particles": 21236953, "setup_s": 0.0002, "per_step_s_min": 0.395796, "per_step_s_median": 0.396781, "per_step_s_max": 0.407899, "throughput_particle_steps_per_s": 53523057.8, "all_finite": true}, + {"nx": 3600, "particles": 40714821, "setup_s": 0.0004, "per_step_s_min": 0.786864, "per_step_s_median": 0.79079, "per_step_s_max": 0.795612, "throughput_particle_steps_per_s": 51486249.9, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/throughput_vs_particles.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/throughput_vs_particles.png new file mode 100644 index 000000000..304d4d710 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/throughput_vs_particles.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu_perf_sweep.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu_perf_sweep.py new file mode 100644 index 000000000..e4afc1ae8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu_perf_sweep.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""GPU performance sweep for the Warp grid-direct WCSPH backend. + +Sweeps particle count (via ``nx``) and, for each, measures the steady per-step +wall time and throughput of the fixed-step continuity-density PEC step on the +current GPU. Run it on each GPU and paste the JSON block; the per-GPU JSONs +together form a cross-GPU performance artifact. + +This is GPU-only on purpose -- the single-threaded PySPH CPU Application is slow +at scale, so the CPU-vs-Warp speedup lives in ``headline_million_100step.py`` +(one point at 1M). Here the cross-GPU comparison comes from running the same +sweep on each GPU and comparing throughput vs particle count. + +Usage (run from the repo root): + + R=.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner + PYTHONPATH=$R python $R/gpu_perf_sweep.py + # custom sweep / longer averaging / a label: + PYTHONPATH=$R python $R/gpu_perf_sweep.py \ + --nx-list 100,200,400,565,1000,1400,1800,2600 --steps 16 --warmup 6 \ + --label "RTX PRO 6000 Blackwell" --output sweep.json + +Notes: +- Physics matches the production runs: gaussian kernel, Tait EOS, continuity + density, radius_scale=3, fixed dt, fp32 (compyle use_double=False). +- The fused grid kernel cold-compiles once on a cold disk cache (reported as + cold_compile_s); a warmup run absorbs it so the swept points are warm. +- Out-of-memory at large nx is caught per point and recorded, so the sweep + finds the GPU's capacity ceiling without aborting. +- nx maps to particle count via the elliptical-drop disk fill + (particles ~ pi * nx^2): nx=100 ~ 31k, 565 ~ 1.0M, 1000 ~ 3.1M, 1800 ~ 10M. +""" + +from __future__ import annotations + +import argparse +import gc +import json +import statistics +import time + +import numpy as np +import warp as wp + +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_leapfrog_step +from warp_elliptical_drop_runner import WarpEllipticalDropRunner + +DT = 3.732778967800475e-07 +PHYS = dict( + rho0=1.0, c0=1400.0, p0=0.0, alpha=0.1, beta=0.0, eos='tait', gamma=7.0, + kernel='gaussian', xsph_eps=0.5, density_mode='continuity', +) + + +def _gpu_info(): + try: + devs = wp.get_cuda_devices() + except Exception: + devs = [] + if not devs: + return {'name': 'cpu/none', 'arch': 'n/a', 'memory_gib': None} + d = devs[0] + return { + 'name': d.name, + 'arch': 'sm_%s' % d.arch, + 'memory_gib': round(d.total_memory / 2**30, 1), + 'warp': wp.__version__, + } + + +def _run_steps(nnps, steps): + dev = nnps.device + walls = [] + for _ in range(steps): + wp.synchronize_device(dev) + t = time.perf_counter() + wc_sph_leapfrog_step(nnps, dt=DT, return_dt=True, **PHYS) + wp.synchronize_device(dev) + walls.append(time.perf_counter() - t) + return walls + + +def bench_one(nx, steps, warmup): + runner = WarpEllipticalDropRunner(nx=nx, steps=steps, dt=DT, **PHYS) + pa = runner.create_particles() + nnps = UniformGridWarpNNPS( + dim=2, particles=[pa], radius_scale=runner.radius_scale + ) + n = int(pa.get_number_of_particles()) + t_setup = time.perf_counter() + wp.synchronize_device(nnps.device) + setup_s = time.perf_counter() - t_setup + + walls = _run_steps(nnps, steps) + pa.gpu.pull('x', 'y', 'rho', 'u', 'v') + finite = bool(np.all(np.isfinite(pa.x)) and np.all(np.isfinite(pa.rho))) + + steady = walls[warmup:] or walls + median = statistics.median(steady) + result = { + 'nx': nx, + 'particles': n, + 'setup_s': round(setup_s, 4), + 'per_step_s_min': round(min(steady), 6), + 'per_step_s_median': round(median, 6), + 'per_step_s_max': round(max(steady), 6), + 'throughput_particle_steps_per_s': round(n / median, 1), + 'all_finite': finite, + } + del nnps, pa, runner + gc.collect() + return result + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--nx-list', default='100,200,300,400,565,800,1000,1400,1800', + help='comma-separated nx values (particles ~ pi*nx^2)') + parser.add_argument('--steps', type=int, default=14) + parser.add_argument('--warmup', type=int, default=4) + parser.add_argument('--label', default=None, + help='override the GPU label in the output') + parser.add_argument('--output', default=None, help='also write JSON here') + args = parser.parse_args() + + wp.init() + gpu = _gpu_info() + if args.label: + gpu['name'] = args.label + + # Warm the kernel cache once (absorbs the one-time cold compile) and time it. + t = time.perf_counter() + try: + bench_one(20, max(4, args.warmup + 2), args.warmup) + except Exception as exc: # pragma: no cover + print('warmup failed:', exc) + cold_compile_s = round(time.perf_counter() - t, 2) + + nx_list = [int(s) for s in args.nx_list.split(',') if s.strip()] + results = [] + for nx in nx_list: + try: + r = bench_one(nx, args.steps, args.warmup) + results.append(r) + print('nx=%-5d particles=%-9d per_step=%.6f s throughput=%.3e p-steps/s finite=%s' + % (r['nx'], r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s'], r['all_finite']), + flush=True) + except Exception as exc: + results.append({'nx': nx, 'error': type(exc).__name__ + ': ' + + str(exc)[:120]}) + print('nx=%-5d FAILED: %s' % (nx, type(exc).__name__), flush=True) + gc.collect() + + report = { + 'benchmark': 'warp grid-direct WCSPH continuity PEC step (fp32)', + 'hardware': gpu, + 'config': { + 'dt': DT, 'steps': args.steps, 'warmup_discarded': args.warmup, + 'physics': PHYS, 'cold_compile_s': cold_compile_s, + }, + 'results': results, + } + + print('\n=== sweep table ===') + print('| nx | particles | per-step (s) | throughput (particle-steps/s) | finite |') + print('|---:|---:|---:|---:|:--:|') + for r in results: + if 'error' in r: + print('| %d | -- | ERROR | %s | -- |' % (r['nx'], r['error'])) + else: + print('| %d | %d | %.6f | %.3e | %s |' % ( + r['nx'], r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s'], r['all_finite'])) + + print('\n=== JSON (paste this) ===') + print(json.dumps(report, indent=2)) + if args.output: + with open(args.output, 'w') as f: + json.dump(report, f, indent=2) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/headline_million_100step.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/headline_million_100step.py new file mode 100644 index 000000000..744490daf --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/headline_million_100step.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +"""Fresh million-particle, 100-fixed-step CPU-vs-Warp headline comparison. + +Warp: the grid-direct WCSPH continuity path (ADR-0004) via the elliptical-drop +runner. CPU: the real PySPH Cython Application +(``pysph/examples/elliptical_drop_no_scheme.py``: Gaussian / TaitEOS / +ContinuityEquation / MomentumEquation / XSPHCorrection / WCSPHStep), forced to +fixed timestep with ``--no-adaptive-timestep`` so it runs exactly the same 100 +steps Warp does. + +Both sides use identical physics (gaussian kernel, tait EOS, continuity density, +radius_scale=3, c0=1400, alpha=0.1, xsph_eps=0.5, dt=3.7328e-07). No numbers are +reused -- both are measured in this run on this machine. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import time +from pathlib import Path + +import numpy as np +import warp as wp + +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_leapfrog_step +from pysph.solver.utils import load +from warp_elliptical_drop_runner import WarpEllipticalDropRunner + +DT = 3.732778967800475e-07 +RHO0, C0, ALPHA, BETA, GAMMA, XSPH = 1.0, 1400.0, 0.1, 0.0, 7.0, 0.5 + + +def run_warp(nx, steps): + runner = WarpEllipticalDropRunner( + nx=nx, steps=steps, dt=DT, rho0=RHO0, c0=C0, p0=0.0, alpha=ALPHA, + beta=BETA, eos='tait', gamma=GAMMA, kernel='gaussian', xsph_eps=XSPH, + density_mode='continuity', + ) + t0 = time.perf_counter() + pa = runner.create_particles() + nnps = UniformGridWarpNNPS( + dim=2, particles=[pa], radius_scale=runner.radius_scale + ) + wp.synchronize_device(nnps.device) + setup_s = time.perf_counter() - t0 + + t1 = time.perf_counter() + for _ in range(steps): + wc_sph_leapfrog_step( + nnps, dt=DT, rho0=RHO0, c0=C0, p0=0.0, alpha=ALPHA, beta=BETA, + eos='tait', gamma=GAMMA, kernel='gaussian', xsph_eps=XSPH, + adaptive_dt=False, density_mode='continuity', return_dt=True, + ) + wp.synchronize_device(nnps.device) + steps_s = time.perf_counter() - t1 + + pa.gpu.pull('x', 'y', 'rho', 'u', 'v') + ke = 0.5 * float(np.sum(pa.m * (pa.u*pa.u + pa.v*pa.v))) + finite = bool(np.all(np.isfinite(pa.x)) and np.all(np.isfinite(pa.rho))) + return { + 'particles': int(pa.get_number_of_particles()), + 'setup_s': setup_s, + 'steps_s': steps_s, + 'total_s': setup_s + steps_s, + 'per_step_s': steps_s / steps, + 'kinetic_energy': ke, + 'all_finite': finite, + } + + +def run_cpu(nx, steps, out_dir): + repo_root = Path(__file__).resolve().parents[5] + app_dir = Path(out_dir) / 'cpu-app-output' + app_dir.mkdir(parents=True, exist_ok=True) + tf = steps * DT + command = [ + sys.executable, 'pysph/examples/elliptical_drop_no_scheme.py', + '--nx', str(nx), '--tf', repr(tf), '--timestep', repr(DT), + '--no-adaptive-timestep', '--n-damp', '0', '--pfreq', str(steps), + '--fname', 'cpu', '--directory', str(app_dir), + '--logfile', '', '--quiet', + ] + t0 = time.perf_counter() + proc = subprocess.run(command, cwd=repo_root, text=True, + stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + wall = time.perf_counter() - t0 + if proc.returncode != 0: + raise RuntimeError('CPU app failed:\n' + proc.stdout[-3000:]) + files = sorted(app_dir.glob('cpu_*.npz')) + sorted(app_dir.glob('cpu_*.hdf5')) + last = load(str(files[-1])) + count = int(last['solver_data']['count']) + pa = last['arrays']['fluid'] + ke = 0.5 * float(np.sum(pa.m * (pa.u*pa.u + pa.v*pa.v))) + finite = bool(np.all(np.isfinite(pa.x)) and np.all(np.isfinite(pa.rho))) + return { + 'particles': int(pa.get_number_of_particles()), + 'total_s': wall, + 'steps_recorded': count, + 'per_step_s': wall / count if count else None, + 'kinetic_energy': ke, + 'all_finite': finite, + 'command': command, + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--nx', type=int, default=565) + parser.add_argument('--steps', type=int, default=100) + parser.add_argument('--output', default=None) + parser.add_argument('--out-dir', default='/tmp/headline_million') + args = parser.parse_args() + + warp = run_warp(args.nx, args.steps) + cpu = run_cpu(args.nx, args.steps, args.out_dir) + + result = { + 'case': { + 'nx': args.nx, 'steps': args.steps, 'dt': DT, + 'particles': warp['particles'], 'mode': 'fixed timestep', + 'kernel': 'gaussian', 'eos': 'tait', 'density_mode': 'continuity', + 'cpu_backend': 'pysph Application (elliptical_drop_no_scheme), single-threaded, --no-adaptive-timestep', + 'warp_path': 'grid-direct (ADR-0004)', + }, + 'warp': warp, + 'cpu': cpu, + 'speedup_total_wall': cpu['total_s'] / warp['total_s'], + 'speedup_per_step': (cpu['per_step_s'] / warp['per_step_s']) + if cpu['per_step_s'] else None, + 'kinetic_energy_rel_delta': abs(warp['kinetic_energy'] - cpu['kinetic_energy']) + / abs(cpu['kinetic_energy']) if cpu['kinetic_energy'] else None, + } + print(json.dumps(result, indent=2, sort_keys=True)) + if args.output: + with open(args.output, 'w') as f: + json.dump(result, f, indent=2, sort_keys=True) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/million-cpu-gpu-10step-cache-reuse-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/million-cpu-gpu-10step-cache-reuse-summary.json new file mode 100644 index 000000000..d58b9891d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/million-cpu-gpu-10step-cache-reuse-summary.json @@ -0,0 +1,98 @@ +{ + "cache_profile": { + "avg_neighbors_per_cache": 44.873, + "current_cache_builds_per_step": 2, + "previous_cache_builds_per_step": 8, + "segmented_cache_time_s_range": [ + 0.036122, + 0.052126 + ], + "segmented_step_wall_s_range": [ + 0.098335, + 0.13829 + ] + }, + "case": { + "alpha": 0.1, + "beta": 0.0, + "c0": 1400.0, + "density_mode": "continuity", + "dt": 3.732778967800475e-07, + "gamma": 7.0, + "kernel": "Gaussian", + "mode": "fixed timestep", + "nx": 565, + "optimization": "reuse one neighbor cache per PEC half-stage", + "particles": 1002885, + "rho0": 1.0, + "steps": 10, + "tf": 3.732778967800475e-06, + "xsph_eps": 0.5 + }, + "cpu": { + "metrics": { + "all_finite": true, + "axis_x_abs": 0.999184493010564, + "axis_y_abs": 0.9999306197161054, + "kinetic_energy": 7854.129614325726, + "p_max": 131.6238816019233, + "p_min": -138.08411842127555, + "particles": 1002885, + "rho_max": 1.000068054002785, + "rho_min": 0.9999282369195914, + "steps": 10, + "time": 3.732778967800475e-06, + "x_max": 0.999184493010564, + "x_min": -0.9982998675927736, + "y_max": 0.9999306197161054, + "y_min": -0.9990453336294709 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/pysph/million-pysph_00010.hdf5" + }, + "deltas": { + "axis_x_abs": -1.2296967044633789e-07, + "axis_y_abs": 4.773760275966765e-10, + "kinetic_energy": 8.525348675902933e-06, + "p_max": 0.18822166956107367, + "p_min": 0.030224378306797917, + "rho_max": 1.4501548406542497e-08, + "rho_min": -9.119009991565008e-10, + "x_max": -1.2296967044633789e-07, + "x_min": -1.4833358752408543e-07, + "y_max": 4.773760275966765e-10, + "y_min": 8.082948310672577e-08 + }, + "performance": { + "cpu_average_step_time_s": 5.747999999999999, + "cpu_wall_time_s": 57.48, + "cpu_wall_time_source": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/million-cpu-gpu-10step-summary.json", + "previous_speedup_wall_time": 8.01673640167364, + "previous_warp_wall_time_s": 7.17, + "speedup_wall_time": 12.7450110864745, + "warp_average_step_time_s": 0.45099999999999996, + "warp_improvement_vs_previous": 1.58980044345898, + "warp_wall_time_s": 4.51 + }, + "warp": { + "metrics": { + "all_finite": true, + "axis_x_abs": 0.9991843700408936, + "axis_y_abs": 0.9999306201934814, + "dt_max_used": 3.732778967800475e-07, + "dt_min_used": 3.732778967800475e-07, + "kinetic_energy": 7854.129622851075, + "p_max": 131.81210327148438, + "p_min": -138.05389404296875, + "particles": 1002885, + "rho_max": 1.0000680685043335, + "rho_min": 0.9999282360076904, + "steps": 10, + "time": 3.732778967800476e-06, + "x_max": 0.9991843700408936, + "x_min": -0.9983000159263611, + "y_max": 0.9999306201934814, + "y_min": -0.9990452527999878 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-cache-reuse/warp/million-warp.npz" + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/million-cpu-gpu-10step-fused-eqns-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/million-cpu-gpu-10step-fused-eqns-summary.json new file mode 100644 index 000000000..3ab7e4658 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/million-cpu-gpu-10step-fused-eqns-summary.json @@ -0,0 +1,163 @@ +{ + "adaptive_guard_nx100_resolved": { + "committed_steps": 1393, + "committed_warp_wall_s": 23.629107111992198, + "deltas_vs_committed_warp": { + "axis_major": 4.768e-07, + "axis_minor": 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b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/blackwell-rtxpro6000-million-100step-summary.json @@ -0,0 +1,61 @@ +{ + "note": "Production GPU result on an NVIDIA RTX PRO 6000 Blackwell Server Edition, recorded from a run by @kunalpuri-prediqt on the brev cloud box (host: brev-xrsztl72t) on 2026-06-18. Numbers are as reported by the scripts; not re-run on the dev RTX 4060. CPU = single-threaded PySPH Cython Application; Warp = grid-direct WCSPH (fp32).", + "hardware": { + "gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition", + "gpu_memory_gib": 95, + "arch": "sm_120 (Blackwell)", + "warp": "1.14.0", + "cuda_toolkit": "12.9", + "driver_cuda": "13.0", + "cpu_threads": 1 + }, + "million_100step_fixed": { + "nx": 565, + "particles": 1002885, + "steps": 100, + "dt": 3.732778967800475e-07, + "mode": "fixed timestep (--no-adaptive-timestep --n-damp 0)", + "kernel": "gaussian", "eos": "tait", "density_mode": "continuity", + "cpu_total_wall_s": 357.4319926870012, + "cpu_per_step_s": 3.574319926870012, + "cpu_kinetic_energy": 7854.038960953369, + "warp_total_wall_s": 1.316273918000661, + "warp_setup_s": 0.45374192199960817, + "warp_steps_s": 0.8625319960010529, + "warp_per_step_s": 0.008625319960010529, + "warp_kinetic_energy": 7854.038954632484, + "speedup_total_wall": 271.54833640547884, + "speedup_per_step": 414.3985316998778, + "kinetic_energy_rel_delta": 8.047941353933115e-10, + "all_finite": true + }, + "segmented_profile": { + "nx": 565, + "particles": 1002885, + "steps": 12, + "warmup_discarded": 2, + "flat_cache_builds_total": 0, + "grid_builds_per_step": 2.0, + "equation_launches_total": 24, + "step_wall_s_steady_range": [0.00803287199960323, 0.008118533998640487], + "equation_launch_s_steady_range_per_launch": [0.001573813999129925, 0.0016127579983731266], + "grid_build_s_steady_range": [0.00019679899924085476, 0.0002280590015288908], + "kinetic_energy": 7854.127631137399, + "all_finite": true, + "note": "steady step wall ~8.0 ms (vs ~60 ms on the RTX 4060 -> ~7.4x faster per step on this GPU); no flat neighbor cache built." + }, + "kernel_cache": { + "cold_compile_ms_first_run": 38575.45, + "warm_load_ms_subsequent": 6.42, + "note": "Fused continuity grid kernel cold-compiled once then loaded (cached) on subsequent invocations -- confirms the deterministic-name on-disk kernel cache (md5 cache key) works on a fresh machine." + }, + "comparison_vs_rtx4060_laptop": { + "rtx4060_speedup_total_wall": 41.17, + "rtx4060_speedup_per_step": 57.64, + "rtx4060_warp_per_step_s": 0.05976, + "blackwell_speedup_total_wall": 271.55, + "blackwell_speedup_per_step": 414.40, + "blackwell_warp_per_step_s": 0.008625, + "note": "Same fp32 grid-direct code; per-GPU jump is the Blackwell datacenter-class GPU (~7x faster per step) over the laptop 4060 while single-threaded CPU stays ~3.5 s/step." + } +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-headline-speedups.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-headline-speedups.json new file mode 100644 index 000000000..3a227ef08 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-headline-speedups.json @@ -0,0 +1,34 @@ +{ + "note": "Fresh, same-session, same-machine CPU-vs-Warp speedups for the grid-direct (ADR-0004) path. No numbers reused. CPU = single-threaded PySPH Cython Application (pysph/examples/elliptical_drop_no_scheme.py: Gaussian/TaitEOS/ContinuityEquation/MomentumEquation/XSPHCorrection/WCSPHStep). Warp = grid-direct WCSPH continuity path on RTX 4060 (fp32). Measured 2026-06-18.", + "hardware": {"gpu": "NVIDIA GeForce RTX 4060 Laptop GPU", "cpu_threads": 1, "warp": "1.14.0"}, + "nx100_apples_to_apples": { + "description": "resolved elliptical drop, continuity density, PySPH timestep policy (adaptive), identical step count both sides", + "particles": 31417, + "steps_cpu": 1393, + "steps_warp": 1393, + "cpu_wall_s": 160.099, + "cpu_s_per_step": 0.11493, + "warp_wall_s": 6.781, + "warp_s_per_step": 0.00487, + "speedup_wall": 23.61, + "final_state_deltas_vs_cpu": "fp32 scale (~1e-6/1e-7); KE delta ~2e-4 absolute", + "source": "fresh-nx100-cpu-vs-warp-summary.json" + }, + "million_100step_fixed": { + "description": "1M-particle elliptical drop, 100 fixed undamped steps (n_damp=0), identical dt both sides", + "particles": 1002885, + "steps": 100, + "dt": 3.732778967800475e-07, + "cpu_total_wall_s": 344.50, + "cpu_s_per_step": 3.4450, + "warp_setup_s": 2.39, + "warp_steps_s": 5.976, + "warp_total_wall_s": 8.367, + "warp_s_per_step": 0.05976, + "speedup_total_wall": 41.17, + "speedup_per_step_compute": 57.64, + "kinetic_energy_rel_delta": 1.64e-09, + "all_finite": true, + "source": "fresh-million-100step-cpu-vs-warp.json" + } +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-million-100step-cpu-vs-warp.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-million-100step-cpu-vs-warp.json new file mode 100644 index 000000000..d0cf859b1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-million-100step-cpu-vs-warp.json @@ -0,0 +1,56 @@ +{ + "case": { + "cpu_backend": "pysph Application (elliptical_drop_no_scheme), single-threaded, --no-adaptive-timestep", + "density_mode": "continuity", + "dt": 3.732778967800475e-07, + "eos": "tait", + "kernel": "gaussian", + "mode": "fixed timestep", + "nx": 565, + "particles": 1002885, + "steps": 100, + "warp_path": "grid-direct (ADR-0004)" + }, + "cpu": { + "all_finite": true, + "command": [ + "/home/kunalp/.pqt_venv_e0b41259/bin/python", + "pysph/examples/elliptical_drop_no_scheme.py", + "--nx", + "565", + "--tf", + "3.732778967800475e-05", + "--timestep", + "3.732778967800475e-07", + "--no-adaptive-timestep", + "--n-damp", + "0", + "--pfreq", + "100", + "--fname", + "cpu", + "--directory", + "/tmp/headline_million/cpu-app-output", + "--logfile", + "", + "--quiet" + ], + "kinetic_energy": 7854.038960953369, + "particles": 1002885, + "per_step_s": 3.4450491134000187, + "steps_recorded": 100, + "total_s": 344.50491134000185 + }, + "kinetic_energy_rel_delta": 1.6373507657086987e-09, + "speedup_per_step": 57.64458860827358, + "speedup_total_wall": 41.17249293352525, + "warp": { + "all_finite": true, + "kinetic_energy": 7854.038948093552, + "particles": 1002885, + "per_step_s": 0.059763616959971844, + "setup_s": 2.390994438988855, + "steps_s": 5.976361695997184, + "total_s": 8.36735613498604 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-nx100-cpu-vs-warp-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-nx100-cpu-vs-warp-summary.json new file mode 100644 index 000000000..3446100d1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/fresh-nx100-cpu-vs-warp-summary.json @@ -0,0 +1,217 @@ +{ + "case": { + "alpha": 0.1, + "beta": 0.0, + "c0": 1400.0, + "cfl": 0.3, + "dt": 2.1090201168072683e-06, + "gamma": 7.0, + "hdx": 1.3, + "n_damp": 50, + "nx": 100, + "output_times": [ + 0.0008, + 0.0038 + ], + "p0": 0.0, + "rho0": 1.0, + "warp_density_mode": "continuity", + "warp_dt_max": Infinity, + "warp_timestep_policy": "pysph", + "xsph_eps": 0.5 + }, + "comparisons": { + "t0p0008000": { + "deltas": { + "axis_major_estimate": -5.596712788769054e-07, + "axis_minor_estimate": 5.454902523016614e-07, + "kinetic_energy": -0.0033081273668358335, + "rho_max": 8.027067783800135e-07, + "rho_min": -3.310849231441537e-07, + "x_max": 5.45490252523706e-07, + "x_min": -3.666763179754895e-07, + "y_max": -5.596712782107716e-07, + "y_min": 5.596712788769054e-07 + }, + "image": "/tmp/headline_nx100/headline-nx100-t0p0008000.png", + "time": 0.0008 + }, + "t0p0038000": { + "deltas": { + "axis_major_estimate": -5.510011020781747e-07, + "axis_minor_estimate": 1.7770991430632321e-06, + "kinetic_energy": -0.0002200547114625806, + "rho_max": 1.1139908333568371e-06, + "rho_min": -1.2401270134132503e-06, + "x_max": 1.7770991441734552e-06, + "x_min": -1.002238760983154e-06, + "y_max": -6.702103911848667e-07, + "y_min": 5.510011020781747e-07 + }, + "image": "/tmp/headline_nx100/headline-nx100-t0p0038000.png", + "time": 0.0038 + } + }, + "cpu": { + "average_step_time_s": 0.11493080448025716, + "backend": "pysph-application", + "checkpoints": { + "t0p0008000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.0816640490130147, + "axis_minor_estimate": 0.9220712094421513, + "axis_x_abs": 0.9220712094421513, + "axis_y_abs": 1.0816640490130147, + "exact_major_axis": 1.0831034701687434, + "exact_minor_axis": 0.9232728243814081, + "kinetic_energy": 7818.556269923258, + "p_max": 11071.301686731818, + "p_min": -835.6697397960966, + "particles": 31417, + "rho_max": 1.0055542309769985, + "rho_min": 0.9995725485493702, + "time": 0.0008, + "x_max": 0.922071209442151, + "x_min": -0.9220712094421513, + "y_max": 1.081664049013014, + "y_min": -1.0816640490130147 + }, + "path": "/tmp/headline_nx100/headline-nx100-pysph-app-output/headline-nx100-pysph_00313.hdf5" + }, + "t0p0038000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.4365264918961826, + "axis_minor_estimate": 0.6964109650387659, + "axis_x_abs": 0.6964109650387659, + "axis_y_abs": 1.4365264918961826, + "exact_major_axis": 1.4392190525454083, + "exact_minor_axis": 0.6948212631228, + "kinetic_energy": 7797.707446258537, + "p_max": 4204.864301278084, + "p_min": -4743.656305926694, + "particles": 31417, + "rho_max": 1.002131063362072, + "rho_min": 0.9975631121324761, + "time": 0.0038, + "x_max": 0.6964109650387648, + "x_min": -0.6964109650387659, + "y_max": 1.4365264918961822, + "y_min": -1.4365264918961826 + }, + "path": "/tmp/headline_nx100/headline-nx100-pysph-app-output/headline-nx100-pysph_01393.hdf5" + } + }, + "command": [ + "/home/kunalp/.pqt_venv_e0b41259/bin/python", + "pysph/examples/elliptical_drop_no_scheme.py", + "--nx", + "100", + "--tf", + "0.0038", + "--timestep", + "2.1090201168072683e-06", + "--adaptive-timestep", + "--cfl", + "0.3", + "--n-damp", + "50", + "--fname", + "headline-nx100-pysph", + "--directory", + "/tmp/headline_nx100/headline-nx100-pysph-app-output", + "--logfile", + "", + "--quiet" + ], + "dt_max": 2.780917055777183e-06, + "dt_mean": 2.7404840979225977e-06, + "dt_min": 2.2023818173548364e-06, + "final_time": 0.0038, + "output_dir": "/tmp/headline_nx100/headline-nx100-pysph-app-output", + "stdout_tail": "/home/kunalp/work/particles/pysph/pysph/solver/output.py:257: H5pyDeprecationWarning: Creating a dataset without passing data or dtype is deprecated. Pass an explicit dtype. Using dtype='f4' will keep the current default behaviour.\n prop = ptype_grp.create_dataset(propname, (0,))\nCreate our own solver.\nCreate our own equations.\nElliptical drop :: 31417 particles\n", + "steps": 1393, + "wall_time_s": 160.09861064099823 + }, + "hardware": { + "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "processor": "", + "python": "3.14.4", + "warp_device": "cuda:0", + "warp_version": "1.14.0" + }, + "performance": { + "cpu_average_step_time_s": 0.11493080448025716, + "cpu_wall_time_s": 160.09861064099823, + "speedup_wall_time": 23.610256518463927, + "warp_average_step_time_s": 0.004867833790385879, + "warp_wall_time_s": 6.7808924700075295 + }, + "warp": { + "average_step_time_s": 0.004867833790385879, + "backend": "warp", + "checkpoints": { + "t0p0008000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.0816634893417358, + "axis_minor_estimate": 0.9220717549324036, + "axis_x_abs": 0.9220717549324036, + "axis_y_abs": 1.0816634893417358, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.555910543130991e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.0831034701687434, + "exact_minor_axis": 0.9232728243814081, + "kinetic_energy": 7818.552961795891, + "p_max": 11072.9501953125, + "p_min": -836.334228515625, + "particles": 31417, + "rho_max": 1.0055550336837769, + "rho_min": 0.999572217464447, + "steps": 313, + "time": 0.0008, + "x_max": 0.9220717549324036, + "x_min": -0.9220715761184692, + "y_max": 1.0816634893417358, + "y_min": -1.0816634893417358 + }, + "path": "/tmp/headline_nx100/headline-nx100-warp-t0p0008000.npz" + }, + "t0p0038000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.4365259408950806, + "axis_minor_estimate": 0.6964127421379089, + "axis_x_abs": 0.6964127421379089, + "axis_y_abs": 1.4365259408950806, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.4392190525454083, + "exact_minor_axis": 0.6948212631228, + "kinetic_energy": 7797.7072262038255, + "p_max": 4207.072265625, + "p_min": -4745.986328125, + "particles": 31417, + "rho_max": 1.0021321773529053, + "rho_min": 0.9975618720054626, + "steps": 1393, + "time": 0.0038, + "x_max": 0.6964127421379089, + "x_min": -0.6964119672775269, + "y_max": 1.436525821685791, + "y_min": -1.4365259408950806 + }, + "path": "/tmp/headline_nx100/headline-nx100-warp-t0p0038000.npz" + } + }, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "final_time": 0.0038, + "steps": 1393, + "wall_time_s": 6.7808924700075295 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-cpu-gpu-grid-direct-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-cpu-gpu-grid-direct-summary.json new file mode 100644 index 000000000..a1ce324ba --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-cpu-gpu-grid-direct-summary.json @@ -0,0 +1,70 @@ +{ + "case": { + "optimization": "ADR-0004 grid-direct neighbor traversal; the fused continuity-density equation kernel and the adaptive CFL dt-factors kernel walk the uniform-grid cell list directly with the support cutoff inline, so the WCSPH continuity hot path builds no flat CSR neighbor list", + "baseline_commit": "429fa23e (ADR-0003 fused-equation, flat CSR neighbor cache)", + "dtype": "fp32 (compyle use_double=False)", + "kernel": "gaussian", + "density_mode": "continuity", + "radius_scale": 3.0 + }, + "million_segmented_profile": { + "nx": 565, + "particles": 1002885, + "steps": 12, + "warmup_discarded": 2, + "dt": 3.732778967800475e-07, + "mode": "fixed timestep", + "all_finite": true, + "flat_cache_builds_total": 0, + "grid_builds_per_step": 2.0, + "equation_launches_per_step": 2, + "step_wall_s_steady_range": [0.059156, 0.063873], + "grid_build_s_steady_range": [0.000379, 0.000692], + "equation_launch_s_steady_range_per_launch": [0.022992, 0.025071], + "kinetic_energy": 7854.127631112813, + "note": "build_neighbor_cache_gpu is never called on the continuity path; the only spatial index is the cheap cell-list build (~0.0005 s). The fused equation kernel absorbed the single neighbor traversal (cutoff walk), so its per-launch time rose vs the flat fused kernel, but eliminating the two flat build-traversals + readback + alloc nets a lower per-step wall." + }, + "comparison_vs_committed_flat_fused_429fa23e": { + "cache_builds_per_step": {"flat": 2, "grid_direct": 0}, + "flat_cache_build_s_per_step_range": [0.033743, 0.045973], + "grid_build_s_per_step_range_grid_direct": [0.000758, 0.001384], + "equation_kernel_s_per_step_range": {"flat_fused": [0.011272, 0.014], "grid_direct": [0.045984, 0.050142]}, + "step_wall_s_steady_range": {"flat_fused": [0.075883, 0.097561], "grid_direct": [0.059156, 0.063873]}, + "step_wall_reduction_approx": "~25-35%", + "kinetic_energy_delta_vs_flat_fused": -1.9917e-06, + "note": "Neighbor work goes from 3 traversals/half-stage (count + fill build, then 1 equation read) to 1 (the equation cutoff walk). Per-step wall is the meaningful metric; equation per-launch time rises because it now does the traversal that the flat build used to do separately." + }, + "adaptive_guard_nx100_resolved": { + "particles": 31417, + "warp_timestep_policy": "pysph", + "density_mode": "continuity", + "output_time": 0.0038, + "committed_steps": 1393, + "grid_direct_steps": 1393, + "grid_direct_all_finite": true, + "committed_warp_wall_s": 23.629107111992198, + "grid_direct_warp_wall_s": 7.824439332005568, + "deltas_vs_committed_warp_final_t0p0038": { + "kinetic_energy": 1.1933e-04, + "kinetic_energy_relative": 1.53e-08, + "axis_major": 2.384e-07, + "axis_minor": 6.557e-07, + "rho_min": -8.941e-07, + "rho_max": 2.384e-07 + }, + "note": "Step count identical to committed (1393); all deltas at fp32 reordering scale. Validates the grid-direct fused equation kernel AND the grid-direct dt-factors kernel over a long adaptive run. The grid dt-factors keeps the rij2>1e-12 inner guard and adds the support cutoff to reproduce the flat neighbor set." + }, + "tests": { + "focused_suite": "50 passed (test_warp_codegen.py + test_warp_sph.py + test_warp_nnps.py)", + "new_tests": [ + "test_grid_neighbor_mode_caches_distinctly_and_forces_geometry", + "test_grid_mode_kernel_sum_matches_reference_single_cell", + "test_warp_grid_direct_accel_matches_flat_fused", + "test_warp_continuity_step_builds_no_flat_neighbor_cache (repurposed)" + ] + }, + "hardware": { + "gpu": "NVIDIA GeForce RTX 4060 Laptop GPU (8 GiB, sm_89)", + "warp": "1.14.0" + } +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-segmented-grid-direct-profile.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-segmented-grid-direct-profile.json new file mode 100644 index 000000000..bd097d5b6 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-grid-direct/million-segmented-grid-direct-profile.json @@ -0,0 +1,39 @@ +{ + "all_finite": true, + "dt": 3.732778967800475e-07, + "equation_launch_s_steady_range": [ + 0.02299178799148649, + 0.02507138199871406 + ], + "equation_launches_total": 24, + "flat_cache_builds_total": 0, + "grid_build_s_steady_range": [ + 0.0003788399917539209, + 0.0006921150052221492 + ], + "grid_builds_per_step": 2.0, + "grid_builds_total": 24, + "kinetic_energy": 7854.127631112813, + "nx": 565, + "particles": 1002885, + "step_wall_s_all": [ + 0.144287, + 0.068555, + 0.062785, + 0.063623, + 0.063873, + 0.063535, + 0.060361, + 0.059156, + 0.060748, + 0.060081, + 0.059521, + 0.059179 + ], + "step_wall_s_steady_range": [ + 0.05915589100914076, + 0.0638731880026171 + ], + "steps": 12, + "warmup_discarded": 2 +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/profile_grid_direct_neighbors.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/profile_grid_direct_neighbors.py new file mode 100644 index 000000000..fd0772b9a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/profile_grid_direct_neighbors.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +"""Segmented per-step profile of the grid-direct continuity path (ADR-0004). + +Reuses the elliptical-drop initial condition from the Warp runner and steps the +continuity-density PEC integrator with fixed dt, instrumenting: + +- whether any flat CSR neighbor cache is built on the continuity path + (``build_neighbor_cache_gpu`` call count -- expected 0 under ADR-0004), +- the uniform-grid build cost (``_build_grid``, the remaining spatial index), +- the fused equation-kernel launch cost (``compute_wcsph_accel_continuity``), +- the steady-state per-step wall time. + +This is the ADR-0004 counterpart to the ADR-0003 fused-equation segmented +profile: there the flat cache build dominated (~0.034-0.046 s/step); here it +should disappear from the continuity path entirely. +""" + +from __future__ import annotations + +import argparse +import json +import time + +import numpy as np +import warp as wp + +import pysph.base.warp_sph as warp_sph +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_leapfrog_step +from warp_elliptical_drop_runner import WarpEllipticalDropRunner + + +def profile(nx, steps, warmup, dt): + runner = WarpEllipticalDropRunner( + nx=nx, steps=steps, dt=dt, rho0=1.0, c0=1400.0, p0=0.0, alpha=0.1, + beta=0.0, eos='tait', gamma=7.0, kernel='gaussian', xsph_eps=0.5, + density_mode='continuity', + ) + pa = runner.create_particles() + nnps = UniformGridWarpNNPS( + dim=2, particles=[pa], radius_scale=runner.radius_scale + ) + + counters = {'flat_cache_builds': 0, 'grid_builds': 0} + timers = {'grid_build_s': [], 'equation_s': []} + + flat_orig = nnps.build_neighbor_cache_gpu + grid_orig = nnps._build_grid + eqn_orig = warp_sph.compute_wcsph_accel_continuity + + def counted_flat(src_index, dst_index): + counters['flat_cache_builds'] += 1 + return flat_orig(src_index, dst_index) + + def timed_grid(src_index): + # _build_grid syncs internally; time the (possibly cached) call. + t0 = time.perf_counter() + out = grid_orig(src_index) + timers['grid_build_s'].append(time.perf_counter() - t0) + counters['grid_builds'] += 1 + return out + + def timed_eqn(*args, **kwargs): + t0 = time.perf_counter() + out = eqn_orig(*args, **kwargs) + timers['equation_s'].append(time.perf_counter() - t0) + return out + + nnps.build_neighbor_cache_gpu = counted_flat + nnps._build_grid = timed_grid + warp_sph.compute_wcsph_accel_continuity = timed_eqn + + step_walls = [] + try: + for s in range(steps): + wp.synchronize_device(nnps.device) + t0 = time.perf_counter() + wc_sph_leapfrog_step( + nnps, dt=dt, rho0=1.0, c0=1400.0, p0=0.0, alpha=0.1, beta=0.0, + eos='tait', gamma=7.0, kernel='gaussian', xsph_eps=0.5, + adaptive_dt=False, density_mode='continuity', return_dt=True, + ) + wp.synchronize_device(nnps.device) + step_walls.append(time.perf_counter() - t0) + finally: + warp_sph.compute_wcsph_accel_continuity = eqn_orig + + pa.gpu.pull('x', 'y', 'rho', 'u', 'v', 'au', 'av', 'arho') + finite = all( + np.all(np.isfinite(getattr(pa, n))) + for n in ('x', 'y', 'rho', 'u', 'v', 'au', 'av', 'arho') + ) + ke = 0.5 * float(np.sum(pa.m * (pa.u*pa.u + pa.v*pa.v))) + + steady = step_walls[warmup:] + grid_steady = timers['grid_build_s'][warmup:] + eqn_steady = timers['equation_s'][2 * warmup:] # 2 launches/step + + def rng(xs): + return [float(min(xs)), float(max(xs))] if xs else [0.0, 0.0] + + return { + 'particles': int(pa.get_number_of_particles()), + 'nx': nx, + 'steps': steps, + 'warmup_discarded': warmup, + 'dt': dt, + 'all_finite': bool(finite), + 'kinetic_energy': ke, + 'flat_cache_builds_total': counters['flat_cache_builds'], + 'grid_builds_total': counters['grid_builds'], + 'grid_builds_per_step': counters['grid_builds'] / steps, + 'equation_launches_total': len(timers['equation_s']), + 'step_wall_s_steady_range': rng(steady), + 'grid_build_s_steady_range': rng(grid_steady), + 'equation_launch_s_steady_range': rng(eqn_steady), + 'step_wall_s_all': [round(w, 6) for w in step_walls], + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--nx', type=int, default=565) + parser.add_argument('--steps', type=int, default=12) + parser.add_argument('--warmup', type=int, default=2) + parser.add_argument('--dt', type=float, default=3.732778967800475e-07) + parser.add_argument('--output', default=None) + args = parser.parse_args() + + result = profile(args.nx, args.steps, args.warmup, args.dt) + print(json.dumps(result, indent=2, sort_keys=True)) + if args.output is not None: + with open(args.output, 'w') as f: + json.dump(result, f, indent=2, sort_keys=True) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-continuity-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-continuity-summary.json new file mode 100644 index 000000000..a4f4f1c9c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-continuity-summary.json @@ -0,0 +1,215 @@ +{ + "case": { + "alpha": 0.1, + "beta": 0.0, + "c0": 1400.0, + "cfl": 0.3, + "dt": 2.1090201168072683e-06, + "gamma": 7.0, + "hdx": 1.3, + "n_damp": 50, + "nx": 100, + "output_times": [ + 0.0008, + 0.0038 + ], + "p0": 0.0, + "rho0": 1.0, + "warp_density_mode": "continuity", + "xsph_eps": 0.5 + }, + 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Using dtype='f4' will keep the current default behaviour.\n prop = ptype_grp.create_dataset(propname, (0,))\nCreate our own solver.\nCreate our own equations.\nElliptical drop :: 31417 particles\n", + "steps": 1393, + "wall_time_s": 228.25765374601178 + }, + "hardware": { + "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "processor": "", + "python": "3.14.4", + "warp_device": "cuda:0", + "warp_version": "1.14.0" + }, + "performance": { + "cpu_average_step_time_s": 0.16386048366547867, + "cpu_wall_time_s": 228.25765374601178, + "speedup_wall_time": 7.606547218180963, + "warp_average_step_time_s": 0.01663417438525551, + "warp_wall_time_s": 30.008050591000938 + }, + "warp": { + "average_step_time_s": 0.01663417438525551, + "backend": "warp", + "checkpoints": { + "t0p0008000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.0816640853881836, + "axis_minor_estimate": 0.9220711588859558, + "axis_x_abs": 0.9220711588859558, + "axis_y_abs": 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Pass an explicit dtype. Using dtype='f4' will keep the current default behaviour.\n prop = ptype_grp.create_dataset(propname, (0,))\nCreate our own solver.\nCreate our own equations.\nElliptical drop :: 31417 particles\n", + "steps": 1393, + "wall_time_s": 233.97314716299297 + }, + "hardware": { + "platform": "Linux-6.6.114.1-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "processor": "", + "python": "3.14.4", + "warp_device": "cuda:0", + "warp_version": "1.14.0" + }, + "performance": { + "cpu_average_step_time_s": 0.16796349401507032, + "cpu_wall_time_s": 233.97314716299297, + "speedup_wall_time": 9.901903870258701, + "warp_average_step_time_s": 0.016962747388364823, + "warp_wall_time_s": 23.629107111992198 + }, + "warp": { + "average_step_time_s": 0.016962747388364823, + "backend": "warp", + "checkpoints": { + "t0p0008000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.0816634893417358, + "axis_minor_estimate": 0.9220717549324036, + "axis_x_abs": 0.9220717549324036, + "axis_y_abs": 1.0816634893417358, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.555910543130991e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.0831034701687434, + "exact_minor_axis": 0.9232728243814081, + "kinetic_energy": 7818.5528883068255, + "p_max": 11072.9501953125, + "p_min": -836.801513671875, + "particles": 31417, + "rho_max": 1.0055550336837769, + "rho_min": 0.9995719790458679, + "steps": 313, + "time": 0.0008, + "x_max": 0.9220716953277588, + "x_min": -0.9220717549324036, + "y_max": 1.0816634893417358, + "y_min": -1.0816634893417358 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-warp-t0p0008000.npz" + }, + "t0p0038000": { + "metrics": { + "all_finite": true, + "axis_major_estimate": 1.4365261793136597, + "axis_minor_estimate": 0.6964123249053955, + "axis_x_abs": 0.6964123249053955, + "axis_y_abs": 1.4365261793136597, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "exact_major_axis": 1.4392190525454083, + "exact_minor_axis": 0.6948212631228, + "kinetic_energy": 7797.707014434469, + "p_max": 4207.072265625, + "p_min": -4745.185546875, + "particles": 31417, + "rho_max": 1.0021320581436157, + "rho_min": 0.9975622892379761, + "steps": 1393, + "time": 0.0038, + "x_max": 0.6964123249053955, + "x_min": -0.6964117884635925, + "y_max": 1.4365261793136597, + "y_min": -1.4365261793136597 + }, + "path": ".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-warp-t0p0038000.npz" + } + }, + "dt_max": 2.7813784981844947e-06, + "dt_mean": 2.727925340990668e-06, + "dt_min": 2.7459356128852786e-09, + "final_time": 0.0038, + "steps": 1393, + "wall_time_s": 23.629107111992198 + } +} \ No newline at end of file diff --git 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b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0038000.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py new file mode 100644 index 000000000..ee3b26fe5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py @@ -0,0 +1,463 @@ +#!/usr/bin/env python3 +"""Resolved elliptical-drop CPU/Warp performance and result comparison.""" + +from __future__ import annotations + +import argparse +import json +import platform +import subprocess +import sys +import time +from pathlib import Path + +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +import numpy as np + +from pysph.examples.elliptical_drop import exact_solution +from pysph.solver.utils import load +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_leapfrog_step + +from warp_elliptical_drop_runner import WarpEllipticalDropRunner + + +def _initial_dt(nx, hdx, c0): + dx = 1.0 / nx + return 0.25 * hdx * dx / (141.0 + c0) + + +def _checkpoint_label(t): + return f't{t:.7f}'.replace('.', 'p') + + +def _pysph_damping_factor(count, n_damp): + if count < n_damp and n_damp > 0: + fraction = (count + 1) / float(n_damp) + return 0.5 * (np.sin(np.pi * (-0.5 + fraction)) + 1.0) + return 1.0 + + +def _warp_timestep_controls(args, t, target, step): + remaining = target - t + if args.warp_timestep_policy == 'current': + return min(args.dt, remaining), 1.0, np.inf + if args.warp_timestep_policy != 'pysph': + raise ValueError( + "warp_timestep_policy must be 'current' or 'pysph'" + ) + damping = _pysph_damping_factor(step, args.n_damp) + return args.warp_dt_max, damping, remaining + + +def _metrics_from_arrays(arrays, solver_time, dt_history=None): + x = arrays['x'] + y = arrays['y'] + m = arrays['m'] + rho = arrays['rho'] + p = arrays['p'] + u = arrays['u'] + v = arrays['v'] + speed2 = u*u + v*v + a, _A, _po, _xe, _ye = exact_solution(solver_time) + major = 1.0 / a + axis_x = max(abs(float(np.min(x))), abs(float(np.max(x)))) + axis_y = max(abs(float(np.min(y))), abs(float(np.max(y)))) + result = { + 'all_finite': bool(all(np.all(np.isfinite(arrays[name])) + for name in ('x', 'y', 'rho', 'p', 'u', 'v'))), + 'particles': int(x.size), + 'time': float(solver_time), + 'x_min': float(np.min(x)), + 'x_max': float(np.max(x)), + 'y_min': float(np.min(y)), + 'y_max': float(np.max(y)), + 'axis_x_abs': axis_x, + 'axis_y_abs': axis_y, + 'axis_major_estimate': max(axis_x, axis_y), + 'axis_minor_estimate': min(axis_x, axis_y), + 'exact_major_axis': float(major), + 'exact_minor_axis': float(a), + 'rho_min': float(np.min(rho)), + 'rho_max': float(np.max(rho)), + 'p_min': float(np.min(p)), + 'p_max': float(np.max(p)), + 'kinetic_energy': float(0.5 * np.sum(m * speed2)), + } + if dt_history is not None and len(dt_history) > 0: + dts = np.asarray(dt_history) + result.update({ + 'steps': int(dts.size), + 'dt_min': float(np.min(dts)), + 'dt_max': float(np.max(dts)), + 'dt_mean': float(np.mean(dts)), + }) + return result + + +def _save_warp_checkpoint(path, pa, solver_time, dt_history): + pull_props = [ + 'x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', 'u', 'v', 'w', + 'au', 'av', 'aw' + ] + for optional in ('ax', 'ay', 'az', 'arho', 'dt_cfl', 'dt_force'): + if optional in pa.properties: + pull_props.append(optional) + pa.gpu.pull(*pull_props) + arrays = {name: getattr(pa, name).copy() for name in pull_props} + metrics = _metrics_from_arrays(arrays, solver_time, dt_history) + np.savez( + path, + **arrays, + dt_history=np.asarray(dt_history), + solver_time=np.asarray([solver_time]), + metrics=json.dumps(metrics, sort_keys=True), + ) + return metrics + + +def _run_warp(args, out_dir, output_times): + runner = WarpEllipticalDropRunner( + nx=args.nx, steps=1, dt=args.dt, rho0=args.rho0, c0=args.c0, + p0=args.p0, hdx=args.hdx, alpha=args.alpha, beta=args.beta, + eos='tait', gamma=args.gamma, kernel='gaussian', xsph_eps=args.xsph_eps, + adaptive_dt=True, cfl=args.cfl, dt_min=args.dt_min, + dt_max=args.warp_dt_max, density_mode=args.warp_density_mode, + ) + pa = runner.create_particles() + nnps = UniformGridWarpNNPS( + dim=2, particles=[pa], radius_scale=runner.radius_scale + ) + dt_history = [] + checkpoints = {} + target_index = 0 + t = 0.0 + step = 0 + start = time.perf_counter() + while target_index < len(output_times): + target = output_times[target_index] + remaining = target - t + if remaining <= args.time_epsilon: + label = _checkpoint_label(target) + path = out_dir / f'{args.prefix}-warp-{label}.npz' + metrics = _save_warp_checkpoint(path, pa, t, dt_history) + checkpoints[label] = {'path': str(path), 'metrics': metrics} + target_index += 1 + continue + dt_cap, dt_scale, step_dt_max = _warp_timestep_controls( + args, t, target, step + ) + _result, dt_used = wc_sph_leapfrog_step( + nnps, dt=args.dt, rho0=args.rho0, c0=args.c0, p0=args.p0, + alpha=args.alpha, beta=args.beta, eos='tait', gamma=args.gamma, + kernel='gaussian', xsph_eps=args.xsph_eps, adaptive_dt=True, + cfl=args.cfl, dt_min=args.dt_min, dt_max=dt_cap, return_dt=True, + density_mode=args.warp_density_mode, + adaptive_dt_scale=dt_scale, step_dt_max=step_dt_max, + ) + dt_history.append(dt_used) + t += dt_used + step += 1 + if step >= args.max_steps: + raise RuntimeError( + f"Warp exceeded --max-steps={args.max_steps} at t={t}" + ) + wall = time.perf_counter() - start + return { + 'backend': 'warp', + 'wall_time_s': wall, + 'steps': step, + 'average_step_time_s': wall / step if step else 0.0, + 'final_time': t, + 'dt_min': float(np.min(dt_history)), + 'dt_max': float(np.max(dt_history)), + 'dt_mean': float(np.mean(dt_history)), + 'checkpoints': checkpoints, + } + + +def _run_pysph_application(args, out_dir, output_times): + app_dir = out_dir / f'{args.prefix}-pysph-app-output' + app_dir.mkdir(parents=True, exist_ok=True) + command = [ + sys.executable, + 'pysph/examples/elliptical_drop_no_scheme.py', + '--nx', str(args.nx), + '--tf', str(max(output_times)), + '--timestep', str(args.dt), + '--adaptive-timestep', + '--cfl', str(args.cfl), + '--n-damp', str(args.n_damp), + '--fname', f'{args.prefix}-pysph', + '--directory', str(app_dir), + '--logfile', '', + '--quiet', + ] + start = time.perf_counter() + proc = subprocess.run( + command, + cwd=Path(__file__).resolve().parents[5], + text=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + wall = time.perf_counter() - start + if proc.returncode != 0: + raise RuntimeError( + 'PySPH Application baseline failed with exit code ' + f'{proc.returncode}\n{proc.stdout}' + ) + files = sorted(app_dir.glob(f'{args.prefix}-pysph_*.npz')) + files.extend(sorted(app_dir.glob(f'{args.prefix}-pysph_*.hdf5'))) + checkpoints = {} + dt_values = [] + for file_path in files: + data = load(str(file_path)) + solver_data = data['solver_data'] + solver_time = float(solver_data['t']) + if solver_time > 0.0: + dt_values.append(float(solver_data['dt'])) + for target in output_times: + if abs(solver_time - target) <= args.output_tolerance: + label = _checkpoint_label(target) + pa = data['arrays']['fluid'] + arrays = { + name: getattr(pa, name) + for name in ('x', 'y', 'h', 'm', 'rho', 'p', 'u', 'v') + } + metrics = _metrics_from_arrays(arrays, solver_time) + checkpoints[label] = { + 'path': str(file_path), + 'metrics': metrics, + } + missing = [ + _checkpoint_label(t) for t in output_times + if _checkpoint_label(t) not in checkpoints + ] + if missing: + raise RuntimeError( + f"PySPH Application missing checkpoints {missing}; files={files}" + ) + steps = int(max((load(str(p))['solver_data']['count'] for p in files), + default=0)) + summary = { + 'backend': 'pysph-application', + 'command': command, + 'wall_time_s': wall, + 'steps': steps, + 'average_step_time_s': wall / steps if steps else 0.0, + 'final_time': max(output_times), + 'stdout_tail': proc.stdout[-4000:], + 'output_dir': str(app_dir), + 'checkpoints': checkpoints, + } + if dt_values: + summary.update({ + 'dt_min': float(np.min(dt_values)), + 'dt_max': float(np.max(dt_values)), + 'dt_mean': float(np.mean(dt_values)), + }) + return summary + + +def _checkpoint_arrays(path): + if '-pysph_' in Path(path).name: + data = load(str(path)) + pa = data['arrays']['fluid'] + return { + name: np.asarray(getattr(pa, name), dtype=np.float64).copy() + for name in ('x', 'y', 'rho', 'u', 'v') + } + data = np.load(path) + return { + name: np.asarray(data[name], dtype=np.float64).copy() + for name in ('x', 'y', 'rho', 'u', 'v') + } + + +def _plot_indices(n, max_points=20000): + if n <= max_points: + return np.arange(n) + return np.linspace(0, n - 1, max_points, dtype=np.int64) + + +def _plot_checkpoint(cpu_path, warp_path, image_path, solver_time): + cpu = _checkpoint_arrays(cpu_path) + warp = _checkpoint_arrays(warp_path) + cpu_speed = np.sqrt(cpu['u']*cpu['u'] + cpu['v']*cpu['v']) + warp_speed = np.sqrt(warp['u']*warp['u'] + warp['v']*warp['v']) + vmax = max(float(cpu_speed.max()), float(warp_speed.max())) + xmin = min(float(cpu['x'].min()), float(warp['x'].min())) + xmax = max(float(cpu['x'].max()), float(warp['x'].max())) + ymin = min(float(cpu['y'].min()), float(warp['y'].min())) + ymax = max(float(cpu['y'].max()), float(warp['y'].max())) + pad = 0.05 * max(xmax - xmin, ymax - ymin) + _a, _A, _po, xe, ye = exact_solution(solver_time) + cpu_idx = _plot_indices(cpu['x'].size) + warp_idx = _plot_indices(warp['x'].size) + fig, axes = plt.subplots(1, 2, figsize=(10, 4.6), constrained_layout=True) + for ax, data, speed, title, idx in ( + (axes[0], cpu, cpu_speed, 'PySPH CPU Application', cpu_idx), + (axes[1], warp, warp_speed, 'Warp GPU', warp_idx), + ): + sc = ax.scatter( + data['x'][idx], data['y'][idx], c=speed[idx], s=1.5, vmin=0.0, + vmax=vmax, cmap='viridis', linewidths=0.0, rasterized=True, + ) + ax.plot(xe, ye, color='black', linewidth=0.75) + ax.set_title(title) + ax.set_aspect('equal', adjustable='box') + ax.set_xlim(xmin - pad, xmax + pad) + ax.set_ylim(ymin - pad, ymax + pad) + ax.set_xlabel('x') + ax.set_ylabel('y') + fig.colorbar(sc, ax=axes, label='speed') + fig.savefig(image_path, dpi=180) + plt.close(fig) + + +def _metric_deltas(cpu, warp): + deltas = {} + for key in ( + 'axis_major_estimate', 'axis_minor_estimate', 'rho_min', 'rho_max', + 'kinetic_energy', 'x_min', 'x_max', 'y_min', 'y_max' + ): + deltas[key] = float(warp[key] - cpu[key]) + return deltas + + +def _hardware_summary(): + summary = { + 'platform': platform.platform(), + 'processor': platform.processor(), + 'python': sys.version.split()[0], + } + try: + import warp as wp + except Exception as exc: # pragma: no cover + summary['warp_error'] = repr(exc) + else: + summary['warp_version'] = getattr(wp, '__version__', 'unknown') + try: + summary['warp_device'] = str(wp.get_device()) + except Exception as exc: # pragma: no cover + summary['warp_device_error'] = repr(exc) + return summary + + +def _parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--nx', type=int, default=100) + parser.add_argument('--rho0', type=float, default=1.0) + parser.add_argument('--c0', type=float, default=1400.0) + parser.add_argument('--p0', type=float, default=0.0) + parser.add_argument('--hdx', type=float, default=1.3) + parser.add_argument('--alpha', type=float, default=0.1) + parser.add_argument('--beta', type=float, default=0.0) + parser.add_argument('--gamma', type=float, default=7.0) + parser.add_argument('--xsph-eps', type=float, default=0.5) + parser.add_argument('--cfl', type=float, default=0.3) + parser.add_argument('--n-damp', type=int, default=50) + parser.add_argument('--dt', type=float, default=None) + parser.add_argument('--dt-min', type=float, default=1.0e-10) + parser.add_argument('--warp-dt-max', type=float, default=np.inf) + parser.add_argument('--output-times', default='0.0008,0.0038') + parser.add_argument('--output-tolerance', type=float, default=1.0e-10) + parser.add_argument('--time-epsilon', type=float, default=1.0e-14) + parser.add_argument('--max-steps', type=int, default=10000000) + parser.add_argument('--output-dir', default=None) + parser.add_argument('--prefix', default='resolved-nx100-continuity') + parser.add_argument('--warp-density-mode', + choices=('continuity', 'summation'), + default='continuity') + parser.add_argument('--warp-timestep-policy', + choices=('pysph', 'current'), default='pysph') + parser.add_argument('--skip-pysph-application', action='store_true') + return parser.parse_args() + + +def main(): + args = _parse_args() + if args.dt is None: + args.dt = _initial_dt(args.nx, args.hdx, args.c0) + output_times = sorted( + float(item.strip()) for item in args.output_times.split(',') + if item.strip() + ) + out_dir = ( + Path(args.output_dir) if args.output_dir is not None + else Path(__file__).parent / 'resolved' + ) + out_dir.mkdir(parents=True, exist_ok=True) + + summary = { + 'case': { + 'nx': args.nx, + 'rho0': args.rho0, + 'c0': args.c0, + 'p0': args.p0, + 'hdx': args.hdx, + 'alpha': args.alpha, + 'beta': args.beta, + 'gamma': args.gamma, + 'xsph_eps': args.xsph_eps, + 'cfl': args.cfl, + 'n_damp': args.n_damp, + 'dt': args.dt, + 'warp_dt_max': args.warp_dt_max, + 'warp_density_mode': args.warp_density_mode, + 'warp_timestep_policy': args.warp_timestep_policy, + 'output_times': output_times, + }, + 'hardware': _hardware_summary(), + } + if not args.skip_pysph_application: + summary['cpu'] = _run_pysph_application(args, out_dir, output_times) + summary['warp'] = _run_warp(args, out_dir, output_times) + + comparisons = {} + if 'cpu' in summary: + for target in output_times: + label = _checkpoint_label(target) + cpu_checkpoint = summary['cpu']['checkpoints'][label] + warp_checkpoint = summary['warp']['checkpoints'][label] + image_path = out_dir / f'{args.prefix}-{label}.png' + _plot_checkpoint( + cpu_checkpoint['path'], warp_checkpoint['path'], image_path, + target, + ) + comparisons[label] = { + 'time': target, + 'image': str(image_path), + 'deltas': _metric_deltas( + cpu_checkpoint['metrics'], warp_checkpoint['metrics'] + ), + } + summary['comparisons'] = comparisons + cpu_time = summary['cpu']['wall_time_s'] + warp_time = summary['warp']['wall_time_s'] + summary['performance'] = { + 'speedup_wall_time': cpu_time / warp_time if warp_time else None, + 'cpu_wall_time_s': cpu_time, + 'warp_wall_time_s': warp_time, + 'cpu_average_step_time_s': summary['cpu']['average_step_time_s'], + 'warp_average_step_time_s': summary['warp']['average_step_time_s'], + } + + summary_path = out_dir / f'{args.prefix}-summary.json' + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True)) + print(json.dumps(summary, indent=2, sort_keys=True)) + if 'cpu' in summary: + for checkpoint in summary['cpu']['checkpoints'].values(): + if not checkpoint['metrics']['all_finite']: + raise SystemExit("CPU checkpoint contains non-finite values") + for checkpoint in summary['warp']['checkpoints'].values(): + if not checkpoint['metrics']['all_finite']: + raise SystemExit("Warp checkpoint contains non-finite values") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh new file mode 100755 index 000000000..f5b12821d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +if [ -z "${VIRTUAL_ENV:-}" ]; then + source "$HOME/prediqt/activate" +fi + +export ZOLTAN="${ZOLTAN:-$HOME/prediqt/zoltan}" +export LD_LIBRARY_PATH="$ZOLTAN/lib:${LD_LIBRARY_PATH:-}" + +cd "$ROOT" + +OUT=".ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-smoke.npz" + +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py \ + --nx 8 \ + --steps 2 \ + --dt 1.0e-5 \ + --rho0 1.0 \ + --c0 20.0 \ + --p0 0.0 \ + --alpha 0.1 \ + --beta 0.0 \ + --eos tait \ + --gamma 7.0 \ + --kernel gaussian \ + --xsph-eps 0.5 \ + --adaptive-dt \ + --cfl 0.25 \ + --dt-min 1.0e-7 \ + --dt-max 1.0e-5 \ + --output "$OUT" + +test -s "$OUT" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py new file mode 100644 index 000000000..08270dc69 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +"""Application-style Warp runner for the elliptical-drop initial condition.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_leapfrog_step + + +class WarpEllipticalDropRunner: + """Small runner around the current Warp WCSPH prototype. + + This intentionally does not use PySPH's Application/Solver stack yet. It + gives the Warp kernels a repeatable elliptical-drop-style workload while + the missing WCSPH terms are still being ported. + """ + + def __init__(self, nx=8, steps=2, dt=1.0e-5, rho0=1.0, c0=20.0, + p0=0.0, hdx=1.3, alpha=0.1, beta=0.0, eos='tait', + gamma=7.0, kernel='gaussian', radius_scale=None, + xsph_eps=0.5, adaptive_dt=False, cfl=0.25, dt_min=0.0, + dt_max=None, density_mode='summation', output=None): + self.nx = int(nx) + self.steps = int(steps) + self.dt = float(dt) + self.rho0 = float(rho0) + self.c0 = float(c0) + self.p0 = float(p0) + self.hdx = float(hdx) + self.alpha = float(alpha) + self.beta = float(beta) + self.eos = eos + self.gamma = float(gamma) + self.kernel = kernel + self.radius_scale = ( + 3.0 if kernel == 'gaussian' and radius_scale is None + else 2.0 if radius_scale is None + else float(radius_scale) + ) + self.xsph_eps = None if xsph_eps is None else float(xsph_eps) + self.adaptive_dt = bool(adaptive_dt) + self.cfl = float(cfl) + self.dt_min = float(dt_min) + self.dt_max = self.dt if dt_max is None else float(dt_max) + self.density_mode = density_mode + self.dx = 1.0 / self.nx + self.output = Path(output) if output is not None else None + self.dt_history = [] + + def create_particles(self): + dx = self.dx + x, y = np.mgrid[-1.05:1.05 + 1.0e-4:dx, + -1.05:1.05 + 1.0e-4:dx] + condition = ~((x*x + y*y - 1.0) > 1.0e-10) + x = np.asarray(x[condition].ravel(), dtype=np.float64) + y = np.asarray(y[condition].ravel(), dtype=np.float64) + z = np.zeros_like(x) + + m = np.ones_like(x) * dx * dx * self.rho0 + h = np.ones_like(x) * self.hdx * dx + rho = np.ones_like(x) * self.rho0 + p = np.zeros_like(x) + cs = np.ones_like(x) * self.c0 + u = -100.0 * x + v = 100.0 * y + w = np.zeros_like(x) + au = np.zeros_like(x) + av = np.zeros_like(x) + aw = np.zeros_like(x) + arho = np.zeros_like(x) + ax = np.zeros_like(x) + ay = np.zeros_like(x) + az = np.zeros_like(x) + x0 = np.zeros_like(x) + y0 = np.zeros_like(x) + z0 = np.zeros_like(x) + u0 = np.zeros_like(x) + v0 = np.zeros_like(x) + w0 = np.zeros_like(x) + rho_ref = np.zeros_like(x) + + return get_particle_array( + name='fluid', x=x, y=y, z=z, h=h, m=m, rho=rho, p=p, + cs=cs, u=u, v=v, w=w, au=au, av=av, aw=aw, arho=arho, + ax=ax, ay=ay, az=az, x0=x0, y0=y0, z0=z0, u0=u0, v0=v0, + w0=w0, rho0=rho_ref, backend='warp' + ) + + def run(self): + pa = self.create_particles() + nnps = UniformGridWarpNNPS( + dim=2, particles=[pa], radius_scale=self.radius_scale + ) + + time = 0.0 + for _ in range(self.steps): + _, dt_used = wc_sph_leapfrog_step( + nnps, dt=self.dt, rho0=self.rho0, c0=self.c0, p0=self.p0, + alpha=self.alpha, beta=self.beta, eos=self.eos, + gamma=self.gamma, kernel=self.kernel, xsph_eps=self.xsph_eps, + adaptive_dt=self.adaptive_dt, cfl=self.cfl, + dt_min=self.dt_min, dt_max=self.dt_max, return_dt=True, + density_mode=self.density_mode + ) + self.dt_history.append(dt_used) + time += dt_used + + pull_props = [ + 'x', 'y', 'z', 'rho', 'p', 'cs', 'u', 'v', 'w', 'au', 'av', 'aw' + ] + for optional in ('ax', 'ay', 'az', 'arho', 'dt_cfl', 'dt_force'): + if optional in pa.properties: + pull_props.append(optional) + pa.gpu.pull(*pull_props) + metrics = self._metrics(pa, time) + if self.output is not None: + self._write_output(pa, metrics) + return metrics + + def _metrics(self, pa, time): + finite_props = [ + 'x', 'y', 'z', 'rho', 'p', 'cs', 'u', 'v', 'w', 'au', 'av', 'aw' + ] + finite_props.extend( + name for name in ('ax', 'ay', 'az', 'arho', 'dt_cfl', 'dt_force') + if name in pa.properties + ) + finite = all(np.all(np.isfinite(getattr(pa, name))) + for name in finite_props) + ke = 0.5 * np.sum(pa.m * (pa.u*pa.u + pa.v*pa.v + pa.w*pa.w)) + radius = np.sqrt(pa.x*pa.x + pa.y*pa.y) + dt_history = np.asarray(self.dt_history) + return { + 'particles': int(pa.get_number_of_particles()), + 'steps': self.steps, + 'dt': self.dt, + 'dt_min_used': float(np.min(dt_history)), + 'dt_max_used': float(np.max(dt_history)), + 'dt_last': float(dt_history[-1]), + 'time': float(time), + 'nx': self.nx, + 'rho_min': float(np.min(pa.rho)), + 'rho_max': float(np.max(pa.rho)), + 'c0': self.c0, + 'eos': self.eos, + 'gamma': self.gamma, + 'alpha': self.alpha, + 'beta': self.beta, + 'kernel': self.kernel, + 'radius_scale': self.radius_scale, + 'xsph_eps': self.xsph_eps, + 'adaptive_dt': self.adaptive_dt, + 'density_mode': self.density_mode, + 'cfl': self.cfl, + 'dt_min': self.dt_min, + 'dt_max': self.dt_max, + 'p_min': float(np.min(pa.p)), + 'p_max': float(np.max(pa.p)), + 'cs_min': float(np.min(pa.cs)), + 'cs_max': float(np.max(pa.cs)), + 'x_min': float(np.min(pa.x)), + 'x_max': float(np.max(pa.x)), + 'y_min': float(np.min(pa.y)), + 'y_max': float(np.max(pa.y)), + 'radius_max': float(np.max(radius)), + 'kinetic_energy': float(ke), + 'all_finite': bool(finite), + } + + def _write_output(self, pa, metrics): + self.output.parent.mkdir(parents=True, exist_ok=True) + optional = {} + for name in ('ax', 'ay', 'az', 'arho', 'dt_cfl', 'dt_force'): + if name in pa.properties: + optional[name] = getattr(pa, name) + np.savez( + self.output, + x=pa.x, y=pa.y, z=pa.z, h=pa.h, m=pa.m, rho=pa.rho, p=pa.p, + cs=pa.cs, u=pa.u, v=pa.v, w=pa.w, au=pa.au, av=pa.av, aw=pa.aw, + dt_history=np.asarray(self.dt_history), + **optional, + metrics=json.dumps(metrics, sort_keys=True), + ) + + +def _parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--nx', type=int, default=8) + parser.add_argument('--steps', type=int, default=2) + parser.add_argument('--dt', type=float, default=1.0e-5) + parser.add_argument('--rho0', type=float, default=1.0) + parser.add_argument('--c0', type=float, default=20.0) + parser.add_argument('--p0', type=float, default=0.0) + parser.add_argument('--hdx', type=float, default=1.3) + parser.add_argument('--alpha', type=float, default=0.1) + parser.add_argument('--beta', type=float, default=0.0) + parser.add_argument('--eos', choices=('isothermal', 'tait'), + default='tait') + parser.add_argument('--gamma', type=float, default=7.0) + parser.add_argument('--kernel', choices=('cubic', 'gaussian'), + default='gaussian') + parser.add_argument('--radius-scale', type=float, default=None) + parser.add_argument('--xsph-eps', type=float, default=0.5) + parser.add_argument('--no-xsph', action='store_true') + parser.add_argument('--adaptive-dt', action='store_true') + parser.add_argument('--cfl', type=float, default=0.25) + parser.add_argument('--dt-min', type=float, default=0.0) + parser.add_argument('--dt-max', type=float, default=None) + parser.add_argument('--density-mode', choices=('summation', 'continuity'), + default='summation') + parser.add_argument('--output', default=None) + return parser.parse_args() + + +def main(): + args = _parse_args() + runner = WarpEllipticalDropRunner( + nx=args.nx, steps=args.steps, dt=args.dt, rho0=args.rho0, + c0=args.c0, p0=args.p0, hdx=args.hdx, alpha=args.alpha, + beta=args.beta, eos=args.eos, gamma=args.gamma, kernel=args.kernel, + radius_scale=args.radius_scale, + xsph_eps=None if args.no_xsph else args.xsph_eps, + adaptive_dt=args.adaptive_dt, cfl=args.cfl, dt_min=args.dt_min, + dt_max=args.dt_max, density_mode=args.density_mode, + output=args.output + ) + metrics = runner.run() + print(json.dumps(metrics, indent=2, sort_keys=True)) + if metrics['particles'] <= 0: + raise SystemExit("No particles were created") + if not metrics['all_finite']: + raise SystemExit("Non-finite values in final state") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-3d-snapshot.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-3d-snapshot.png new file mode 100644 index 000000000..32282f311 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-3d-snapshot.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-perf.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-perf.json new file mode 100644 index 000000000..45869071f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-perf.json @@ -0,0 +1,24 @@ +{ + "c0": 32.849657532461435, + "case": "Lobovsky 3D dam-break, no obstacle (~1M particles)", + "cpu_pysph_fp64": { + "particle_steps_per_s": 239131.64494557082, + "s_per_step": 4.181692474149986, + "solve_s": 83.63384948299972, + "steps": 20 + }, + "device": "RTX 4060 Laptop (fp32) vs single-thread PySPH Cython (fp64)", + "dx": 0.011, + "kernel": "wendland", + "n_fluid": 733050, + "n_total": 999975, + "n_wall": 266925, + "snapshot_image": "/home/kunalp/work/particles/pysph/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1M-3d-snapshot.png", + "snapshot_t": 0.20000278010831346, + "speedup_per_step": 10.101015329561102, + "warp_gpu_fp32": { + "particle_steps_per_s": 2415472.4113783734, + "s_per_step": 0.4139873406500101, + "steps": 20 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-fused-perf.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-fused-perf.json new file mode 100644 index 000000000..a6b9be654 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-fused-perf.json @@ -0,0 +1,22 @@ +{ + "c0": 32.849657532461435, + "case": "Lobovsky 3D dam-break, no obstacle (~1M particles)", + "cpu_pysph_fp64": { + "particle_steps_per_s": 200835.71135595912, + "s_per_step": 5.049261374649996, + "solve_s": 100.98522749299991, + "steps": 20 + }, + "device": "RTX 4060 Laptop (fp32) vs single-thread PySPH Cython (fp64)", + "dx": 0.0108, + "kernel": "wendland", + "n_fluid": 799940, + "n_total": 1014072, + "n_wall": 214132, + "speedup_per_step": 14.969771031846312, + "warp_gpu_fp32": { + "particle_steps_per_s": 3006464.6140166847, + "s_per_step": 0.33729716799998644, + "steps": 20 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-perf.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-perf.json new file mode 100644 index 000000000..2f4793c00 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench-1Mplus-perf.json @@ -0,0 +1,22 @@ +{ + "c0": 32.849657532461435, + "case": "Lobovsky 3D dam-break, no obstacle (~1M particles)", + "cpu_pysph_fp64": { + "particle_steps_per_s": 224930.86884339087, + "s_per_step": 4.508371862049989, + "solve_s": 90.16743724099979, + "steps": 20 + }, + "device": "RTX 4060 Laptop (fp32) vs single-thread PySPH Cython (fp64)", + "dx": 0.0108, + "kernel": "wendland", + "n_fluid": 799940, + "n_total": 1014072, + "n_wall": 214132, + "speedup_per_step": 10.875212514350949, + "warp_gpu_fp32": { + "particle_steps_per_s": 2446170.9997094763, + "s_per_step": 0.4145548287999645, + "steps": 20 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench_1M_and_3d_snapshot.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench_1M_and_3d_snapshot.py new file mode 100644 index 000000000..36c94602e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/bench_1M_and_3d_snapshot.py @@ -0,0 +1,247 @@ +#!/usr/bin/env python3 +"""~1M-particle perf comparison + a 3D-explicit developed snapshot. + +Two deliverables for the 3D dam-break (Lobovsky no-obstacle): + +1. **Perf at ~1M particles** -- per-step throughput of the default PySPH CPU + Application (fp64, single-threaded Cython) vs the Warp `wc_sph_dam_break_step` + (fp32 GPU), each over a fixed short step count. Per-step / particle-steps-per- + second is the meaningful large-N metric (a *developed* 1M run to a physical + `tf` is multi-hour on the CPU, so it is intentionally not attempted -- matching + how the committed 1M elliptical comparison was measured). + +2. **A 3D-explicit snapshot** of the developed Warp 1M state (x-z side view, x-y + top-down, and a 3D scatter), so the fully-3D nature of the simulation is + unmistakable (the simulation has many particle layers across the channel + width `y`; the earlier single x-z panel only projected them). + +The perf JSON is printed and written BEFORE the (longer) developed-snapshot phase +so the headline number is captured regardless of the render phase. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import tempfile +import time +from pathlib import Path + +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D # noqa: F401 (registers 3d projection) +import numpy as np + +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_dam_break_step +from dam_break_3d_runner import REF_C0, WarpDamBreak3DRunner, damp_factor + +EXAMPLE = 'pysph.examples.dam_break.dam_break_3d_lobovsky' + + +def cpu_per_step(dx, steps, outdir): + """Time the PySPH Application solve loop over a fixed step count at ~1M.""" + code = ( + "import time, json\n" + "from {mod} import DamBreak3D\n" + "app = DamBreak3D()\n" + "app.setup(argv={argv!r})\n" + "t0 = time.perf_counter()\n" + "app.solve()\n" + "dt = time.perf_counter() - t0\n" + "print('PERFJSON ' + json.dumps({{'solve_s': dt, " + "'count': int(app.solver.count)}}))\n" + ).format( + mod=EXAMPLE, + argv=['--dx', str(dx), '-d', str(outdir), '--max-steps', str(steps), + '--pfreq', '1000000'], + ) + t0 = time.perf_counter() + proc = subprocess.run([sys.executable, '-c', code], + capture_output=True, text=True) + wall = time.perf_counter() - t0 + perf = None + for line in proc.stdout.splitlines(): + if line.startswith('PERFJSON '): + perf = json.loads(line[len('PERFJSON '):]) + if perf is None: + sys.stderr.write(proc.stdout[-3000:] + '\n' + proc.stderr[-3000:]) + raise SystemExit('CPU run did not report PERFJSON') + perf['subprocess_wall_s'] = wall + perf['s_per_step'] = perf['solve_s'] / max(perf['count'], 1) + return perf + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=0.011, + help='~1.07M particles at 0.011.') + p.add_argument('--cpu-steps', type=int, default=20) + p.add_argument('--warp-perf-steps', type=int, default=20) + p.add_argument('--snapshot-tf', type=float, default=0.20, + help='Continue Warp to this physical time for the snapshot ' + '(0 = perf only, no developed snapshot).') + p.add_argument('--hdx', type=float, default=1.3) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--cfl', type=float, default=0.3) + p.add_argument('--n-damp', type=int, default=50) + p.add_argument('--prefix', default='bench-1M') + args = p.parse_args() + + out_dir = Path(__file__).parent + import warp as wp + + # --- Warp IC (also gives the particle count) --- + runner = WarpDamBreak3DRunner( + dx=args.dx, hdx=args.hdx, c0=args.c0, cfl=args.cfl, + n_damp=args.n_damp, adaptive_dt=True, + ) + fluid, wall = runner.create_particles() + n_fluid = fluid.get_number_of_particles() + n_wall = wall.get_number_of_particles() + n_total = n_fluid + n_wall + print('[bench] particles: fluid=%d wall=%d total=%d (dx=%.4f)' + % (n_fluid, n_wall, n_total, args.dx), flush=True) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall], radius_scale=runner.radius_scale + ) + + def warp_step(n_now, push): + scale = damp_factor(n_now, runner.n_damp) + return wc_sph_dam_break_step( + nnps, fluid_index=0, solid_indices=(1,), dt=runner.dt, + rho0=runner.rho0, c0=runner.c0, gamma=runner.gamma, + alpha=runner.alpha, beta=runner.beta, kernel='wendland', + xsph_eps=runner.xsph_eps, gz=runner.gz, gravity_ramp=1.0, + adaptive_dt=True, cfl=runner.cfl, dt_min=runner.dt_min, + dt_max=runner.dt_max, adaptive_dt_scale=scale, + step_dt_max=runner.dt_max, push=push, return_dt=True, + ) + + # --- Warp per-step throughput (fixed short count) --- + t_sim, count = 0.0, 0 + t_sim += warp_step(count, push=True); count += 1 # warmup (module load) + wp.synchronize_device(nnps.device) + t0 = time.perf_counter() + for _ in range(args.warp_perf_steps): + t_sim += warp_step(count, push=False); count += 1 + wp.synchronize_device(nnps.device) + warp_per_step = (time.perf_counter() - t0) / args.warp_perf_steps + + # --- CPU per-step throughput (subprocess, fixed short count) --- + tmp = tempfile.mkdtemp(prefix='dam_break_1M_', dir=str(out_dir)) + cpu = cpu_per_step(args.dx, args.cpu_steps, tmp) + import shutil + shutil.rmtree(tmp, ignore_errors=True) + + perf = { + 'case': 'Lobovsky 3D dam-break, no obstacle (~1M particles)', + 'dx': args.dx, 'c0': args.c0, 'kernel': 'wendland', + 'n_fluid': n_fluid, 'n_wall': n_wall, 'n_total': n_total, + 'cpu_pysph_fp64': { + 's_per_step': cpu['s_per_step'], 'steps': cpu['count'], + 'solve_s': cpu['solve_s'], + 'particle_steps_per_s': n_total / cpu['s_per_step'], + }, + 'warp_gpu_fp32': { + 's_per_step': warp_per_step, 'steps': args.warp_perf_steps, + 'particle_steps_per_s': n_total / warp_per_step, + }, + 'speedup_per_step': cpu['s_per_step'] / warp_per_step, + 'device': 'RTX 4060 Laptop (fp32) vs single-thread PySPH Cython (fp64)', + } + print('PERF ' + json.dumps(perf, indent=2, sort_keys=True), flush=True) + (out_dir / f'{args.prefix}-perf.json').write_text( + json.dumps(perf, indent=2, sort_keys=True)) + print('[bench] perf written; per-step speedup %.1fx (cpu %.3fs warp %.3fs)' + % (perf['speedup_per_step'], cpu['s_per_step'], warp_per_step), + flush=True) + + if args.snapshot_tf <= 0: + return 0 + + # --- continue Warp to a developed time for the 3D-explicit snapshot --- + print('[bench] developing to tf=%.3f for snapshot (warp per-step ~%.3fs -> ' + '~%.0f steps)...' % (args.snapshot_tf, warp_per_step, + args.snapshot_tf / 9e-4), flush=True) + while t_sim < args.snapshot_tf - 1e-12: + t_sim += warp_step(count, push=False); count += 1 + if count % 200 == 0: + print('[bench] step %d t=%.4f' % (count, t_sim), flush=True) + fluid.gpu.pull('x', 'y', 'z', 'u', 'v', 'w') + wall.gpu.pull('x', 'y', 'z') + fx, fy, fz = np.asarray(fluid.x), np.asarray(fluid.y), np.asarray(fluid.z) + fu, fv, fw = np.asarray(fluid.u), np.asarray(fluid.v), np.asarray(fluid.w) + wx, wy, wz = np.asarray(wall.x), np.asarray(wall.y), np.asarray(wall.z) + speed = np.sqrt(fu*fu + fv*fv + fw*fw) + vmax = max(float(speed.max()), 1e-6) + + def sub(n_pts, *arrs): + if arrs[0].size <= n_pts: + return arrs + rng = np.linspace(0, arrs[0].size - 1, n_pts).astype(int) + return tuple(a[rng] for a in arrs) + + fxp, fzp, fyp, sp = sub(200000, fx, fz, fy, speed) + wxp, wzp, wyp = sub(120000, wx, wz, wy) + fx3, fy3, fz3, s3 = sub(25000, fx, fy, fz, speed) + + fig = plt.figure(figsize=(15, 8), constrained_layout=True) + fig.suptitle('Warp GPU (fp32) 3D dam break, %d fluid + %d wall = %d total, ' + 't=%.3f s (dx=%.3f, WendlandQuintic)' + % (n_fluid, n_wall, n_total, t_sim, args.dx), fontsize=12) + + ax1 = fig.add_subplot(2, 2, 1) + ax1.scatter(wxp, wzp, s=2, c='0.8', marker='s', linewidths=0) + s1 = ax1.scatter(fxp, fzp, c=sp, s=2, vmin=0, vmax=vmax, cmap='viridis', + linewidths=0) + ax1.set_title('x-z side view (all y projected)') + ax1.set_xlabel('x (m)'); ax1.set_ylabel('z (m)') + ax1.set_aspect('equal', adjustable='box') + fig.colorbar(s1, ax=ax1, label='speed (m/s)', shrink=0.8) + + ax2 = fig.add_subplot(2, 2, 2) + ax2.scatter(wxp, wyp, s=2, c='0.85', marker='s', linewidths=0) + s2 = ax2.scatter(fxp, fyp, c=sp, s=2, vmin=0, vmax=vmax, cmap='viridis', + linewidths=0) + ax2.set_title('x-y top-down view (channel width in y)') + ax2.set_xlabel('x (m)'); ax2.set_ylabel('y (m)') + ax2.set_aspect('equal', adjustable='box') + fig.colorbar(s2, ax=ax2, label='speed (m/s)', shrink=0.8) + + ax3 = fig.add_subplot(2, 2, 3) + s3c = ax3.scatter(fyp, fzp, c=sp, s=2, vmin=0, vmax=vmax, cmap='viridis', + linewidths=0) + ax3.set_title('y-z end view (cross-channel structure)') + ax3.set_xlabel('y (m)'); ax3.set_ylabel('z (m)') + ax3.set_aspect('equal', adjustable='box') + fig.colorbar(s3c, ax=ax3, label='speed (m/s)', shrink=0.8) + + ax4 = fig.add_subplot(2, 2, 4, projection='3d') + ax4.scatter(fx3, fy3, fz3, c=s3, s=1, vmin=0, vmax=vmax, cmap='viridis', + linewidths=0) + ax4.set_title('3D scatter (subsampled %d)' % fx3.size) + ax4.set_xlabel('x'); ax4.set_ylabel('y'); ax4.set_zlabel('z') + try: + ax4.set_box_aspect((np.ptp(fx3), max(np.ptp(fy3), 1e-3), np.ptp(fz3))) + except Exception: + pass + ax4.view_init(elev=18, azim=-72) + + img = out_dir / f'{args.prefix}-3d-snapshot.png' + fig.savefig(img, dpi=140) + plt.close(fig) + perf['snapshot_image'] = str(img) + perf['snapshot_t'] = t_sim + (out_dir / f'{args.prefix}-perf.json').write_text( + json.dumps(perf, indent=2, sort_keys=True)) + print('[bench] snapshot written: %s (t=%.3f, %d steps)' + % (img, t_sim, count), flush=True) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/compare_warp_pysph_dam_break_3d.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/compare_warp_pysph_dam_break_3d.py new file mode 100644 index 000000000..44f7b730f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/compare_warp_pysph_dam_break_3d.py @@ -0,0 +1,403 @@ +#!/usr/bin/env python3 +"""Tier-1 smoke parity: Warp 3D dam-break vs a hand-rolled CPU baseline. + +Builds the same Lobovsky no-obstacle initial condition (DamBreak3DGeometry, +fluid + one wall array) at a coarse resolution and advances BOTH: + +- the Warp ``wc_sph_dam_break_step`` (via ``WarpDamBreak3DRunner``), and +- a hand-rolled CPU EPEC reimplementation using ``LinkedListNNPS(dim=3)`` + + ``WendlandQuintic(dim=3)`` and the exact same equation blocks the Warp + generator emits (continuity, Tait + Tait-HG walls, pressure gradient, + Monaghan AV, XSPH, gravity), + +then compares field-by-field. To make the comparison a clean fp32-vs-fp64 +diff, both sides run with a FIXED dt, no ``n_damp`` damping, and full gravity +(``adaptive_dt=False``, ``n_damp=0``). The CPU stepper mirrors +``wc_sph_dam_break_step`` exactly: fluid accel + density rate sum over +[fluid, wall]; wall density rate from fluid only; XSPH from fluid only; walls +held fixed (zero acceleration -> the shared PEC stage leaves them in place) +with ``TaitEOSHGCorrection`` (clamp rho >= rho0) re-applied every evaluation. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from cyarray.carray import UIntArray + +from pysph.base.kernels import WendlandQuintic +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.examples._db_geometry import DamBreak3DGeometry + +from dam_break_3d_runner import H, REF_C0, WarpDamBreak3DRunner + + +# -------------------------------------------------------------------------- +# CPU equation blocks (mirror the Warp generated blocks; cross-array capable). +# -------------------------------------------------------------------------- +def _neighbors(nnps, src_index, dst_index, d_idx): + nbrs = UIntArray() + nnps.get_nearest_particles(src_index, dst_index, d_idx, nbrs) + return nbrs.get_npy_array()[:nbrs.length] + + +def _xij_vij(dst, src, d, s): + xij = [dst.x[d] - src.x[s], dst.y[d] - src.y[s], dst.z[d] - src.z[s]] + vij = [dst.u[d] - src.u[s], dst.v[d] - src.v[s], dst.w[d] - src.w[s]] + return xij, vij + + +def _cpu_continuity(nnps, particles, src_index, dst_index, kernel): + src, dst = particles[src_index], particles[dst_index] + out = np.zeros(dst.get_number_of_particles()) + for d in range(dst.get_number_of_particles()): + total = 0.0 + for s in _neighbors(nnps, src_index, dst_index, d): + xij, vij = _xij_vij(dst, src, d, s) + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d] + src.h[s]) + dw = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dw) + total += src.m[s] * (vij[0]*dw[0] + vij[1]*dw[1] + vij[2]*dw[2]) + out[d] = total + return out + + +def _cpu_pressure_gradient(nnps, particles, src_index, dst_index, kernel): + src, dst = particles[src_index], particles[dst_index] + out = np.zeros((dst.get_number_of_particles(), 3)) + for d in range(dst.get_number_of_particles()): + acc = np.zeros(3) + rhoi21 = 1.0 / (dst.rho[d] * dst.rho[d]) + tmpi = dst.p[d] * rhoi21 + for s in _neighbors(nnps, src_index, dst_index, d): + xij, _ = _xij_vij(dst, src, d, s) + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d] + src.h[s]) + dw = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dw) + rhoj21 = 1.0 / (src.rho[s] * src.rho[s]) + acc += -src.m[s] * (tmpi + src.p[s] * rhoj21) * np.asarray(dw) + out[d] = acc + return out + + +def _cpu_artificial_viscosity(nnps, particles, src_index, dst_index, alpha, + beta, kernel): + src, dst = particles[src_index], particles[dst_index] + out = np.zeros((dst.get_number_of_particles(), 3)) + for d in range(dst.get_number_of_particles()): + acc = np.zeros(3) + for s in _neighbors(nnps, src_index, dst_index, d): + xij, vij = _xij_vij(dst, src, d, s) + vdotx = vij[0]*xij[0] + vij[1]*xij[1] + vij[2]*xij[2] + if vdotx < 0.0: + rij2 = xij[0]**2 + xij[1]**2 + xij[2]**2 + rij = np.sqrt(rij2) + hij = 0.5 * (dst.h[d] + src.h[s]) + mu = hij * vdotx / (rij2 + 0.01*hij*hij) + rhoij1 = 2.0 / (dst.rho[d] + src.rho[s]) + cij = 0.5 * (dst.cs[d] + src.cs[s]) + piij = (-alpha*cij*mu + beta*mu*mu) * rhoij1 + dw = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dw) + acc += -src.m[s] * piij * np.asarray(dw) + out[d] = acc + return out + + +def _cpu_xsph(nnps, particles, src_index, dst_index, eps, kernel): + src, dst = particles[src_index], particles[dst_index] + out = np.zeros((dst.get_number_of_particles(), 3)) + if eps is None or eps == 0.0: + return out + for d in range(dst.get_number_of_particles()): + acc = np.zeros(3) + for s in _neighbors(nnps, src_index, dst_index, d): + xij, vij = _xij_vij(dst, src, d, s) + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d] + src.h[s]) + wij = kernel.kernel(xij=xij, rij=rij, h=hij) + rhoij1 = 2.0 / (dst.rho[d] + src.rho[s]) + acc += (-eps * src.m[s] * wij * rhoij1) * np.asarray(vij) + out[d] = acc + return out + + +def _tait_eos(rho, rho0, c0, gamma, p0=0.0): + ratio = rho / rho0 + p = p0 + (rho0*c0*c0/gamma) * (ratio**gamma - 1.0) + cs = c0 * ratio**(0.5 * (gamma - 1.0)) + return p, cs + + +# -------------------------------------------------------------------------- +# CPU dam-break EPEC stepper (mirrors wc_sph_dam_break_step exactly). +# -------------------------------------------------------------------------- +class CpuDamBreak3D: + def __init__(self, fluid, wall, rho0, c0, gamma, alpha, beta, xsph_eps, + gz, radius_scale, p0=0.0): + self.fluid = fluid + self.wall = wall + self.particles = [fluid, wall] + self.rho0 = rho0 + self.c0 = c0 + self.gamma = gamma + self.alpha = alpha + self.beta = beta + self.eps = xsph_eps + self.gz = gz + self.p0 = p0 + self.radius_scale = radius_scale + self.kernel = WendlandQuintic(dim=3) + + def _nnps(self): + return LinkedListNNPS( + dim=3, particles=self.particles, radius_scale=self.radius_scale + ) + + def _accel(self): + f, w = self.fluid, self.wall + # EOS: fluid Tait; wall Tait-HG (clamp rho >= rho0 in place). + f.p[:], f.cs[:] = _tait_eos( + f.rho, self.rho0, self.c0, self.gamma, self.p0 + ) + np.maximum(w.rho, self.rho0, out=w.rho) + w.p[:], w.cs[:] = _tait_eos(w.rho, self.rho0, self.c0, self.gamma) + # Zero accumulators (walls keep zero accel -> stay fixed). + for pa in (f, w): + for name in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + getattr(pa, name)[:] = 0.0 + nnps = self._nnps() + # Fluid: continuity + pressure + AV summed over [fluid, wall]. + for src in (0, 1): + f.arho[:] += _cpu_continuity(nnps, self.particles, src, 0, + self.kernel) + pg = _cpu_pressure_gradient(nnps, self.particles, src, 0, + self.kernel) + av = _cpu_artificial_viscosity(nnps, self.particles, src, 0, + self.alpha, self.beta, self.kernel) + f.au[:] += pg[:, 0] + av[:, 0] + f.av[:] += pg[:, 1] + av[:, 1] + f.aw[:] += pg[:, 2] + av[:, 2] + # XSPH from fluid neighbours only. + xs = _cpu_xsph(nnps, self.particles, 0, 0, self.eps, self.kernel) + f.ax[:], f.ay[:], f.az[:] = xs[:, 0], xs[:, 1], xs[:, 2] + # Wall density rate from the fluid only. + w.arho[:] = _cpu_continuity(nnps, self.particles, 0, 1, self.kernel) + # Gravity (full) into the fluid acceleration. + f.aw[:] += self.gz + + def _save_state(self): + for pa in (self.fluid, self.wall): + pa.x0[:], pa.y0[:], pa.z0[:] = pa.x, pa.y, pa.z + pa.u0[:], pa.v0[:], pa.w0[:] = pa.u, pa.v, pa.w + pa.rho0[:] = pa.rho + + def _pec_stage(self, dt, stage, xsph): + fac = dt * stage + for pa in (self.fluid, self.wall): + use_xsph = xsph and (pa is self.fluid) + ax = pa.ax if use_xsph else np.zeros_like(pa.x) + ay = pa.ay if use_xsph else np.zeros_like(pa.x) + az = pa.az if use_xsph else np.zeros_like(pa.x) + # Position uses the CURRENT velocity (before this stage's kick). + new_x = pa.x0 + fac * (pa.u + ax) + new_y = pa.y0 + fac * (pa.v + ay) + new_z = pa.z0 + fac * (pa.w + az) + pa.u[:] = pa.u0 + fac * pa.au + pa.v[:] = pa.v0 + fac * pa.av + pa.w[:] = pa.w0 + fac * pa.aw + pa.x[:], pa.y[:], pa.z[:] = new_x, new_y, new_z + pa.rho[:] = pa.rho0 + fac * pa.arho + + def step(self, dt): + self._save_state() + self._accel() # E + self._pec_stage(dt, 0.5, xsph=True) # P + self._accel() # E + self._pec_stage(dt, 1.0, xsph=True) # C + + def run(self, steps, dt): + for _ in range(steps): + self.step(dt) + + +def _build_cpu_particles(dx, hdx, rho0, c0, nboundary_layers): + geom = DamBreak3DGeometry( + container_height=1.5 * H, container_width=H / 2.0, + container_length=161 * H / 30.0, fluid_column_height=H, + fluid_column_width=H / 2.0, fluid_column_length=2.0 * H, + dx=dx, nboundary_layers=nboundary_layers, hdx=hdx, rho0=rho0, + with_obstacle=False, + ) + fluid_cpu, wall_cpu = geom.create_particles() + + def rebuild(src, name): + n = src.get_number_of_particles() + z = np.zeros(n) + return get_particle_array( + name=name, + x=np.asarray(src.x).copy(), y=np.asarray(src.y).copy(), + z=np.asarray(src.z).copy(), h=np.asarray(src.h).copy(), + m=np.asarray(src.m).copy(), rho=np.ones(n) * rho0, + p=z.copy(), cs=np.ones(n) * c0, + u=z.copy(), v=z.copy(), w=z.copy(), + au=z.copy(), av=z.copy(), aw=z.copy(), arho=z.copy(), + ax=z.copy(), ay=z.copy(), az=z.copy(), + x0=z.copy(), y0=z.copy(), z0=z.copy(), + u0=z.copy(), v0=z.copy(), w0=z.copy(), rho0=z.copy(), + ) + + return rebuild(fluid_cpu, 'fluid'), rebuild(wall_cpu, 'wall') + + +def _max_abs_diff(a, b): + return float(np.max(np.abs(np.asarray(a) - np.asarray(b)))) + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=0.12) + p.add_argument('--steps', type=int, default=3) + p.add_argument('--dt', type=float, default=1.0e-4) + p.add_argument('--hdx', type=float, default=1.3) + p.add_argument('--rho0', type=float, default=1000.0) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--gamma', type=float, default=7.0) + p.add_argument('--alpha', type=float, default=0.25) + p.add_argument('--beta', type=float, default=0.0) + p.add_argument('--xsph-eps', type=float, default=0.5) + p.add_argument('--gz', type=float, default=-9.81) + p.add_argument('--radius-scale', type=float, default=2.0) + p.add_argument('--nboundary-layers', type=int, default=1) + p.add_argument('--rtol', type=float, default=2.0e-3, + help='Relative tol for kinematics + density.') + p.add_argument('--p-atol-factor', type=float, default=16.0, + help='Pressure absolute tol = factor*rho0*c0^2*2^-23 ' + '(fp32 Tait-EOS cancellation floor near rest).') + p.add_argument('--output-dir', default=None) + p.add_argument('--prefix', default='comparison-smoke') + args = p.parse_args() + + out_dir = Path(args.output_dir) if args.output_dir else Path(__file__).parent + out_dir.mkdir(parents=True, exist_ok=True) + warp_path = out_dir / f'{args.prefix}-warp.npz' + + # --- Warp side: fixed dt, no damping, full gravity (clean diff) --- + runner = WarpDamBreak3DRunner( + dx=args.dx, hdx=args.hdx, steps=args.steps, rho0=args.rho0, + c0=args.c0, gamma=args.gamma, alpha=args.alpha, beta=args.beta, + kernel='wendland', radius_scale=args.radius_scale, + xsph_eps=args.xsph_eps, gz=args.gz, n_damp=0, adaptive_dt=False, + dt=args.dt, nboundary_layers=args.nboundary_layers, output=warp_path, + ) + warp_metrics = runner.run() + warp = np.load(warp_path) + + # --- CPU side: identical IC (deterministic geometry), same fixed dt --- + fluid, wall = _build_cpu_particles( + args.dx, args.hdx, args.rho0, args.c0, args.nboundary_layers + ) + cpu = CpuDamBreak3D( + fluid, wall, rho0=args.rho0, c0=args.c0, gamma=args.gamma, + alpha=args.alpha, beta=args.beta, xsph_eps=args.xsph_eps, gz=args.gz, + radius_scale=args.radius_scale, + ) + cpu.run(args.steps, args.dt) + + # Sanity: identical particle counts / initial layout. + assert fluid.get_number_of_particles() == warp['fluid_x'].size, ( + "fluid count mismatch -> non-identical IC" + ) + assert wall.get_number_of_particles() == warp['wall_x'].size, ( + "wall count mismatch -> non-identical IC" + ) + + diffs = { + 'fluid_x': _max_abs_diff(fluid.x, warp['fluid_x']), + 'fluid_y': _max_abs_diff(fluid.y, warp['fluid_y']), + 'fluid_z': _max_abs_diff(fluid.z, warp['fluid_z']), + 'fluid_u': _max_abs_diff(fluid.u, warp['fluid_u']), + 'fluid_v': _max_abs_diff(fluid.v, warp['fluid_v']), + 'fluid_w': _max_abs_diff(fluid.w, warp['fluid_w']), + 'fluid_rho': _max_abs_diff(fluid.rho, warp['fluid_rho']), + 'fluid_p': _max_abs_diff(fluid.p, warp['fluid_p']), + 'wall_rho': _max_abs_diff(wall.rho, warp['wall_rho']), + 'wall_p': _max_abs_diff(wall.p, warp['wall_p']), + } + # Relative scales for a fp32-vs-fp64 pass/fail verdict. + scales = { + 'fluid_x': np.abs(warp['fluid_x']).max() + 1.0, + 'fluid_y': np.abs(warp['fluid_y']).max() + 1.0, + 'fluid_z': np.abs(warp['fluid_z']).max() + 1.0, + 'fluid_u': np.abs(warp['fluid_u']).max() + 1.0, + 'fluid_v': np.abs(warp['fluid_v']).max() + 1.0, + 'fluid_w': np.abs(warp['fluid_w']).max() + 1.0, + 'fluid_rho': float(args.rho0), + 'fluid_p': np.abs(warp['fluid_p']).max() + 1.0, + 'wall_rho': float(args.rho0), + 'wall_p': np.abs(warp['wall_p']).max() + 1.0, + } + rel = {k: float(diffs[k] / scales[k]) for k in diffs} + worst = float(max(rel.values())) + cpu_finite = all(np.all(np.isfinite(getattr(fluid, n))) + for n in ('x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p')) + + # Verdict. Kinematics + density are compared in RELATIVE terms -- the Warp + # (fp32) vs CPU (fp64) step agrees on these to ~1e-7. Pressure is special + # near rest: p = B*((rho/rho0)^gamma - 1) is a tiny difference of near-equal + # quantities, so the fp32 rounding of rho (~rho0*2^-23 absolute) is + # amplified by dp/drho ~ B*gamma/rho0, giving a large RELATIVE p error while + # the ABSOLUTE error stays at the fp32 Tait cancellation floor. We therefore + # gate pressure on an absolute tolerance derived from that floor, not a + # relative one. (At a developed free surface, p >> this floor and the + # relative agreement recovers; see tier-2 / experiment.md.) + kin_fields = ('fluid_x', 'fluid_y', 'fluid_z', 'fluid_u', 'fluid_v', + 'fluid_w', 'fluid_rho', 'wall_rho') + fp32_eps = 2.0 ** -23 + p_abs_floor = args.p_atol_factor * args.rho0 * args.c0 * args.c0 * fp32_eps + kin_ok = all(rel[k] <= args.rtol for k in kin_fields) + pressure_ok = (diffs['fluid_p'] <= p_abs_floor + and diffs['wall_p'] <= p_abs_floor) + passed = bool( + bool(warp_metrics['all_finite']) and bool(cpu_finite) + and kin_ok and pressure_ok + ) + + report = { + 'params': { + 'dx': args.dx, 'steps': args.steps, 'dt': args.dt, + 'hdx': args.hdx, 'rho0': args.rho0, 'c0': args.c0, + 'gamma': args.gamma, 'alpha': args.alpha, 'beta': args.beta, + 'xsph_eps': args.xsph_eps, 'gz': args.gz, + 'radius_scale': args.radius_scale, + }, + 'fluid_particles': int(fluid.get_number_of_particles()), + 'wall_particles': int(wall.get_number_of_particles()), + 'max_abs_diff': diffs, + 'max_rel_diff': rel, + 'worst_rel_diff': worst, + 'rtol_kinematic': args.rtol, + 'pressure_abs_floor': float(p_abs_floor), + 'kinematics_density_ok': bool(kin_ok), + 'pressure_ok': bool(pressure_ok), + 'warp_metrics': warp_metrics, + 'passed': passed, + } + print(json.dumps(report, indent=2, sort_keys=True)) + (out_dir / f'{args.prefix}-summary.json').write_text( + json.dumps(report, indent=2, sort_keys=True) + ) + if not passed: + raise SystemExit("Tier-1 parity failed (see max_rel_diff)") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/comparison-resolved-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/comparison-resolved-summary.json new file mode 100644 index 000000000..6eb83fd95 --- /dev/null +++ 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"p_max": 13842.669921875, + "p_min": 2.958691358566284, + "rho_max": 1012.3660888671875, + "rho_min": 1000.0025024414062, + "surge_front_x": 1.9304553270339966, + "wall_p_max": 13616.6513671875 + }, + "warp_all_finite": true, + "warp_t": 0.05 + } + ], + "fluid_particles": 512, + "ic_t": 0.0, + "n_snapshots": 5, + "params": { + "alpha": 0.25, + "beta": 0.0, + "c0": 32.849657532461435, + "cfl": 0.3, + "dt_max": 0.0008004282726565228, + "dt_min": 0.0, + "dx": 0.12, + "gamma": 7.0, + "gz": -9.81, + "keep_output": false, + "n_damp": 50, + "output_dir": null, + "pfreq": 15, + "prefix": "comparison-resolved", + "radius_scale": 2.0, + "rho0": 1000.0, + "tf": 0.05, + "xsph_eps": 0.5 + }, + "reference_example": "pysph.examples.dam_break.dam_break_3d_lobovsky", + "wall_particles": 2647 +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/comparison-tier1-summary.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/comparison-tier1-summary.json new file mode 100644 index 000000000..0e840864c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/comparison-tier1-summary.json @@ -0,0 +1,86 @@ +{ + "fluid_particles": 234, + "kinematics_density_ok": true, + "max_abs_diff": { + "fluid_p": 0.17433217580421279, + "fluid_rho": 8.50729794592553e-05, + "fluid_u": 5.058560746385264e-08, + "fluid_v": 3.5991372903144904e-08, + "fluid_w": 4.8728901390522605e-08, + "fluid_x": 4.768794581444524e-08, + "fluid_y": 3.0869647393849675e-09, + "fluid_z": 4.803797648555985e-08, + "wall_p": 0.17488435002464575, + "wall_rho": 8.609495273503853e-05 + }, + "max_rel_diff": { + "fluid_p": 0.08591558340491236, + "fluid_rho": 8.50729794592553e-08, + "fluid_u": 5.0585541315802036e-08, + "fluid_v": 3.599132576944242e-08, + "fluid_w": 4.858591413969757e-08, + "fluid_x": 1.6165405099531748e-08, + "fluid_y": 2.5724706097653395e-09, + "fluid_z": 2.5283151388057872e-08, + "wall_p": 0.0861877097067313, + "wall_rho": 8.609495273503853e-08 + }, + "params": { + "alpha": 0.25, + "beta": 0.0, + "c0": 32.849657532461435, + "dt": 0.0001, + "dx": 0.15, + "gamma": 7.0, + "gz": -9.81, + "hdx": 1.3, + "radius_scale": 2.0, + "rho0": 1000.0, + "steps": 3, + "xsph_eps": 0.5 + }, + "passed": true, + "pressure_abs_floor": 2.0582199096679696, + "pressure_ok": true, + "rtol_kinematic": 0.002, + "wall_particles": 1809, + "warp_metrics": { + "adaptive_dt": false, + "all_finite": true, + "alpha": 0.25, + "beta": 0.0, + "c0": 32.849657532461435, + "cfl": 0.3, + "dt": 0.0001, + "dt_last": 0.0001, + "dt_max_used": 0.0001, + "dt_min_used": 0.0001, + "dx": 0.15, + "fluid_particles": 234, + "gamma": 7.0, + "gz": -9.81, + "h0": 0.195, + "hdx": 1.3, + "kernel": "wendland", + "kinetic_energy": 0.0034144900872354915, + "max_height": 0.8999995589256287, + "n_damp": 0, + "p_max": 1.029110074043274, + "p_min": 0.0, + "radius_scale": 2.0, + "rho0": 1000.0, + "rho_max": 1000.0012817382812, + "rho_min": 1000.0, + "steps": 3, + "surge_front_x": 1.9500000476837158, + "time": 0.00030000000000000003, + "w_mean": -0.0029405790528393965, + "wall_p_max": 1.029110074043274, + "wall_p_min": 0.0, + "wall_particles": 1809, + "x_min": 0.15000000596046448, + "xsph_eps": 0.5, + "z_min": 0.14999957382678986 + }, + "worst_rel_diff": 0.0861877097067313 +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-perf.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-perf.json new file mode 100644 index 000000000..6188d695b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-perf.json @@ -0,0 +1,27 @@ +{ + "c0": 32.849657532461435, + "case": "Lobovsky 3D dam-break, no obstacle", + "cpu_pysph_fp64": { + "final_t": 0.4, + "s_per_step": 0.01303212759869848, + "solve_s": 6.007810823, + "steps": 461, + "subprocess_wall_s": 9.911120583999946 + }, + "dx": 0.08, + "kernel": "wendland", + "n_fluid": 1800, + "n_total": 7392, + "n_wall": 5592, + "reference_example": "pysph.examples.dam_break.dam_break_3d_lobovsky", + "snapshot_image": "/home/kunalp/work/particles/pysph/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-snapshot.png", + "speedup_per_step": 1.528473001996329, + "speedup_wall": 1.531795769391973, + "tf": 0.4, + "warp_gpu_fp32": { + "final_t": 0.4, + "s_per_step": 0.008526239967390527, + "steps": 460, + "wall_s": 3.9220703849996426 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-snapshot.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-snapshot.png new file mode 100644 index 000000000..eefce80bd Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/cpu-vs-warp-snapshot.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/dam_break_3d_runner.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/dam_break_3d_runner.py new file mode 100644 index 000000000..a4e422f6b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/dam_break_3d_runner.py @@ -0,0 +1,325 @@ +#!/usr/bin/env python3 +"""Application-style Warp runner for the 3D dam-break. + +The default mirrors the PySPH ``dam_break_3d_lobovsky.py`` no-obstacle +reference (ADR-0005). ``--with-obstacle`` enables the fixed Kleefsman obstacle +already supported by the same ``DamBreak3DGeometry``. Both are advanced with +the additive Warp dam-break step:: + + UniformGridWarpNNPS(dim=3, [fluid, wall(, obstacle)]) + wc_sph_dam_break_step # EPEC, WendlandQuintic, Tait + Tait-HG walls + +Physics parity notes (vs the reference scheme): + +- ``c0`` defaults to the reference scheme's sound speed + ``10*sqrt(2*9.81*0.55)`` (the module constant the WCSPHScheme is built with), + *not* ``get_max_speed`` -- the reference uses the latter only for the initial + ``dt`` guess. See the experiment.md for this known reference inconsistency. +- The Warp step is E-P-E-C (it re-evaluates accelerations before the predictor), + which matches the reference ``EPECIntegrator``; gravity is full strength. +- The ``n_damp`` startup uses PySPH's *timestep* damping factor + ``0.5*(sin(pi*(-0.5 + (count+1)/n_damp)) + 1)`` (Solver._damp_timestep), + applied to the adaptive ``dt`` per step -- the reference does NOT ramp gravity + (the additive ``gravity_ramp`` backend feature is left at 1.0 here). +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np + +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_dam_break_step +from pysph.examples._db_geometry import DamBreak3DGeometry + +# Lobovsky no-obstacle reference scale (dam_break_3d_lobovsky.py). +H = 1.0 +GRAVITY = 9.81 +# Reference scheme sound speed (module constant, built into the WCSPHScheme). +REF_C0 = 10.0 * math.sqrt(2.0 * GRAVITY * 0.55) + + +def damp_factor(count, n_damp): + """PySPH ``Solver._damp_timestep`` factor for 0-based step ``count``.""" + if n_damp > 0 and count < n_damp: + return 0.5 * (math.sin(math.pi * (-0.5 + (count + 1) / float(n_damp))) + + 1.0) + return 1.0 + + +class WarpDamBreak3DRunner: + """Small Application-style runner around ``wc_sph_dam_break_step``. + + Like the elliptical-drop runner, this deliberately does not use PySPH's + Application/Solver stack; it gives the additive 3D dam-break path a + repeatable, reference-matched workload for correctness checks. + """ + + def __init__(self, dx=H / 15.0, hdx=1.3, steps=20, rho0=1000.0, c0=REF_C0, + p0=0.0, gamma=7.0, alpha=0.25, beta=0.0, kernel='wendland', + radius_scale=2.0, xsph_eps=0.5, gz=-GRAVITY, n_damp=50, + nboundary_layers=1, adaptive_dt=True, cfl=0.3, dt=None, + dt_min=0.0, dt_max=None, with_obstacle=False, output=None): + self.dx = float(dx) + self.hdx = float(hdx) + self.steps = int(steps) + self.rho0 = float(rho0) + self.c0 = float(c0) + self.p0 = float(p0) + self.gamma = float(gamma) + self.alpha = float(alpha) + self.beta = float(beta) + self.kernel = kernel + self.radius_scale = float(radius_scale) + self.xsph_eps = None if xsph_eps is None else float(xsph_eps) + self.gz = float(gz) + self.n_damp = int(n_damp) + self.nboundary_layers = int(nboundary_layers) + self.adaptive_dt = bool(adaptive_dt) + self.cfl = float(cfl) + self.h0 = self.hdx * self.dx + # Reference initial dt: 0.25*h0/(1.1*co), co = 10*get_max_speed. + co = 10.0 * math.sqrt(2.0 * GRAVITY * H) + ref_dt = 0.25 * self.h0 / (1.1 * co) + self.dt = ref_dt if dt is None else float(dt) + self.dt_min = float(dt_min) + # PySPH treats ref_dt as the *initial/seed* dt only -- its adaptive + # controller (Integrator.compute_time_step) then grows dt to the + # CFL-limited value with NO clamp to the seed. So we must NOT cap the + # adaptive dt at ref_dt; default to no cap (CFL + n_damp govern, exactly + # like the reference). Capping at ref_dt would make Warp take ~1.7x more, + # smaller steps than PySPH to the same physical time. + self.dt_max = float('inf') if dt_max is None else float(dt_max) + self.with_obstacle = bool(with_obstacle) + self.output = Path(output) if output is not None else None + self.dt_history = [] + self.geom = None + + def _build_geometry(self): + return DamBreak3DGeometry( + container_height=1.5 * H, container_width=H / 2.0, + container_length=161 * H / 30.0, fluid_column_height=H, + fluid_column_width=H / 2.0, fluid_column_length=2.0 * H, + dx=self.dx, nboundary_layers=self.nboundary_layers, + hdx=self.hdx, rho0=self.rho0, + with_obstacle=self.with_obstacle, + ) + + def _to_warp(self, src, name): + """Rebuild a CPU geometry array as a fully-propertied warp array.""" + n = src.get_number_of_particles() + x = np.asarray(src.x, dtype=np.float64) + y = np.asarray(src.y, dtype=np.float64) + z = np.asarray(src.z, dtype=np.float64) + zeros = np.zeros(n, dtype=np.float64) + return get_particle_array( + name=name, x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray(src.h, dtype=np.float64).copy(), + m=np.asarray(src.m, dtype=np.float64).copy(), + rho=np.ones(n) * self.rho0, p=zeros.copy(), + cs=np.ones(n) * self.c0, + u=zeros.copy(), v=zeros.copy(), w=zeros.copy(), + au=zeros.copy(), av=zeros.copy(), aw=zeros.copy(), + arho=zeros.copy(), ax=zeros.copy(), ay=zeros.copy(), + az=zeros.copy(), x0=zeros.copy(), y0=zeros.copy(), + z0=zeros.copy(), u0=zeros.copy(), v0=zeros.copy(), + w0=zeros.copy(), rho0=zeros.copy(), backend='warp', + ) + + def create_particles(self): + self.geom = self._build_geometry() + cpu_particles = self.geom.create_particles() + names = ('fluid', 'wall', 'obstacle') + return tuple( + self._to_warp(pa, names[i]) + for i, pa in enumerate(cpu_particles) + ) + + def run(self): + particles = list(self.create_particles()) + fluid = particles[0] + solids = particles[1:] + nnps = UniformGridWarpNNPS( + dim=3, particles=particles, radius_scale=self.radius_scale + ) + + time = 0.0 + for step in range(self.steps): + scale = damp_factor(step, self.n_damp) + dt_used = wc_sph_dam_break_step( + nnps, fluid_index=0, + solid_indices=tuple(range(1, len(particles))), dt=self.dt, + rho0=self.rho0, c0=self.c0, p0=self.p0, alpha=self.alpha, + beta=self.beta, gamma=self.gamma, kernel=self.kernel, + xsph_eps=self.xsph_eps, gx=0.0, gy=0.0, gz=self.gz, + gravity_ramp=1.0, adaptive_dt=self.adaptive_dt, cfl=self.cfl, + dt_min=self.dt_min, dt_max=self.dt_max, + adaptive_dt_scale=scale, step_dt_max=self.dt_max, + push=(step == 0), return_dt=True, + ) + self.dt_history.append(dt_used) + time += dt_used + + pull = ['x', 'y', 'z', 'rho', 'p', 'cs', 'u', 'v', 'w', + 'au', 'av', 'aw', 'arho'] + fluid.gpu.pull(*pull) + for solid in solids: + solid.gpu.pull('x', 'y', 'z', 'rho', 'p') + metrics = self._metrics(fluid, solids, time) + if self.output is not None: + self._write_output(fluid, solids, metrics) + return metrics + + def _metrics(self, fluid, solids, time): + wall = solids[0] + obstacle = solids[1] if len(solids) > 1 else None + finite_fluid = all( + np.all(np.isfinite(getattr(fluid, n))) + for n in ('x', 'y', 'z', 'rho', 'p', 'u', 'v', 'w', 'au', 'av', + 'aw', 'arho') + ) + finite_solids = all( + np.all(np.isfinite(getattr(solid, n))) + for solid in solids for n in ('x', 'y', 'z', 'rho', 'p') + ) + ke = 0.5 * float(np.sum( + fluid.m * (fluid.u**2 + fluid.v**2 + fluid.w**2) + )) + dt_hist = np.asarray(self.dt_history) + return { + 'fluid_particles': int(fluid.get_number_of_particles()), + 'wall_particles': int(wall.get_number_of_particles()), + 'obstacle_particles': ( + 0 if obstacle is None else + int(obstacle.get_number_of_particles()) + ), + 'steps': self.steps, + 'time': float(time), + 'dx': self.dx, + 'hdx': self.hdx, + 'h0': self.h0, + 'rho0': self.rho0, + 'c0': self.c0, + 'gamma': self.gamma, + 'alpha': self.alpha, + 'beta': self.beta, + 'gz': self.gz, + 'kernel': self.kernel, + 'radius_scale': self.radius_scale, + 'xsph_eps': self.xsph_eps, + 'n_damp': self.n_damp, + 'adaptive_dt': self.adaptive_dt, + 'cfl': self.cfl, + 'dt': self.dt, + 'dt_min_used': float(np.min(dt_hist)), + 'dt_max_used': float(np.max(dt_hist)), + 'dt_last': float(dt_hist[-1]), + 'rho_min': float(np.min(fluid.rho)), + 'rho_max': float(np.max(fluid.rho)), + 'p_min': float(np.min(fluid.p)), + 'p_max': float(np.max(fluid.p)), + 'wall_p_min': float(np.min(wall.p)), + 'wall_p_max': float(np.max(wall.p)), + 'obstacle_p_min': ( + None if obstacle is None else float(np.min(obstacle.p)) + ), + 'obstacle_p_max': ( + None if obstacle is None else float(np.max(obstacle.p)) + ), + 'x_min': float(np.min(fluid.x)), + 'surge_front_x': float(np.max(fluid.x)), + 'z_min': float(np.min(fluid.z)), + 'max_height': float(np.max(fluid.z)), + 'w_mean': float(np.mean(fluid.w)), + 'kinetic_energy': ke, + 'all_finite': bool(finite_fluid and finite_solids), + } + + def _write_output(self, fluid, solids, metrics): + wall = solids[0] + self.output.parent.mkdir(parents=True, exist_ok=True) + data = dict( + fluid_x=fluid.x, fluid_y=fluid.y, fluid_z=fluid.z, + fluid_h=fluid.h, fluid_m=fluid.m, fluid_rho=fluid.rho, + fluid_p=fluid.p, fluid_cs=fluid.cs, + fluid_u=fluid.u, fluid_v=fluid.v, fluid_w=fluid.w, + fluid_au=fluid.au, fluid_av=fluid.av, fluid_aw=fluid.aw, + fluid_arho=fluid.arho, + wall_x=wall.x, wall_y=wall.y, wall_z=wall.z, + wall_rho=wall.rho, wall_p=wall.p, + dt_history=np.asarray(self.dt_history), + metrics=json.dumps(metrics, sort_keys=True), + ) + if len(solids) > 1: + obstacle = solids[1] + data.update( + obstacle_x=obstacle.x, obstacle_y=obstacle.y, + obstacle_z=obstacle.z, obstacle_rho=obstacle.rho, + obstacle_p=obstacle.p, + ) + np.savez(self.output, **data) + + +def _parse_args(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=H / 15.0, + help='Particle spacing (reference uses H/30).') + p.add_argument('--hdx', type=float, default=1.3) + p.add_argument('--steps', type=int, default=20) + p.add_argument('--rho0', type=float, default=1000.0) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--p0', type=float, default=0.0) + p.add_argument('--gamma', type=float, default=7.0) + p.add_argument('--alpha', type=float, default=0.25) + p.add_argument('--beta', type=float, default=0.0) + p.add_argument('--kernel', default='wendland', + choices=('cubic', 'gaussian', 'wendland')) + p.add_argument('--radius-scale', type=float, default=2.0) + p.add_argument('--xsph-eps', type=float, default=0.5) + p.add_argument('--no-xsph', action='store_true') + p.add_argument('--gz', type=float, default=-GRAVITY) + p.add_argument('--n-damp', type=int, default=50) + p.add_argument('--nboundary-layers', type=int, default=1) + p.add_argument('--no-adaptive-dt', action='store_true') + p.add_argument('--cfl', type=float, default=0.3) + p.add_argument('--dt', type=float, default=None) + p.add_argument('--dt-min', type=float, default=0.0) + p.add_argument('--dt-max', type=float, default=None) + p.add_argument('--with-obstacle', action='store_true', + help='Include the fixed Kleefsman obstacle as a third array.') + p.add_argument('--output', default=None) + return p.parse_args() + + +def main(): + args = _parse_args() + runner = WarpDamBreak3DRunner( + dx=args.dx, hdx=args.hdx, steps=args.steps, rho0=args.rho0, + c0=args.c0, p0=args.p0, gamma=args.gamma, alpha=args.alpha, + beta=args.beta, kernel=args.kernel, radius_scale=args.radius_scale, + xsph_eps=None if args.no_xsph else args.xsph_eps, gz=args.gz, + n_damp=args.n_damp, nboundary_layers=args.nboundary_layers, + adaptive_dt=not args.no_adaptive_dt, cfl=args.cfl, dt=args.dt, + dt_min=args.dt_min, dt_max=args.dt_max, + with_obstacle=args.with_obstacle, output=args.output, + ) + metrics = runner.run() + print(json.dumps(metrics, indent=2, sort_keys=True)) + if metrics['fluid_particles'] <= 0: + raise SystemExit("No fluid particles were created") + if metrics['wall_particles'] <= 0: + raise SystemExit("No wall particles were created") + if args.with_obstacle and metrics['obstacle_particles'] <= 0: + raise SystemExit("No obstacle particles were created") + if not metrics['all_finite']: + raise SystemExit("Non-finite values in final state") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/experiment.md new file mode 100644 index 000000000..c53b36253 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/experiment.md @@ -0,0 +1,260 @@ +--- +type: experiment +id: 2026-06-18_warp-dam-break-3d-runner +created: 2026-06-19T10:00:00 CEST +author: @kunalpuri-prediqt +agent: claude +aspect: validation-benchmarks +adr: ADR-0005 +status: complete +last_checked: 2026-06-20T18:58:02 CEST +--- + +# Experiment: Warp 3D dam-break (Lobovsky no-obstacle) + +## Headline + +Second validation case for the Warp backend, and the first **3D**, **gravity- +driven**, **multi-array (fluid + solid wall)** one: the Lobovsky no-obstacle +dam-break. The additive Warp dam-break step matches both a hand-rolled CPU +baseline (tier-1) and the **real shipped PySPH `dam_break_3d_lobovsky.py` +Application** (tier-2) to fp32-vs-fp64 precision on every observable that is not +near-rest-pressure; near-rest pressure is at the fp32 Tait-EOS cancellation +floor and recovers to ~1% relative once the flow develops. + +## Purpose + +Exercise the four WCSPH features the 2D elliptical drop never used -- +WendlandQuintic kernel, gravity, multi-array stepping, and fixed solid walls +(`TaitEOSHGCorrection`) -- end to end against the PySPH CPU references, using +the *same* initial condition the reference builds: + +```text +DamBreak3DGeometry(no obstacle) -> [fluid, wall] +UniformGridWarpNNPS(dim=3, [fluid, wall]) +wc_sph_dam_break_step # EPEC, WendlandQuintic, Tait + Tait-HG walls +``` + +## Reference (CPU) + +`pysph/examples/dam_break/dam_break_3d_lobovsky.py`: `WCSPHScheme` + +`EPECIntegrator` + `WendlandQuintic(dim=3)`, container ~5.367 x 0.5 x 1.5, +fluid column 2.0 x 0.5 x 1.0, `dx=H/30`, `hdx=1.3`, `rho0=1000`, `gamma=7`, +`alpha=0.25`, `beta=0`, `gz=-9.81`, `hg_correction=True`, `tf=2.5`, `n_damp=50`. + +## Active physics / integration + +- **WendlandQuintic** kernel (new device id 2), `radius_scale=2.0` (C2 support + `q<2`). +- **Tait EOS** on the fluid; **`TaitEOSHGCorrection`** (clamp `rho>=rho0` so + `p>=0`) on the wall. +- **Pressure gradient + Monaghan artificial viscosity** summed over + `[fluid, wall]`; **continuity** for the fluid over `[fluid, wall]` and for the + wall over `[fluid]` only. +- **XSPH** position correction from fluid neighbours only. +- **Gravity** added to the fluid acceleration (full strength). +- **Fixed walls**: walls start at rest with zero acceleration, so the shared + PEC stage leaves their position/velocity unchanged while their density (hence + pressure) responds to approaching fluid. +- **Adaptive timestep** with PySPH's `n_damp` startup **timestep** damping (see + parity notes). +- Grid-direct neighbour traversal throughout; all additions are additive to the + backend (the 2D elliptical-drop path is untouched). + +## Validation tiers (all gate on `all_finite`) + +### Tier 0 -- runner smoke (`run_correctness.sh`) + +`dam_break_3d_runner.py --dx 0.1 --steps 20`: 1000 fluid + 3824 wall particles +built from the geometry; `all_finite: true`; walls fixed; `c0 = 32.85` +(reference scheme value); gravity pulls the fluid down (`w_mean < 0`); +`wall_p_min = 0` (HG clamp); `rho ~ rho0` (near rest at this short horizon). + +### Tier 1 -- hand-rolled CPU parity (`compare_warp_pysph_dam_break_3d.py`) + +A CPU EPEC reimplementation (`LinkedListNNPS(dim=3)` + `WendlandQuintic(dim=3)`) +that mirrors `wc_sph_dam_break_step` block-for-block, advanced beside Warp with a +**fixed dt, no damping, full gravity** for a clean fp32-vs-fp64 diff +(`--dx 0.15 --steps 3 --dt 1e-4`, 234 fluid + 1809 wall): + +| field group | result | +| --- | --- | +| x, y, z, u, v, w, rho (fluid); rho (wall) | match to **~1e-8** relative | +| pressure (fluid, wall) | abs diff **0.17 Pa** vs the fp32 Tait floor **2.06 Pa** -> PASS | + +Pressure is gated on an **absolute** tolerance derived from the fp32 Tait-EOS +cancellation floor `rho0*c0^2*2^-23` (see parity notes), not a relative one. + +### Tier 2 -- resolved parity vs the real PySPH Application (`resolved_dam_break_3d_comparison.py`) + +Subprocesses `dam_break_3d_lobovsky.py` (coarse `--dx 0.12 --tf 0.05`), loads the +`t~0` dump as a shared IC, and steps Warp to each CPU checkpoint time +(512 fluid + 2647 wall). Signed `warp - cpu` deltas: + +| observable | t=0.0017 (cp0) | t=0.05 (cp3, developed) | +| --- | --- | --- | +| per-particle x / z (max abs) | 1.0e-7 / 6.6e-8 | -- | +| kinetic energy (rel) | 1.7e-5 | 1.9e-4 | +| surge-front x (rel) | 5e-8 | 8.6e-7 | +| max height (abs, m) | 4e-8 | 3.5e-6 | +| rho_max (rel) | 1.3e-7 | 1.8e-6 | +| **p_max (rel)** | **~13%** (near rest) | **~1.1%** (developed) | +| wall p_max (rel) | ~13% | ~1.3% | + +This is the key result: kinematics, energy, surge front, and density agree to +fp32 across the whole short horizon; pressure starts at the fp32 cancellation +floor (near rest) and **recovers to ~1% relative once the column collapse builds +a real pressure field** -- exactly the tier-1 prediction. + +## Parity notes (honest deltas vs the reference) + +- **fp32 Tait-EOS cancellation floor.** Near rest `rho ~ rho0`, so + `p = B*((rho/rho0)^gamma - 1)` is a tiny difference of near-equal quantities. + fp32 stores `rho ~ 1000` with absolute error `~rho0*2^-23 ~ 1.2e-4`, and + `dp/drho ~ B*gamma/rho0 ~ 1079`, so `dp ~ 0.13 Pa` per particle -- matching the + observed ~0.17 Pa. The *relative* p error is large only because p itself is + ~1 Pa near rest; in *absolute* terms pressure matches, and relative agreement + recovers as p grows. Tier-1 gates pressure on this absolute floor. +- **EPEC, not "PEC vs EPEC".** `wc_sph_dam_break_step` evaluates accelerations, + predicts to the half step, re-evaluates, then corrects (E-P-E-C). That matches + the reference `EPECIntegrator`, so the ADR-0005 "EPEC vs PEC" risk is resolved + -- the reference is run with its native `EPECIntegrator`. +- **`n_damp` damps the timestep, not gravity.** PySPH `Solver._damp_timestep` + scales `dt` by `0.5*(sin(pi*(-0.5 + (count+1)/n_damp)) + 1)` over the first + `n_damp` steps; it does **not** ramp gravity. The runner therefore applies that + factor to the adaptive `dt` (via `adaptive_dt_scale`) with full gravity. The + additive `gravity_ramp` backend feature exists but is left at 1.0 here. (ADR-0005 + step 3 described a gravity ramp; the PySPH-faithful choice is timestep damping.) +- **`c0` reference inconsistency.** The reference builds its `WCSPHScheme` with + the module constant `c0 = 10*sqrt(2*9.81*0.55) ~ 32.85` (the physics sound + speed) but computes its initial `dt` from `10*get_max_speed = 10*sqrt(2*9.81*H) + ~ 44.29`. The runner matches the physics value (32.85) for `c0` and uses the + 44.29 form only for the initial `dt` cap, exactly as the reference does. +- **Cache stability (ADR-0005, precise statement).** The *generated* group source + for the cubic/gaussian 2D kernels is **byte-identical** (kernel choice is a + runtime `kernel_id`, not source) and is pinned by + `test_2d_path_generated_source_is_byte_identical_to_golden`. The cubic (id 0) + and gaussian (id 1) numerical code paths in the shared `_kernel_value/dwdq` + routers are **unchanged** (only an additive `id==2` branch). The router source + itself grew by that branch, so a one-time, **logic-preserving** recompile of + the warm cache can occur; it does not perturb the committed 2D baseline. (This + refines the ADR's "on-disk cache byte-identical" wording -- see the ADR update.) + +## How to run + +```bash +PY=/home/kunalp/.pqt_venv_e0b41259/bin/python # venv with warp+pysph +# smoke +ROOT=$(git rev-parse --show-toplevel) bash /run_correctness.sh +# tier-1 (hand-rolled CPU parity) +$PY /compare_warp_pysph_dam_break_3d.py --dx 0.15 --steps 3 --dt 1e-4 +# tier-2 (real PySPH Application) +$PY /resolved_dam_break_3d_comparison.py --dx 0.12 --tf 0.05 --pfreq 15 +``` + +Performance + representative snapshot (real PySPH CPU vs Warp fp32, same `tf`): + +```bash +$PY /perf_and_snapshot_dam_break_3d.py --dx 0.08 --tf 0.4 +``` + +At `dx=0.08` (7,392 particles), RTX 4060 fp32 vs single-threaded PySPH Cython +fp64, `tf=0.4 s`: per-step **8.5 ms (Warp) vs 13.0 ms (CPU) = ~1.53x**, and total +wall **3.92 s vs 6.01 s = ~1.53x** -- per-step and wall agree because the +adaptive-dt schedules match (Warp 460 steps vs CPU 461). Note: the reference's +`dt = 0.25*h0/(1.1*c_max)` is only the *seed* dt; PySPH grows dt to the +CFL-limited value with no clamp, so the runner must NOT cap at the seed +(`dt_max` defaults to uncapped -- CFL + `n_damp` govern). This is the small-N / +overhead-bound regime; the committed cross-GPU sweep measured ~57.6x per-step at +1M particles on the same 4060. + +The snapshot below (x-z, fluid by speed, walls grey, `t=0.4 s`) shows the two +runs visually indistinguishable: + +![CPU fp64 vs Warp fp32 dam-break snapshot at t=0.4 s](cpu-vs-warp-snapshot.png) + +### Large-N (>1M particles) and a 3D-explicit snapshot + +`bench_1M_and_3d_snapshot.py` -- per-step throughput at scale (a developed 1M run +to a physical `tf` is multi-hour on the CPU, so per-step over a fixed step count +is used, as in the committed 1M elliptical comparison): + +```bash +$PY /bench_1M_and_3d_snapshot.py --dx 0.0108 --snapshot-tf 0 # perf only +$PY /bench_1M_and_3d_snapshot.py --dx 0.011 --snapshot-tf 0.2 # + 3D snapshot +``` + +At **1,014,072 particles**: Warp fp32 (fused) **0.337 s/step (3.01 M +particle-steps/s)** vs PySPH CPU fp64 **4.5-5.0 s/step (~0.21 M)** = **~13-15x +per-step** on the RTX 4060 (CPU per-step has ~10-20% run-to-run variance; Warp is +stable). The GPU advantage scales with N (1.53x at 7k -> ~14x at >1M). The fluid +acceleration+density blocks are **fused** (pressure + AV + continuity in one +kernel per source) -- this made the Warp step 1.23x faster (0.415 -> 0.337 s/step) +than the initial single-block composition. It is still below the elliptical +drop's 57.6x at 1M because XSPH (fluid-only) and wall continuity (fluid->wall) +have heterogeneous source/destination sets and stay separate, so the step is not a +single fused kernel. + +The four-view snapshot at 999,975 particles (`t=0.2 s`) makes the 3D structure +explicit (x-z side, x-y top-down, y-z end, 3D scatter): + +![Warp fp32 3D dam break, ~1M particles, four views at t=0.2 s](bench-1M-3d-snapshot.png) + +### Cross-GPU sweep + +`gpu_perf_sweep_dam_break.py` sweeps particle count (dx) per GPU; lenses + +figures in `gpu-sweep/` (`README.md`). At ~1M, fixed-dt fused step: RTX PRO 6000 +Blackwell 39.1 Mp-st/s (163x vs 1 CPU core) > L40S 25.9 Mp-st/s (108x) > RTX 4060 +4.5 Mp-st/s (19x). Cost/energy favour the L40S ($0.0114 vs $0.0187 per billion +p-steps; 13.5 vs 15.4 kJ/billion). (5090 + B300 deferred.) + +Artifacts: `results-smoke.npz`, `comparison-tier1-summary.json` (+ warp npz), +`comparison-resolved-summary.json`, `cpu-vs-warp-perf.json`, +`cpu-vs-warp-snapshot.png`, `bench-1M-perf.json`, `bench-1Mplus-perf.json`, +`bench-1M-3d-snapshot.png`. + +### Medium-resolution showcase run (2026-06-20) + +Ran a developed Warp-only case at `dx=0.025`: 59,280 fluid + 66,407 wall = +125,687 total particles. A 10-step timing slice measured `0.03140 s/step` +versus `0.42733 s/step` for the single-threaded PySPH CPU baseline (`13.61x`). +The adaptive GPU run then advanced 2,837 steps to `t=0.8016 s`: + +```text +all_finite: true +rho_min / rho_max: 989.7354 / 1014.1267 kg/m^3 +surge_front_x: 5.08698 m +max_height: 0.84473 m +kinetic_energy: 1226.1269 +wall_p_min / wall_p_max: 0.0 / 24391.78 Pa +``` + +Also captured the visually stronger collapse phase at 1,415 steps / `t=0.3996` +s (`all_finite=true`, surge front 3.1553 m, max height 0.9391 m). Artifacts: + +- `showcase-dx025-t080-perf.json` +- `reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png` + (four-view verification figure, embedded in the review) +- `reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png` + (PyVista hero frame, particles coloured by speed, embedded in the review) + +Splashsurf surface reconstruction was exercised successfully, but Blender and +ffmpeg are not installed on this host; large intermediate PLY meshes were not +retained. The hero is an honest particle visualization, not a photorealistic +render. + +## What to expect + +- non-empty fluid + wall arrays, `all_finite: true`, walls fixed; +- tier-1 `passed: true` (kinematics/density relative, pressure absolute floor); +- tier-2 fp32-scale deltas on KE / surge front / height / density, and pressure + recovering to ~1% relative as the flow develops. + +## Out of scope (follow-ups) + +- SPHERIC/Kleefsman obstacle case (third array). +- Long-horizon (`tf=2.5`) run + probe-point pressure `p/(rho g H)` vs + `db_exp_data.get_lobovsky_data()` (the full experimental comparison; needs an + SPH interpolator and a multi-second run). +- A cubic/gaussian dam-break cross-check; performance / cross-GPU + characterisation (correctness-only here). diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/README.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/README.md new file mode 100644 index 000000000..f504ee272 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/README.md @@ -0,0 +1,88 @@ +# Cross-GPU sweep -- Warp 3D dam-break (fused, fp32) + +Per-step throughput of the fused `wc_sph_dam_break_step` (Lobovsky no-obstacle: +fluid + one wall array, WendlandQuintic, Tait + Tait-HG walls, gravity, fp32) vs +particle count, on each GPU. Generated by `gpu_perf_sweep_dam_break.py` (run on +each GPU, GPU-only) + `plot_gpu_sweep_dam_break.py` (lenses + figures). + +Methodology mirrors the elliptical-drop sweep: **fixed dt** per-step (no adaptive +CFL kernel / dt readback), so the number is the step's pure compute throughput, +comparable across GPUs. The production runner uses adaptive dt (one extra CFL +kernel + a scalar device->host readback per step), ~1.5x slower per step at 1M, +so end-to-end production speedups are correspondingly lower. + +Sweep: dx = 0.04 -> 0.005 over the Lobovsky geometry = **32k -> 9.49M particles** +(identical counts on every GPU; ~1M point = dx=0.011 = 999,975). steps=12, +warmup=4. The fused kernels cold-compile once per cold disk cache (~145-200 s, +absorbed by warmup). Per-point OOM is recorded; both cards here cleared the full +list (L40S 44 GiB, RTX PRO 6000 95 GiB). + +GPUs run so far: **L40S, RTX PRO 6000 Blackwell** + an RTX 4060 Laptop anchor. +(RTX 5090 and B300 deferred -- drop their `sweep-dambreak-*.json` here and re-run +the plot script to extend every lens.) + +## Lenses at ~1M particles (999,975), fixed-dt fused step + +| GPU | throughput (Mp-st/s) | per-step (ms) | vs 1 CPU core | $ / billion p-st | kJ / billion p-st | perf/W (p-st/s/W) | MBU% (rough) | +|---|---:|---:|---:|---:|---:|---:|---:| +| RTX PRO 6000 Blackwell (sm_120) | 39.1 | 25.6 | 163x | $0.0187 | 15.4 | 65,099 | ~24 | +| L40S (sm_89) | 25.9 | 38.7 | 108x | **$0.0114** | **13.5** | **73,908** | ~33 | +| RTX 4060 Laptop (sm_89) | 4.5 | 220.1 | 19x | n/a (laptop) | 25.3 | 39,513 | ~19 | + +CPU ref: single-thread PySPH Cython dam-break Application **4.18 s/step** at +999,975 particles (run-to-run 4.2-5.0 s/step; the CPU runs adaptive dt). + +## Findings per lens + +- **Throughput / speed.** RTX PRO 6000 leads at every resolution past ~150k and + is still climbing at 9.5M (45 Mp-st/s, 95 GiB headroom); the L40S saturates at + ~25-27 Mp-st/s from ~600k on. At <100k both Blackwell-class and Ada are tied -- + launch/grid-build bound (the GPU is starved). At 1M, RTX PRO 6000 is **~1.5x** + the L40S. +- **Speedup vs 1 CPU core** (fixed-dt compute, vs 4.18 s/step CPU): **163x** (RTX + PRO 6000), **108x** (L40S), **19x** (4060) at 1M. (The adaptive production step + is ~1.5x slower, so production end-to-end is lower -- e.g. ~12x on the 4060.) +- **$ cost-of-compute** (Brev hourly): the **L40S is cheaper per unit work** + ($0.0114 vs $0.0187 per billion p-steps) -- its $1.06/hr beats the RTX PRO + 6000's $2.63/hr by more than the ~1.5x throughput gap. Same pattern as the + elliptical sweep: cheaper cards do the same work for less money. +- **Energy-to-solution / perf-per-watt** (datasheet TDP estimate): the **L40S + also wins on energy** -- 13.5 vs 15.4 kJ/billion, 73.9k vs 65.1k p-steps/s/W -- + its 350 W beats the RTX PRO 6000's 600 W. (Estimate from board TDP; the step is + not FLOP-bound so true draw is below nameplate -- an upper bracket, not a + measurement.) +- **Roofline / bandwidth utilization** (ROUGH analytic, ~11 KB/p-step +/-2x): ~33% + (L40S) / ~24% (RTX PRO 6000) of peak DRAM BW at 1M -- notably higher than the + elliptical drop's ~12% because the fused multi-array dam-break step moves ~10x + more bytes per particle-step. Still <50%, so it is **not yet bandwidth-bound**: + there is occupancy/launch headroom (consistent with the small-N launch-bound + plateau). A profiler run would be needed for a real number. + +Versus the **elliptical drop** at 1M on the same cards: dam-break throughput is +~3.5x lower (25.9 vs 91.9 Mp-st/s on the L40S) -- expected, since each dam-break +step does pressure + AV + continuity over two source arrays + XSPH + wall +continuity + two EOS + an EPEC double evaluation, vs the elliptical's single +fused continuity kernel. + +## Figures + +![throughput vs particles](db_throughput_vs_particles.png) + +![per-step vs particles](db_perstep_vs_particles.png) + +![speedup vs 1 CPU core at 1M](db_speedup_vs_cpu_1M.png) + +![$ per billion particle-steps at 1M](db_cost_per_billion_1M.png) + +![energy per billion particle-steps at 1M (TDP estimate)](db_energy_per_gpstep_1M.png) + +![analytic memory-bandwidth utilization at 1M (rough)](db_mbu_at_1M.png) + +## Caveats + +- Fixed-dt compute throughput; production adaptive dt is ~1.5x slower/step. +- Two cloud GPUs + a 4060 anchor (5090/B300 deferred). +- $ and energy/perf-W are **estimates** from public hourly rates and datasheet + TDP; no measured power. MBU uses a rough ~11 KB/p-step byte model (+/-2x), no + profiler counter. +- CPU reference has ~10-20% run-to-run variance on the laptop host. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_cost_per_billion_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_cost_per_billion_1M.png new file mode 100644 index 000000000..11300c95f Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_cost_per_billion_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_energy_per_gpstep_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_energy_per_gpstep_1M.png new file mode 100644 index 000000000..69bafc2f4 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_energy_per_gpstep_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_mbu_at_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_mbu_at_1M.png new file mode 100644 index 000000000..5095ef8f2 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_mbu_at_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_perstep_vs_particles.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_perstep_vs_particles.png new file mode 100644 index 000000000..1bef8a477 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_perstep_vs_particles.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_speedup_vs_cpu_1M.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_speedup_vs_cpu_1M.png new file mode 100644 index 000000000..00a7bd63f Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_speedup_vs_cpu_1M.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_throughput_vs_particles.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_throughput_vs_particles.png new file mode 100644 index 000000000..d4c0be146 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/db_throughput_vs_particles.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/plot_gpu_sweep_dam_break.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/plot_gpu_sweep_dam_break.py new file mode 100644 index 000000000..1cfd01c49 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/plot_gpu_sweep_dam_break.py @@ -0,0 +1,204 @@ +#!/usr/bin/env python3 +"""Cross-GPU lenses + figures for the Warp 3D dam-break sweep. + +Reads gpu-sweep/sweep-dambreak-*.json (one per GPU) and writes PNGs + prints a +lens table. Pure data plotting -- no GPU required. Run: + + python .../2026-06-18_warp-dam-break-3d-runner/gpu-sweep/plot_gpu_sweep_dam_break.py + +Lenses (mirrors the elliptical-drop sweep): +- throughput vs particles, per-step vs particles +- speedup vs a single CPU core at ~1M +- $ cost-of-compute (Brev hourly), at 1M (bar) + vs particles (curve) +- energy-to-solution (kJ/billion, TDP estimate) and perf-per-watt +- analytic roofline / memory-bandwidth utilization (rough byte model) + +All GPU numbers are the FIXED-dt fused dam-break step (the sweep's metric) -- +the same methodology as the elliptical sweep. The production runner uses adaptive +dt (one extra CFL kernel + a scalar readback per step), ~1.5x slower per step at +1M, so end-to-end production speedups are correspondingly lower. +""" + +import glob +import json +import os + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +HERE = os.path.dirname(os.path.abspath(__file__)) + +# The sweep's ~1M point (dx=0.011 over the Lobovsky no-obstacle geometry). +AT_1M = 999975 + +# Single-thread PySPH Cython dam-break Application at 999,975 particles: +# 4.18 s/step (dx=0.011-matched measurement; run-to-run 4.2-5.0 s/step on the +# 4060 host). The CPU runs adaptive dt; its per-step compute is the reference. +CPU_REF_S_PER_STEP_AT_1M = 4.18 + +# NVIDIA Brev on-demand hourly rates (USD/hr), 2026-06 (same table as the +# elliptical sweep). Cards without a measured sweep are simply skipped. +BREV_COST_PER_HR = {'B300': 9.49, 'RTX PRO 6000': 2.63, 'L40S': 1.06, + 'RTX 5090': 0.78} +BOARD_TDP_W = {'B300': 1400.0, 'RTX PRO 6000': 600.0, 'RTX 5090': 575.0, + 'L40S': 350.0, 'RTX 4060': 115.0} +PEAK_BW_TBS = {'B300': 8.0, 'RTX PRO 6000': 1.79, 'RTX 5090': 1.79, + 'L40S': 0.864, 'RTX 4060': 0.27} + +# Rough effective DRAM traffic per particle-step (bytes) for the fused dam-break +# step: ~73 3D neighbours x ~44 B (geom + m/rho/p/cs + vel) in the fused +# pressure+AV+continuity kernel, + an XSPH pass (~60 nbr x ~36 B), x2 EPEC accel +# evaluations, + own-state/PEC/EOS (~0.3 KB). ~11 KB. This is an ORDER-OF- +# MAGNITUDE estimate (no profiler; ignores L2 reuse; +/- ~2x), ~10x the +# elliptical's 1.12 KB single-continuity kernel. MBU below is correspondingly +# rough. +B_EFF_BYTES = 11000.0 + +# RTX 4060 Laptop anchor (local 2-point run, fixed-dt fused step). +ANCHOR_4060 = { + 'name': 'RTX 4060 Laptop (sm_89)', + 'points': [(32254, 0.014128, 2283000.0), (AT_1M, 0.220072, 4544000.0)], +} + + +def _lookup(table, name): + for key, val in table.items(): + if key in name: + return val + return None + + +def _label(hw): + name = hw['name'].replace('NVIDIA ', '').replace(' Server Edition', '') + return '%s (%s)' % (name, hw['arch']) + + +def load_gpus(): + gpus = [] + for f in sorted(glob.glob(os.path.join(HERE, 'sweep-dambreak-*.json'))): + d = json.load(open(f)) + pts = [(r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s']) + for r in d['results'] if 'error' not in r] + gpus.append({'name': _label(d['hardware']), 'points': sorted(pts)}) + gpus.append(ANCHOR_4060) + gpus.sort(key=lambda g: g['points'][-1][2]) + return gpus + + +def at_1m(g): + pts = [p for p in g['points'] if p[0] == AT_1M] + return pts[0] if pts else None + + +def cost_per_billion(throughput, cost_hr): + return cost_hr / (throughput * 3600.0) * 1e9 + + +def main(): + gpus = load_gpus() + + # ---- lens table at ~1M ---- + print('\n=== Lenses at ~1M particles (%d), fixed-dt fused dam-break step ===' + % AT_1M) + hdr = ('GPU', 'thru(Mp-st/s)', 'per-step(ms)', 'vs CPU', '$/B p-st', + 'kJ/B p-st', 'perf/W(p-st/s/W)', 'MBU%~') + print('| %-26s | %12s | %11s | %7s | %9s | %9s | %16s | %6s |' % hdr) + print('|%s|' % ('|'.join(['-' * w for w in + (28, 14, 13, 9, 11, 11, 18, 8)]))) + rows = {} + for g in sorted(gpus, key=lambda g: (at_1m(g) or (0, 0, 0))[2], + reverse=True): + p = at_1m(g) + if not p: + continue + thru, per = p[2], p[1] + cost = _lookup(BREV_COST_PER_HR, g['name']) + tdp = _lookup(BOARD_TDP_W, g['name']) + peak = _lookup(PEAK_BW_TBS, g['name']) + speedup = CPU_REF_S_PER_STEP_AT_1M / per + cpb = cost_per_billion(thru, cost) if cost else None + kjpb = tdp / thru * 1e6 if tdp else None + ppw = thru / tdp if tdp else None + mbu = thru * B_EFF_BYTES / (peak * 1e12) * 100 if peak else None + rows[g['name']] = dict(thru=thru, per=per, speedup=speedup, cpb=cpb, + kjpb=kjpb, ppw=ppw, mbu=mbu) + print('| %-26s | %12.1f | %11.2f | %6.0fx | %9s | %9s | %16s | %6s |' % ( + g['name'], thru / 1e6, per * 1e3, speedup, + ('$%.4f' % cpb) if cpb else 'n/a', + ('%.1f' % kjpb) if kjpb else 'n/a', + ('%.0f' % ppw) if ppw else 'n/a', + ('%.0f' % mbu) if mbu else 'n/a')) + print('\nCPU ref: single-thread PySPH Cython %.2f s/step at %d particles. ' + 'GPU = fixed-dt fused step.' % (CPU_REF_S_PER_STEP_AT_1M, AT_1M)) + + # ---- figures ---- + def _curve(ykey, ylabel, title, fname, ylog=False): + fig, ax = plt.subplots(figsize=(8, 5)) + for g in gpus: + xs = [p[0] for p in g['points']] + ys = [ykey(p) for p in g['points']] + style = 'o' if len(xs) == 1 else 'o-' + ax.plot(xs, ys, style, label=g['name'], markersize=5) + ax.set_xscale('log') + if ylog: + ax.set_yscale('log') + ax.set_xlabel('particles') + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.grid(True, which='both', alpha=0.3) + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(os.path.join(HERE, fname), dpi=130) + plt.close(fig) + + _curve(lambda p: p[2] / 1e6, 'throughput (million particle-steps / s)', + 'Warp 3D dam-break (fused, fp32) -- throughput vs particle count', + 'db_throughput_vs_particles.png') + _curve(lambda p: p[1], 'per-step wall time (s)', + 'Warp 3D dam-break (fused, fp32) -- per-step time vs particle count', + 'db_perstep_vs_particles.png', ylog=True) + + def _bar(metric, fmt, xlabel, title, fname, color, reverse): + data = [(name, r[metric]) for name, r in rows.items() + if r.get(metric) is not None] + data.sort(key=lambda r: r[1], reverse=reverse) + names = [d[0] for d in data] + vals = [d[1] for d in data] + fig, ax = plt.subplots(figsize=(8.5, 4.4)) + bars = ax.barh(names, vals, color=color, alpha=0.85) + for b, v in zip(bars, vals): + ax.text(b.get_width(), b.get_y() + b.get_height() / 2, + ' ' + fmt % v, va='center', fontsize=9) + ax.set_xlim(0, max(vals) * 1.18) + ax.set_xlabel(xlabel) + ax.set_title(title) + ax.grid(True, axis='x', alpha=0.3) + fig.tight_layout() + fig.savefig(os.path.join(HERE, fname), dpi=130) + plt.close(fig) + + _bar('speedup', '%.0fx', + 'speedup vs single CPU core (PySPH ~%.1f s/step at 1M)' + % CPU_REF_S_PER_STEP_AT_1M, + 'GPU speedup vs 1 CPU core @ 1M (dam-break, fixed-dt fused, fp32)', + 'db_speedup_vs_cpu_1M.png', 'tab:green', reverse=False) + _bar('cpb', '$%.4f', '$ per billion particle-steps (lower = cheaper)', + 'Cost of compute @ 1M (dam-break; NVIDIA Brev hourly pricing)', + 'db_cost_per_billion_1M.png', 'tab:red', reverse=True) + _bar('kjpb', '%.1f kJ', 'kJ per billion particle-steps (TDP estimate)', + 'Energy-to-solution @ 1M (dam-break; ESTIMATE from board TDP)', + 'db_energy_per_gpstep_1M.png', 'tab:orange', reverse=True) + _bar('mbu', '%.0f%%', + 'analytic memory-bandwidth utilization @ 1M (% of peak, ROUGH)', + 'Roofline @ 1M (dam-break; ANALYTIC ~11 KB/p-step, +/-2x)', + 'db_mbu_at_1M.png', 'tab:purple', reverse=False) + + print('\nwrote db_throughput_vs_particles.png, db_perstep_vs_particles.png, ' + 'db_speedup_vs_cpu_1M.png, db_cost_per_billion_1M.png, ' + 'db_energy_per_gpstep_1M.png, db_mbu_at_1M.png to %s' % HERE) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-l40s.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-l40s.json new file mode 100644 index 000000000..f076b7cb6 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-l40s.json @@ -0,0 +1,40 @@ +{ + "benchmark": "warp 3D dam-break fused WCSPH step (fp32)", + "hardware": { + "name": "L40S", + "arch": "sm_89", + "memory_gib": 44.4, + "warp": "1.14.0" + }, + "config": { + "dt": 0.0001, + "steps": 12, + "warmup_discarded": 4, + "physics": { + "rho0": 1000.0, + "c0": 32.849657532461435, + "gamma": 7.0, + "alpha": 0.25, + "beta": 0.0, + "kernel": "wendland", + "xsph_eps": 0.5, + "gz": -9.81 + }, + "radius_scale": 2.0, + "hdx": 1.3, + "geometry": "Lobovsky no-obstacle", + "cold_compile_s": 198.04 + }, + "results": [ + {"dx": 0.04, "particles": 32254, "fluid": 16250, "wall": 16004, "per_step_s_min": 0.004976, "per_step_s_median": 0.005042, "per_step_s_max": 0.005082, "throughput_particle_steps_per_s": 6397173.1, "all_finite": true}, + {"dx": 0.03, "particles": 71867, "fluid": 34848, "wall": 37019, "per_step_s_min": 0.005469, "per_step_s_median": 0.005526, "per_step_s_max": 0.005558, "throughput_particle_steps_per_s": 13006217.6, "all_finite": true}, + {"dx": 0.022, "particles": 158651, "fluid": 89100, "wall": 69551, "per_step_s_min": 0.008626, "per_step_s_median": 0.008675, "per_step_s_max": 0.008742, "throughput_particle_steps_per_s": 18288044.6, "all_finite": true}, + {"dx": 0.016, "particles": 367292, "fluid": 240250, "wall": 127042, "per_step_s_min": 0.015353, "per_step_s_median": 0.015419, "per_step_s_max": 0.015535, "throughput_particle_steps_per_s": 23820232.4, "all_finite": true}, + {"dx": 0.013, "particles": 633538, "fluid": 441864, "wall": 191674, "per_step_s_min": 0.024763, "per_step_s_median": 0.024871, "per_step_s_max": 0.025077, "throughput_particle_steps_per_s": 25473412.7, "all_finite": true}, + {"dx": 0.011, "particles": 999975, "fluid": 733050, "wall": 266925, "per_step_s_min": 0.038515, "per_step_s_median": 0.038657, "per_step_s_max": 0.039102, "throughput_particle_steps_per_s": 25867836.2, "all_finite": true}, + {"dx": 0.009, "particles": 1752448, "fluid": 1355310, "wall": 397138, "per_step_s_min": 0.065818, "per_step_s_median": 0.0663, "per_step_s_max": 0.066502, "throughput_particle_steps_per_s": 26432063.0, "all_finite": true}, + {"dx": 0.0075, "particles": 2906784, "fluid": 2334948, "wall": 571836, "per_step_s_min": 0.110765, "per_step_s_median": 0.112063, "per_step_s_max": 0.112401, "throughput_particle_steps_per_s": 25938908.2, "all_finite": true}, + {"dx": 0.006, "particles": 5480298, "fluid": 4588074, "wall": 892224, "per_step_s_min": 0.217808, "per_step_s_median": 0.220719, "per_step_s_max": 0.221015, "throughput_particle_steps_per_s": 24829314.9, "all_finite": true}, + {"dx": 0.005, "particles": 9486957, "fluid": 7880400, "wall": 1606557, "per_step_s_min": 0.346884, "per_step_s_median": 0.350562, "per_step_s_max": 0.350962, "throughput_particle_steps_per_s": 27062121.9, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-rtxpro6000.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-rtxpro6000.json new file mode 100644 index 000000000..9ff45931f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu-sweep/sweep-dambreak-rtxpro6000.json @@ -0,0 +1,40 @@ +{ + "benchmark": "warp 3D dam-break fused WCSPH step (fp32)", + "hardware": { + "name": "RTX PRO 6000 Blackwell", + "arch": "sm_120", + "memory_gib": 95.0, + "warp": "1.14.0" + }, + "config": { + "dt": 0.0001, + "steps": 12, + "warmup_discarded": 4, + "physics": { + "rho0": 1000.0, + "c0": 32.849657532461435, + "gamma": 7.0, + "alpha": 0.25, + "beta": 0.0, + "kernel": "wendland", + "xsph_eps": 0.5, + "gz": -9.81 + }, + "radius_scale": 2.0, + "hdx": 1.3, + "geometry": "Lobovsky no-obstacle", + "cold_compile_s": 145.58 + }, + "results": [ + {"dx": 0.04, "particles": 32254, "fluid": 16250, "wall": 16004, "per_step_s_min": 0.005448, "per_step_s_median": 0.005466, "per_step_s_max": 0.005518, "throughput_particle_steps_per_s": 5900852.4, "all_finite": true}, + {"dx": 0.03, "particles": 71867, "fluid": 34848, "wall": 37019, "per_step_s_min": 0.006231, "per_step_s_median": 0.006236, "per_step_s_max": 0.006267, "throughput_particle_steps_per_s": 11523879.9, "all_finite": true}, + {"dx": 0.022, "particles": 158651, "fluid": 89100, "wall": 69551, "per_step_s_min": 0.007521, "per_step_s_median": 0.007612, "per_step_s_max": 0.007643, "throughput_particle_steps_per_s": 20842763.6, "all_finite": true}, + {"dx": 0.016, "particles": 367292, "fluid": 240250, "wall": 127042, "per_step_s_min": 0.011435, "per_step_s_median": 0.011504, "per_step_s_max": 0.011562, "throughput_particle_steps_per_s": 31927970.7, "all_finite": true}, + {"dx": 0.013, "particles": 633538, "fluid": 441864, "wall": 191674, "per_step_s_min": 0.017385, "per_step_s_median": 0.017479, "per_step_s_max": 0.017618, "throughput_particle_steps_per_s": 36245002.7, "all_finite": true}, + {"dx": 0.011, "particles": 999975, "fluid": 733050, "wall": 266925, "per_step_s_min": 0.025436, "per_step_s_median": 0.025601, "per_step_s_max": 0.025768, "throughput_particle_steps_per_s": 39059394.2, "all_finite": true}, + {"dx": 0.009, "particles": 1752448, "fluid": 1355310, "wall": 397138, "per_step_s_min": 0.042633, "per_step_s_median": 0.042907, "per_step_s_max": 0.043053, "throughput_particle_steps_per_s": 40842545.8, "all_finite": true}, + {"dx": 0.0075, "particles": 2906784, "fluid": 2334948, "wall": 571836, "per_step_s_min": 0.067804, "per_step_s_median": 0.068542, "per_step_s_max": 0.068948, "throughput_particle_steps_per_s": 42409004.9, "all_finite": true}, + {"dx": 0.006, "particles": 5480298, "fluid": 4588074, "wall": 892224, "per_step_s_min": 0.124892, "per_step_s_median": 0.126501, "per_step_s_max": 0.126966, "throughput_particle_steps_per_s": 43322210.7, "all_finite": true}, + {"dx": 0.005, "particles": 9486957, "fluid": 7880400, "wall": 1606557, "per_step_s_min": 0.209162, "per_step_s_median": 0.211193, "per_step_s_max": 0.214387, "throughput_particle_steps_per_s": 44920867.4, "all_finite": true} + ] +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu_perf_sweep_dam_break.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu_perf_sweep_dam_break.py new file mode 100644 index 000000000..cbb799efc --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/gpu_perf_sweep_dam_break.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 +"""Cross-GPU performance sweep for the Warp 3D dam-break (fused) step. + +Sweeps particle count (via ``dx`` over the Lobovsky no-obstacle geometry) and, +for each, measures the steady per-step wall time and throughput of the fused +``wc_sph_dam_break_step`` on the current GPU. Run it on each GPU and paste the +JSON block; the per-GPU JSONs together form a cross-GPU performance artifact +(mirrors ``gpu_perf_sweep.py`` for the elliptical drop). + +GPU-only on purpose: the single-threaded PySPH CPU Application is slow at scale, +so the CPU-vs-Warp speedup is a separate 1M headline. Here the cross-GPU +comparison comes from running the same ``dx`` list on each GPU and comparing +throughput vs particle count. + +Usage (run from the repo root, with the warp+pysph venv active): + + R=.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner + python $R/gpu_perf_sweep_dam_break.py --label "L40S" --output sweep-dambreak-l40s.json + +Notes: +- Physics matches the Lobovsky no-obstacle case: WendlandQuintic, Tait EOS, + Tait-HG walls, gravity, fluid+wall arrays, radius_scale=2, fixed dt, fp32. +- The step is run with a FIXED dt (adaptive off) for clean per-step timing, like + the elliptical sweep. The dt value does not affect per-step compute time. +- The initial condition is built with vectorized numpy masks (same box + conditions as DamBreak3DGeometry) -- NOT the example's Python per-point loop -- + so building 1M-10M particle ICs is seconds, not minutes. +- The fused dam-break kernels cold-compile once on a cold disk cache (reported as + cold_compile_s); a warmup run absorbs it so the swept points are warm. +- Out-of-memory at large dx is caught per point and recorded, so the sweep finds + the GPU's capacity ceiling without aborting. +- dx -> particles (Lobovsky no-obstacle): 0.04 ~ 39k, 0.011 ~ 1.0M, + 0.009 ~ 1.8M, 0.006 ~ 5.6M, 0.005 ~ 9.5M (fluid + one wall array). +""" + +from __future__ import annotations + +import argparse +import gc +import json +import math +import statistics +import time + +import numpy as np +import warp as wp + +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_dam_break_step + +# Lobovsky no-obstacle geometry + physics (matches dam_break_3d_lobovsky.py). +H = 1.0 +GRAVITY = 9.81 +REF_C0 = 10.0 * math.sqrt(2.0 * GRAVITY * 0.55) +HDX = 1.3 +NB_LAYERS = 1 +RADIUS_SCALE = 2.0 +DT = 1.0e-4 +CONTAINER_H, CONTAINER_W, CONTAINER_L = 1.5 * H, H / 2.0, 161 * H / 30.0 +FLUID_H, FLUID_W, FLUID_L = H, H / 2.0, 2.0 * H +PHYS = dict(rho0=1000.0, c0=REF_C0, gamma=7.0, alpha=0.25, beta=0.0, + kernel='wendland', xsph_eps=0.5, gz=-GRAVITY) + + +def _gpu_info(): + try: + devs = wp.get_cuda_devices() + except Exception: + devs = [] + if not devs: + return {'name': 'cpu/none', 'arch': 'n/a', 'memory_gib': None} + d = devs[0] + return { + 'name': d.name, + 'arch': 'sm_%s' % d.arch, + 'memory_gib': round(d.total_memory / 2**30, 1), + 'warp': wp.__version__, + } + + +def _fast_ic(dx): + """Vectorized Lobovsky no-obstacle IC (same box masks as DamBreak3DGeometry).""" + ghost = NB_LAYERS * dx + cw2 = 0.5 * CONTAINER_W + eps = 0.1 * dx + xx, yy, zz = np.mgrid[-ghost:CONTAINER_L + ghost + eps:dx, + -cw2 - ghost:cw2 + ghost + eps:dx, + -ghost:CONTAINER_H + ghost + eps:dx] + x, y, z = xx.ravel(), yy.ravel(), zz.ravel() + fluid_m = ((x > 0) & (x <= FLUID_L) & (y > -cw2) & (y < cw2) + & (z > 0) & (z <= FLUID_H)) + wall_m = ((y <= -cw2) | (y >= cw2) | (x >= CONTAINER_L) | (x <= 0) + | (z <= 0)) + rho0 = PHYS['rho0'] + m0, h0 = rho0 * dx**3, HDX * dx + + def mk(mask, name): + n = int(mask.sum()) + zr = np.zeros(n) + return get_particle_array( + name=name, x=x[mask].copy(), y=y[mask].copy(), z=z[mask].copy(), + h=np.full(n, h0), m=np.full(n, m0), rho=np.full(n, rho0), + p=zr.copy(), cs=np.full(n, PHYS['c0']), + u=zr.copy(), v=zr.copy(), w=zr.copy(), + au=zr.copy(), av=zr.copy(), aw=zr.copy(), arho=zr.copy(), + ax=zr.copy(), ay=zr.copy(), az=zr.copy(), + x0=zr.copy(), y0=zr.copy(), z0=zr.copy(), + u0=zr.copy(), v0=zr.copy(), w0=zr.copy(), rho0=zr.copy(), + backend='warp') + + return mk(fluid_m, 'fluid'), mk(wall_m, 'wall') + + +def _run_steps(nnps, steps): + dev = nnps.device + walls = [] + for i in range(steps): + wp.synchronize_device(dev) + t = time.perf_counter() + wc_sph_dam_break_step( + nnps, fluid_index=0, solid_indices=(1,), dt=DT, adaptive_dt=False, + gravity_ramp=1.0, push=(i == 0), return_dt=True, **PHYS) + wp.synchronize_device(dev) + walls.append(time.perf_counter() - t) + return walls + + +def bench_one(dx, steps, warmup): + fluid, wall = _fast_ic(dx) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall], radius_scale=RADIUS_SCALE) + nf = int(fluid.get_number_of_particles()) + nw = int(wall.get_number_of_particles()) + n = nf + nw + + walls = _run_steps(nnps, steps) + fluid.gpu.pull('x', 'rho') + finite = bool(np.all(np.isfinite(fluid.x)) and np.all(np.isfinite(fluid.rho))) + + steady = walls[warmup:] or walls + median = statistics.median(steady) + result = { + 'dx': dx, 'particles': n, 'fluid': nf, 'wall': nw, + 'per_step_s_min': round(min(steady), 6), + 'per_step_s_median': round(median, 6), + 'per_step_s_max': round(max(steady), 6), + 'throughput_particle_steps_per_s': round(n / median, 1), + 'all_finite': finite, + } + del nnps, fluid, wall + gc.collect() + return result + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--dx-list', + default='0.04,0.03,0.022,0.016,0.013,0.011,0.009,0.0075,0.006,0.005', + help='comma-separated dx values (smaller dx = more particles)') + parser.add_argument('--steps', type=int, default=12) + parser.add_argument('--warmup', type=int, default=4) + parser.add_argument('--label', default=None, + help='override the GPU label in the output') + parser.add_argument('--output', default=None, help='also write JSON here') + args = parser.parse_args() + + wp.init() + gpu = _gpu_info() + if args.label: + gpu['name'] = args.label + + # Warm the kernel cache once (absorbs the cold compile of the fused kernels). + t = time.perf_counter() + try: + bench_one(0.05, max(4, args.warmup + 2), args.warmup) + except Exception as exc: # pragma: no cover + print('warmup failed:', exc, flush=True) + cold_compile_s = round(time.perf_counter() - t, 2) + print('cold_compile_s=%.2f' % cold_compile_s, flush=True) + + dx_list = [float(s) for s in args.dx_list.split(',') if s.strip()] + results = [] + for dx in dx_list: + try: + r = bench_one(dx, args.steps, args.warmup) + results.append(r) + print('dx=%-6.4f particles=%-9d per_step=%.6f s ' + 'throughput=%.3e p-steps/s finite=%s' + % (r['dx'], r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s'], r['all_finite']), + flush=True) + except Exception as exc: + results.append({'dx': dx, 'error': type(exc).__name__ + ': ' + + str(exc)[:120]}) + print('dx=%-6.4f FAILED: %s' % (dx, type(exc).__name__), flush=True) + gc.collect() + + report = { + 'benchmark': 'warp 3D dam-break fused WCSPH step (fp32)', + 'hardware': gpu, + 'config': { + 'dt': DT, 'steps': args.steps, 'warmup_discarded': args.warmup, + 'physics': PHYS, 'radius_scale': RADIUS_SCALE, 'hdx': HDX, + 'geometry': 'Lobovsky no-obstacle', 'cold_compile_s': cold_compile_s, + }, + 'results': results, + } + + print('\n=== sweep table ===') + print('| dx | particles | per-step (s) | throughput (p-steps/s) | finite |') + print('|---:|---:|---:|---:|:--:|') + for r in results: + if 'error' in r: + print('| %.4f | -- | ERROR | %s | -- |' % (r['dx'], r['error'])) + else: + print('| %.4f | %d | %.6f | %.3e | %s |' % ( + r['dx'], r['particles'], r['per_step_s_median'], + r['throughput_particle_steps_per_s'], r['all_finite'])) + + print('\n=== JSON (paste this) ===') + print(json.dumps(report, indent=2)) + if args.output: + with open(args.output, 'w') as f: + json.dump(report, f, indent=2) + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/perf_and_snapshot_dam_break_3d.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/perf_and_snapshot_dam_break_3d.py new file mode 100644 index 000000000..82d4f2bfe --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/perf_and_snapshot_dam_break_3d.py @@ -0,0 +1,247 @@ +#!/usr/bin/env python3 +"""Performance + representative-snapshot comparison: Warp (fp32) vs PySPH (CPU). + +Runs the *default* PySPH `dam_break_3d_lobovsky.py` Application (WCSPHScheme + +EPECIntegrator + WendlandQuintic, single-threaded Cython, fp64) and the additive +Warp `wc_sph_dam_break_step` (fp32, RTX GPU) for the SAME Lobovsky no-obstacle +case to the SAME physical time `tf`, then: + +1. reports particle counts and wall-clock performance (total + per-step) and the + Warp-vs-PySPH speedup, and +2. renders a side-by-side x-z snapshot of the final state (fluid coloured by + speed, walls grey) for the two runs. + +Timing notes (honest, reproducible -- not a tuned benchmark): +- CPU = the PySPH Application *solve loop* only (`app.setup()` then time + `app.solve()`); compile/setup and a warm Cython cache are excluded. +- Warp = the GPU stepping loop only (NNPS build excluded; the first step carries + a one-time module load, amortised in per-step over hundreds of steps). +- Both use their native adaptive dt + `n_damp`, so step counts differ slightly; + per-step is the fixed-overhead-free metric. GPU advantage grows with particle + count (this case is small; see the cross-GPU sweep for the 1M regime). +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import tempfile +import time +from pathlib import Path + +import matplotlib +matplotlib.use('Agg') +from matplotlib import pyplot as plt +import numpy as np + +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_dam_break_step +from pysph.solver.utils import get_files, load + +from dam_break_3d_runner import REF_C0, WarpDamBreak3DRunner, damp_factor + +EXAMPLE = 'pysph.examples.dam_break.dam_break_3d_lobovsky' + + +def run_cpu(dx, tf, pfreq, outdir): + code = ( + "import time, json\n" + "from {mod} import DamBreak3D\n" + "app = DamBreak3D()\n" + "app.setup(argv={argv!r})\n" + "t0 = time.perf_counter()\n" + "app.solve()\n" + "dt = time.perf_counter() - t0\n" + "print('PERFJSON ' + json.dumps({{'solve_s': dt, " + "'count': int(app.solver.count), 't': float(app.solver.t)}}))\n" + ).format( + mod=EXAMPLE, + argv=['--dx', str(dx), '-d', str(outdir), '--tf', str(tf), + '--pfreq', str(pfreq), '--detailed-output'], + ) + t0 = time.perf_counter() + proc = subprocess.run([sys.executable, '-c', code], + capture_output=True, text=True) + subprocess_wall = time.perf_counter() - t0 + perf = None + for line in proc.stdout.splitlines(): + if line.startswith('PERFJSON '): + perf = json.loads(line[len('PERFJSON '):]) + if perf is None: + sys.stderr.write(proc.stdout[-2000:] + '\n' + proc.stderr[-2000:]) + raise SystemExit('CPU run did not report PERFJSON') + perf['subprocess_wall_s'] = subprocess_wall + return perf + + +def run_warp(dx, tf, args): + import warp as wp + runner = WarpDamBreak3DRunner( + dx=dx, hdx=args.hdx, rho0=args.rho0, c0=args.c0, gamma=args.gamma, + alpha=args.alpha, beta=args.beta, kernel='wendland', + radius_scale=args.radius_scale, xsph_eps=args.xsph_eps, gz=args.gz, + n_damp=args.n_damp, adaptive_dt=True, cfl=args.cfl, + ) + fluid, wall = runner.create_particles() + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall], radius_scale=runner.radius_scale + ) + t, count, push = 0.0, 0, True + wp.synchronize_device(nnps.device) + t0 = time.perf_counter() + while t < tf - 1.0e-12: + scale = damp_factor(count, runner.n_damp) + dt = wc_sph_dam_break_step( + nnps, fluid_index=0, solid_indices=(1,), dt=runner.dt, + rho0=runner.rho0, c0=runner.c0, gamma=runner.gamma, + alpha=runner.alpha, beta=runner.beta, kernel='wendland', + xsph_eps=runner.xsph_eps, gz=runner.gz, gravity_ramp=1.0, + adaptive_dt=True, cfl=runner.cfl, dt_min=runner.dt_min, + dt_max=runner.dt_max, adaptive_dt_scale=scale, + step_dt_max=min(runner.dt_max, tf - t), push=push, return_dt=True, + ) + push = False + t += dt + count += 1 + wp.synchronize_device(nnps.device) + elapsed = time.perf_counter() - t0 + fluid.gpu.pull('x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p') + wall.gpu.pull('x', 'y', 'z') + return { + 'wall_s': elapsed, 'count': count, 't': t, + 'fluid': {k: np.asarray(getattr(fluid, k)) for k in + ('x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p')}, + 'wall': {k: np.asarray(getattr(wall, k)) for k in ('x', 'y', 'z')}, + 'n_fluid': fluid.get_number_of_particles(), + 'n_wall': wall.get_number_of_particles(), + } + + +def _speed(d): + return np.sqrt(d['u']**2 + d['v']**2 + d['w']**2) + + +def _panel(ax, fluid, wall, title, vmax, xlim, zlim): + ax.scatter(wall['x'], wall['z'], s=4, c='0.78', marker='s', + linewidths=0, label='wall') + sc = ax.scatter(fluid['x'], fluid['z'], c=_speed(fluid), s=6, vmin=0.0, + vmax=vmax, cmap='viridis', linewidths=0) + ax.set_title(title) + ax.set_xlabel('x (m)') + ax.set_ylabel('z (m)') + ax.set_aspect('equal', adjustable='box') + ax.set_xlim(*xlim) + ax.set_ylim(*zlim) + return sc + + +def plot_snapshot(cpu, warp, path, meta): + cpu_speed = _speed(cpu['fluid']) + warp_speed = _speed(warp['fluid']) + vmax = max(float(cpu_speed.max()), float(warp_speed.max()), 1e-6) + allx = np.concatenate([cpu['fluid']['x'], warp['fluid']['x'], + cpu['wall']['x'], warp['wall']['x']]) + allz = np.concatenate([cpu['fluid']['z'], warp['fluid']['z'], + cpu['wall']['z'], warp['wall']['z']]) + pad = 0.05 + xlim = (allx.min() - pad, allx.max() + pad) + zlim = (allz.min() - pad, allz.max() + pad) + fig, axes = plt.subplots(1, 2, figsize=(13, 4.4), constrained_layout=True) + _panel(axes[0], cpu['fluid'], cpu['wall'], + 'PySPH CPU (fp64) t=%.3f s %d fluid' % ( + cpu['t'], cpu['fluid']['x'].size), vmax, xlim, zlim) + sc = _panel(axes[1], warp['fluid'], warp['wall'], + 'Warp GPU (fp32) t=%.3f s %d fluid' % ( + warp['t'], warp['n_fluid']), vmax, xlim, zlim) + fig.colorbar(sc, ax=axes, label='speed |v| (m/s)', shrink=0.85) + fig.suptitle(meta, fontsize=11) + fig.savefig(path, dpi=160) + plt.close(fig) + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=0.08) + p.add_argument('--tf', type=float, default=0.4) + p.add_argument('--pfreq', type=int, default=1000000, + help='Large -> CPU dumps only initial + final (less IO).') + p.add_argument('--hdx', type=float, default=1.3) + p.add_argument('--rho0', type=float, default=1000.0) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--gamma', type=float, default=7.0) + p.add_argument('--alpha', type=float, default=0.25) + p.add_argument('--beta', type=float, default=0.0) + p.add_argument('--xsph-eps', type=float, default=0.5) + p.add_argument('--gz', type=float, default=-9.81) + p.add_argument('--cfl', type=float, default=0.3) + p.add_argument('--n-damp', type=int, default=50) + p.add_argument('--radius-scale', type=float, default=2.0) + p.add_argument('--prefix', default='cpu-vs-warp') + p.add_argument('--keep-output', action='store_true') + args = p.parse_args() + args.dt_min = 0.0 + args.dt_max = None + + out_dir = Path(__file__).parent + tmp = tempfile.mkdtemp(prefix='dam_break_perf_', dir=str(out_dir)) + + cpu_perf = run_cpu(args.dx, args.tf, args.pfreq, tmp) + snaps = get_files(str(tmp)) + if not snaps: + raise SystemExit('No CPU snapshots produced') + last = load(snaps[-1]) + arrays = last['arrays'] + fl, wl = arrays['fluid'], arrays.get('boundary', arrays.get('wall')) + cpu = { + 't': float(last['solver_data']['t']), + 'fluid': {k: np.asarray(getattr(fl, k)) for k in + ('x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p')}, + 'wall': {k: np.asarray(getattr(wl, k)) for k in ('x', 'y', 'z')}, + } + + warp = run_warp(args.dx, args.tf, args) + + img = out_dir / f'{args.prefix}-snapshot.png' + meta = ('3D dam break (Lobovsky no-obstacle) dx=%.3f ' + 'fluid=%d / wall=%d c0=%.1f Wendland' % ( + args.dx, warp['n_fluid'], warp['n_wall'], args.c0)) + plot_snapshot(cpu, warp, img, meta) + + cpu_steps = int(cpu_perf['count']) + warp_steps = int(warp['count']) + cpu_per_step = cpu_perf['solve_s'] / max(cpu_steps, 1) + warp_per_step = warp['wall_s'] / max(warp_steps, 1) + report = { + 'case': 'Lobovsky 3D dam-break, no obstacle', + 'reference_example': EXAMPLE, + 'dx': args.dx, 'tf': args.tf, 'c0': args.c0, 'kernel': 'wendland', + 'n_fluid': warp['n_fluid'], 'n_wall': warp['n_wall'], + 'n_total': warp['n_fluid'] + warp['n_wall'], + 'cpu_pysph_fp64': { + 'solve_s': cpu_perf['solve_s'], 'steps': cpu_steps, + 's_per_step': cpu_per_step, 'final_t': cpu['t'], + 'subprocess_wall_s': cpu_perf['subprocess_wall_s'], + }, + 'warp_gpu_fp32': { + 'wall_s': warp['wall_s'], 'steps': warp_steps, + 's_per_step': warp_per_step, 'final_t': warp['t'], + }, + 'speedup_wall': cpu_perf['solve_s'] / warp['wall_s'], + 'speedup_per_step': cpu_per_step / warp_per_step, + 'snapshot_image': str(img), + } + print(json.dumps(report, indent=2, sort_keys=True)) + (out_dir / f'{args.prefix}-perf.json').write_text( + json.dumps(report, indent=2, sort_keys=True) + ) + + if not args.keep_output: + import shutil + shutil.rmtree(tmp, ignore_errors=True) + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/resolved_dam_break_3d_comparison.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/resolved_dam_break_3d_comparison.py new file mode 100644 index 000000000..b1d3a2c59 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/resolved_dam_break_3d_comparison.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python3 +"""Tier-2 resolved parity: real PySPH dam-break Application vs the Warp step. + +Runs the shipped CPU reference ``dam_break_3d_lobovsky.py`` (WCSPHScheme + +EPECIntegrator + WendlandQuintic) as a subprocess at a coarse resolution for a +short horizon, then: + +1. loads the earliest dump (t ~ 0) and uses its *exact* fluid + boundary arrays + as a shared initial condition (identical particle layout/ordering), and +2. advances the additive Warp ``wc_sph_dam_break_step`` from that IC, snapshotting + at each CPU checkpoint time, + +and reports, per checkpoint, signed CPU-vs-Warp deltas on aggregate observables +(kinetic energy, surge-front x, max fluid height, density/pressure ranges, wall +pressure) plus a short-horizon per-particle delta at the first checkpoint. + +Per the ADR, the headline validators are the *aggregate* observables: fp32 (Warp) +vs fp64 (CPU) on a chaotic free-surface flow defeats long-horizon per-particle +parity, so the per-particle delta is reported for the earliest checkpoint only. +The Warp step is EPEC (matching the reference EPECIntegrator); its sound speed +``c0`` matches the reference scheme constant ``10*sqrt(2*9.81*0.55)``. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import tempfile +from pathlib import Path + +import numpy as np + +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_sph import wc_sph_dam_break_step +from pysph.solver.utils import get_files, load + +from dam_break_3d_runner import GRAVITY, H, REF_C0, damp_factor + +EXAMPLE = 'pysph.examples.dam_break.dam_break_3d_lobovsky' + + +def _run_cpu_reference(dx, tf, pfreq, outdir): + """Subprocess the real Application; run() only (skips mayavi post_process).""" + code = ( + "from {mod} import DamBreak3D; " + "DamBreak3D().run(argv={argv!r})" + ).format( + mod=EXAMPLE, + argv=['--dx', str(dx), '-d', str(outdir), '--tf', str(tf), + '--pfreq', str(pfreq), '--detailed-output'], + ) + subprocess.run([sys.executable, '-c', code], check=True) + + +def _load_sorted(outdir): + files = get_files(str(outdir)) + snaps = [] + for f in files: + data = load(f) + t = float(data['solver_data']['t']) + snaps.append((t, data['arrays'])) + snaps.sort(key=lambda s: s[0]) + return snaps + + +def _fluid_wall(arrays): + """Return (fluid, boundary) ParticleArrays regardless of key naming.""" + fluid = arrays['fluid'] + wall = arrays['boundary'] if 'boundary' in arrays else arrays['wall'] + return fluid, wall + + +def _warp_from(pa, name, c0, rho0): + n = pa.get_number_of_particles() + z = np.zeros(n) + rho = np.asarray(pa.rho, dtype=np.float64).copy() if 'rho' in pa.properties \ + else np.ones(n) * rho0 + return get_particle_array( + name=name, + x=np.asarray(pa.x, dtype=np.float64).copy(), + y=np.asarray(pa.y, dtype=np.float64).copy(), + z=np.asarray(pa.z, dtype=np.float64).copy(), + h=np.asarray(pa.h, dtype=np.float64).copy(), + m=np.asarray(pa.m, dtype=np.float64).copy(), + rho=rho, p=z.copy(), cs=np.ones(n) * c0, + u=z.copy(), v=z.copy(), w=z.copy(), + au=z.copy(), av=z.copy(), aw=z.copy(), arho=z.copy(), + ax=z.copy(), ay=z.copy(), az=z.copy(), + x0=z.copy(), y0=z.copy(), z0=z.copy(), + u0=z.copy(), v0=z.copy(), w0=z.copy(), rho0=z.copy(), + backend='warp', + ) + + +def _observables(fx, fy, fz, fu, fv, fw, frho, fp, fm, wp_): + speed2 = fu*fu + fv*fv + fw*fw + return { + 'kinetic_energy': float(0.5 * np.sum(fm * speed2)), + 'surge_front_x': float(np.max(fx)), + 'max_height': float(np.max(fz)), + 'min_z': float(np.min(fz)), + 'rho_min': float(np.min(frho)), + 'rho_max': float(np.max(frho)), + 'p_min': float(np.min(fp)), + 'p_max': float(np.max(fp)), + 'wall_p_max': float(np.max(wp_)), + 'mean_speed': float(np.mean(np.sqrt(speed2))), + } + + +def _cpu_observables(fluid, wall): + def prop(pa, name, default=0.0): + if name in pa.properties: + return np.asarray(getattr(pa, name), dtype=np.float64) + return np.full(pa.get_number_of_particles(), default) + return _observables( + prop(fluid, 'x'), prop(fluid, 'y'), prop(fluid, 'z'), + prop(fluid, 'u'), prop(fluid, 'v'), prop(fluid, 'w'), + prop(fluid, 'rho', 1000.0), prop(fluid, 'p'), prop(fluid, 'm'), + prop(wall, 'p'), + ) + + +def _warp_observables(fluid, wall): + return _observables( + fluid.x, fluid.y, fluid.z, fluid.u, fluid.v, fluid.w, + fluid.rho, fluid.p, fluid.m, wall.p, + ) + + +def _step_to(nnps, fluid_idx, wall_idx, t_target, t_now, count, args, push): + """Advance Warp until time reaches t_target; return (t_now, count).""" + first = push + while t_now < t_target - 1e-12: + scale = damp_factor(count, args.n_damp) + remaining = t_target - t_now + dt_used = wc_sph_dam_break_step( + nnps, fluid_index=fluid_idx, solid_indices=(wall_idx,), + dt=args.dt_max, rho0=args.rho0, c0=args.c0, gamma=args.gamma, + alpha=args.alpha, beta=args.beta, kernel='wendland', + xsph_eps=args.xsph_eps, gz=args.gz, gravity_ramp=1.0, + adaptive_dt=True, cfl=args.cfl, dt_min=args.dt_min, + dt_max=args.dt_max, adaptive_dt_scale=scale, + step_dt_max=min(args.dt_max, remaining), push=first, + return_dt=True, + ) + first = False + t_now += dt_used + count += 1 + return t_now, count + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=0.1) + p.add_argument('--tf', type=float, default=0.06) + p.add_argument('--pfreq', type=int, default=20) + p.add_argument('--rho0', type=float, default=1000.0) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--gamma', type=float, default=7.0) + p.add_argument('--alpha', type=float, default=0.25) + p.add_argument('--beta', type=float, default=0.0) + p.add_argument('--xsph-eps', type=float, default=0.5) + p.add_argument('--gz', type=float, default=-GRAVITY) + p.add_argument('--cfl', type=float, default=0.3) + p.add_argument('--n-damp', type=int, default=50) + p.add_argument('--dt-min', type=float, default=0.0) + p.add_argument('--dt-max', type=float, default=None) + p.add_argument('--radius-scale', type=float, default=2.0) + p.add_argument('--keep-output', action='store_true') + p.add_argument('--output-dir', default=None) + p.add_argument('--prefix', default='comparison-resolved') + args = p.parse_args() + + if args.dt_max is None: + h0 = 1.3 * args.dx + co = 10.0 * np.sqrt(2.0 * GRAVITY * H) + args.dt_max = 0.25 * h0 / (1.1 * co) + + out_dir = Path(args.output_dir) if args.output_dir else Path(__file__).parent + out_dir.mkdir(parents=True, exist_ok=True) + + tmp = tempfile.mkdtemp(prefix='dam_break_cpu_', dir=str(out_dir)) + _run_cpu_reference(args.dx, args.tf, args.pfreq, tmp) + snaps = _load_sorted(tmp) + if len(snaps) < 2: + raise SystemExit("Reference produced <2 snapshots; raise --tf/--pfreq") + + t0, ic = snaps[0] + fluid_ic, wall_ic = _fluid_wall(ic) + fluid = _warp_from(fluid_ic, 'fluid', args.c0, args.rho0) + wall = _warp_from(wall_ic, 'wall', args.c0, args.rho0) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall], radius_scale=args.radius_scale + ) + + checkpoints = [] + t_now, count, push = t0, 0, True + for k, (t_cp, arrays) in enumerate(snaps[1:]): + t_now, count = _step_to( + nnps, 0, 1, t_cp, t_now, count, args, push + ) + push = False + fluid.gpu.pull('x', 'y', 'z', 'rho', 'p', 'u', 'v', 'w') + wall.gpu.pull('x', 'y', 'z', 'rho', 'p') + cpu_fluid, cpu_wall = _fluid_wall(arrays) + cpu_obs = _cpu_observables(cpu_fluid, cpu_wall) + warp_obs = _warp_observables(fluid, wall) + deltas = {key: warp_obs[key] - cpu_obs[key] for key in cpu_obs} + finite = bool( + np.all(np.isfinite(fluid.x)) and np.all(np.isfinite(fluid.rho)) + and np.all(np.isfinite(fluid.p)) + ) + entry = { + 'checkpoint': k, + 'cpu_t': t_cp, + 'warp_t': t_now, + 'cpu': cpu_obs, + 'warp': warp_obs, + 'delta_warp_minus_cpu': deltas, + 'warp_all_finite': finite, + } + # Short-horizon per-particle delta at the first checkpoint only. + if k == 0 and fluid.get_number_of_particles() == \ + cpu_fluid.get_number_of_particles(): + entry['per_particle_max_abs'] = { + 'x': float(np.max(np.abs(fluid.x - np.asarray(cpu_fluid.x)))), + 'z': float(np.max(np.abs(fluid.z - np.asarray(cpu_fluid.z)))), + 'rho': float(np.max(np.abs( + fluid.rho - np.asarray(cpu_fluid.rho)))), + } + checkpoints.append(entry) + + report = { + 'params': vars(args), + 'reference_example': EXAMPLE, + 'n_snapshots': len(snaps), + 'ic_t': t0, + 'fluid_particles': int(fluid.get_number_of_particles()), + 'wall_particles': int(wall.get_number_of_particles()), + 'checkpoints': checkpoints, + 'all_finite': all(c['warp_all_finite'] for c in checkpoints), + } + print(json.dumps(report, indent=2, sort_keys=True)) + summary_path = out_dir / f'{args.prefix}-summary.json' + summary_path.write_text(json.dumps(report, indent=2, sort_keys=True)) + + if not args.keep_output: + import shutil + shutil.rmtree(tmp, ignore_errors=True) + + if not report['all_finite']: + raise SystemExit("Warp produced non-finite values at a checkpoint") + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/run_correctness.sh b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/run_correctness.sh new file mode 100644 index 000000000..0447d7ed4 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/run_correctness.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# Smoke wrapper for the 3D dam-break Warp runner (ADR-0005). +# Runs a coarse, short collapse and asserts a non-empty output + all_finite. +set -euo pipefail + +ROOT="${ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)}" +# Prefer an already-active venv; else the known PySPH/warp venv (see memory +# pysph-warp-dev-env: bare python/python3 on PATH do NOT have warp). +if [ -n "${VIRTUAL_ENV:-}" ]; then + PY="${PY:-python}" +else + PY="${PY:-/home/kunalp/.pqt_venv_e0b41259/bin/python}" +fi + +cd "$ROOT" + +DIR=".ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner" +OUT="$DIR/results-smoke.npz" + +"$PY" "$DIR/dam_break_3d_runner.py" \ + --dx 0.1 \ + --steps 20 \ + --hdx 1.3 \ + --rho0 1000.0 \ + --gamma 7.0 \ + --alpha 0.25 \ + --beta 0.0 \ + --kernel wendland \ + --radius-scale 2.0 \ + --xsph-eps 0.5 \ + --gz -9.81 \ + --n-damp 50 \ + --cfl 0.3 \ + --output "$OUT" + +test -s "$OUT" +echo "smoke OK: $OUT" diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/showcase-dx025-t080-perf.json b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/showcase-dx025-t080-perf.json new file mode 100644 index 000000000..f0c338d13 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/showcase-dx025-t080-perf.json @@ -0,0 +1,24 @@ +{ + "c0": 32.849657532461435, + "case": "Lobovsky 3D dam-break, no obstacle (~1M particles)", + "cpu_pysph_fp64": { + "particle_steps_per_s": 294120.6924978172, + "s_per_step": 0.42733137520044695, + "solve_s": 2.1366568760022346, + "steps": 5 + }, + "device": "RTX 4060 Laptop (fp32) vs single-thread PySPH Cython (fp64)", + "dx": 0.025, + "kernel": "wendland", + "n_fluid": 59280, + "n_total": 125687, + "n_wall": 66407, + "snapshot_image": "/home/kunalp/work/particles/pysph/.ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/showcase-dx025-t080-3d-snapshot.png", + "snapshot_t": 0.8000625194274892, + "speedup_per_step": 13.609282932344543, + "warp_gpu_fp32": { + "particle_steps_per_s": 4002771.7204599013, + "s_per_step": 0.03139999199993326, + "steps": 10 + } +} \ No newline at end of file diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/coupled-column-dx080-t020-hero.png b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/coupled-column-dx080-t020-hero.png new file mode 100644 index 000000000..6824f105b Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/coupled-column-dx080-t020-hero.png differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/experiment.md new file mode 100644 index 000000000..7d3d6069c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/experiment.md @@ -0,0 +1,157 @@ +--- +type: experiment +id: 2026-06-19_warp-floating-body-rigid +created: 2026-06-19T22:45:00 CEST +author: @kunalpuri-prediqt +agent: claude +aspect: validation-benchmarks +adr: ADR-0006 +status: active +last_checked: 2026-07-06T11:18:54 CEST +--- + +# Experiment: Warp floating / rigid body coupled to SPH (3D dam-break) + +## Headline + +The next Warp benchmark after the 3D Lobovsky dam-break (ADR-0005): a +**floating rigid body** -- the dramatic case is a dam-break surge tossing a +floating box (mirrors `pysph/examples/rigid_body/dam_break3D_sph.py`). The +**deliverable is a photorealistic animation** of the GPU (Warp) simulation, +rendered out-of-band (splashsurf -> Blender Cycles; Omniverse was rejected for +this 8 GB / WSL2 setup -- it can't surface a Warp point cloud natively and its +headless cloud batch is broken). + +A fixed wall and a floating body differ by one thing: the body is *integrated* +(6-DOF), the wall is not. So this extends the existing multi-array dam-break +step by lifting "fixed": reduce force+torque over the body, integrate the COM, +and rigid-transform the body particles. All additive (ADR-0006), keeping the 2D +elliptical-drop path byte-identical. + +## Validation strategy + +The shipped CPU rigid-body Application cannot run on this Python 3.14 venv +(`compyle 0.9.1` uses the removed `ast.Str` for `rigid_body.py`'s matrix +`declare()`s). So validation is against a **faithful numpy reimplementation** of +`RigidBodyMoments`/`Motion` (tier-1 style, as ADR-0005 used for the hand-rolled +CPU baseline). Tier-2 (real CPU Application) is deferred behind a compyle fix. + +## Phases (ADR-0006) + +- **P0 -- kill-test (DONE).** `p0_rigid_reduce_kill_test.py`: on an asymmetric + synthetic body (real torque / off-diagonal inertia), prove the one new GPU + primitive -- an `atomic_add` SUM-reduction producing the 16-slot + `RigidBodyMoments` `mi` vector -- before touching the backend. Result on the + RTX 4060: + - reduction reproduces `RigidBodyMoments`: **2.7e-15** (f64 accum, f32 inputs); + **8e-14** (f64 data); + - **fp32 `atomic_add` is non-deterministic: 3.0e-6** run-to-run spread + (order-dependent / non-associative, unlike the dt-reduce's `atomic_max`); + **f64 accumulators drop it to ~1e-15** -> locked decision: accumulate in f64; + - host finalize (COM / inertia tensor / torque-about-COM / `omega_dot`) matches + numpy to **6e-14**; device rigid-transform exact (**5.5e-17**). + +- **P1 -- reduction in the backend (DONE).** Added to `pysph/base/warp_sph.py`, + all additive (no generated source / router edits): + - `_rigid_moments_reduce_f32` / `_rigid_moments_reduce_f64` (f64 accumulators + on both paths), modeled on the `_wcsph_dt_reduce_*` `atomic_max` template; + - `_rigid_finalize_moments` (host numpy 6-DOF moments, reusing + `rigid_body.py:128-207`); + - `compute_rigid_body_moments(pa, nbody, omega, ...)` driver. + Tests (`pysph/base/tests/test_warp_sph.py`): + `test_rigid_body_moments_matches_reference_3d[False/True]` (two-body, + asymmetric; f32 and f64 paths vs the numpy reference) and + `test_rigid_moments_f32_kernel_is_accurate_and_deterministic`. The + cache-stability guard `test_2d_path_generated_source_is_byte_identical_to_golden` + still passes. + +- **P2 (DONE, amended to fully device-resident).** The implementation owner + rejected a host-side production solve because the goal is a genuinely GPU- + accelerated backend. Added persistent `WarpRigidBodyState` plus additive Warp + kernels for moment finalize, the symmetric 3x3 angular-acceleration solve, + RK2 saved/midpoint/full compact state, rigid velocity + `v = vc + omega x (x-cm)`, and stage position updates. The production stage + performs no host finalize, pull, `.numpy()`, or explicit synchronization; + `_rigid_finalize_moments` is retained only as the validation/host-query oracle. + Device finalize and both RK2 stages match faithful NumPy/PySPH formulas for + asymmetric two-body fp32/fp64 cases; pure translation preserves body geometry + and bodies remain isolated. Focused P1/P2 + 2D cache guard: `10 passed`; + final full Warp SPH regression: `49 passed`. +- **P3 (DONE; review approved).** Added deterministic two-pass Liu coupling: + body->fluid acceleration plus reversed fluid->body reaction, avoiding the + nondeterministic fp32 source atomics rejected in P0. Added a static + `RigidNumberDensity` pre-pass, rigid density/body-force staging, and sibling + `wc_sph_dam_break_rigid_step`; the body is excluded from fluid PEC and its + contribution to fluid `arho` is evaluated exactly once. Primitive parity + checks (Liu/reference + equal reaction, Wendland number density, density/body + force) pass; the complete coupled EPEC smoke test passes. +- **P4.** EPEC fidelity + existing-driver behaviour guard; assemble the 3D + surge-tosses-a-box case; render. + +## P2 runtime case (2026-06-20) + +Ran an asymmetric 3D box (9x7x5 = 315 particles) under prescribed nonzero net +force and torque for 2,000 RK2 steps at `dt=1e-4` (`t=0.2`) on the RTX 4060. +The stepping loop called only `save_rigid_body_state` and the two device RK2 +stages; host arrays were read once after the final synchronization. + +```text +wall_s: 0.5445955659997708 +steps_per_s: 3672.4500250537144 +particle_steps_per_s: 1156821.7578919202 +device_error: 0 +all_finite: true +vc_max_abs_error: 1.552180384223334e-09 +initial_omega: [0.2, -0.1, 0.3] +final_omega: [0.4878298261, -0.1616930311, 0.4444991226] +max_pair_distance_drift: 4.1726284255583224e-07 +relative_pair_distance_drift: 9.386771416218177e-07 +``` + +This validates repeated GPU-resident rigid stepping and expected force/torque +response. It is not a fluid-coupled floating-body claim; see P3 below. + +## P3 coupled runtime cases (2026-06-20) + +Coarse end-to-end smoke (`dx=0.10`, 1,000 fluid + 3,824 wall + 44 body, 20 +adaptive steps, `t=0.00310`): finite, device error 0, body moved under computed +force/torque, relative rigid-geometry drift `2.16e-7`. + +First substantial collision-free transient (`dx=0.08`, body initially inside +the collapsing column, 1,800 fluid + 5,592 wall + 66 body = 7,458 total): + +```text +steps / time: 241 / 0.200603 s +all_finite / error: true / 0 +body COM displacement: [0.02396, -0.01009, -0.08081] m +body vc: [0.24182, -0.06702, -0.65823] m/s +body omega: [-0.14531, 0.70659, 0.20398] rad/s +final body force: [29.23, -5.27, 14.18] N +fluid rho range: 991.68 .. 1015.78 kg/m^3 +body rho range: 912.75 .. 1015.08 kg/m^3 +relative geometry drift: 1.90e-6 +``` + +The positive final vertical force despite body weight demonstrates developed +fluid reaction (not prescribed P2 force). The horizon avoids wall contact, +which remains P4 scope. Hero image: +`reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3_assets/coupled-column-dx080-t020-hero.png`. + +## How to run + +```bash +PY=/home/kunalp/.pqt_venv_e0b41259/bin/python +# P0 kill-test (standalone, no backend dependency beyond warp) +$PY .ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/p0_rigid_reduce_kill_test.py +# P1 backend tests + cache guard +$PY -m pytest -q pysph/base/tests/test_warp_sph.py -k rigid \ + pysph/base/tests/test_warp_codegen.py::test_2d_path_generated_source_is_byte_identical_to_golden +# P3 coupled transient +$PY .ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/warp_floating_box_runner.py \ + --dx 0.08 --tf 0.2 --steps 2000 --box-x 1.50 --box-z 0.72 +``` + +## Out of scope (follow-ups) + +- Rigid-wall collision/contact and the assembled photorealistic animation (P4); + tier-2 CPU parity (needs the compyle py3.14 fix). diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/p0_rigid_reduce_kill_test.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/p0_rigid_reduce_kill_test.py new file mode 100644 index 000000000..522aa1f02 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/p0_rigid_reduce_kill_test.py @@ -0,0 +1,330 @@ +#!/usr/bin/env python3 +"""P0 kill-test for the Warp floating-body benchmark (ADR-0006 de-risk). + +The one genuinely new GPU primitive for a moving rigid body is a SUM-reduction +over the body's particles producing the 16-slot ``mi`` vector that PySPH's +``RigidBodyMoments.reduce`` builds (rigid_body.py:90-122): total mass, m*x/y/z +(for COM), 6 second-moments about the origin, total force, and torque about the +origin. The host then finalizes COM / parallel-axis inertia tensor / torque +about COM / omega_dot (rigid_body.py:128-207) -- that stays in numpy. + +This harness proves, on a synthetic body, BEFORE touching warp_sph.py: + + 1. A Warp ``atomic_add`` reduction reproduces the exact 16 ``mi`` sums and the + downstream COM/inertia/force/torque/omega_dot of ``RigidBodyMoments``. + 2. The non-determinism of fp32 ``atomic_add`` (order-dependent, non-associative + -- the risk the adversarial critique raised, unlike the order-independent + ``atomic_max`` dt-reduce) by launching 20x and measuring the spread. + 3. The mitigation: accumulating in f64 even from f32 particle data. + 4. The device rigid-transform (``RigidBodyMotion``: v = vc + omega x r). + +Sets the parity tolerance for P1. No NNPS, no PySPH Application (the CPU rigid +reference is blocked by compyle/ast.Str on py3.14); the reference here is a +faithful numpy reimplementation of RigidBodyMoments. +""" +import numpy as np +import warp as wp + +wp.init() +DEV = "cuda:0" +RNG = np.random.default_rng(7) + + +# -------------------------------------------------------------------------- +# Synthetic rigid body: a sphere of particles, COM deliberately off-origin so +# the torque-about-origin -> torque-about-COM shift and parallel-axis theorem +# are actually exercised. Forces mimic gravity + a depth-dependent buoyancy-ish +# pressure push so SF and torque are both non-trivial. +# -------------------------------------------------------------------------- +def make_body(R=0.025, center=(0.05, 0.18, 0.03), solid_rho=500.0): + # ASYMMETRIC body: an ellipsoid (distinct semi-axes) so the inertia tensor + # has real off-diagonal terms, with a graded density and a one-sided force + # field so the NET TORQUE is non-trivial (omega_dot is then a real number, + # not f32 noise about zero). + ax, ay, az = R, 1.8 * R, 0.6 * R + g = np.linspace(-2.0 * R, 2.0 * R, 26) + X, Y, Z = np.meshgrid(g, g, g, indexing="ij") + p = ((X / ax) ** 2 + (Y / ay) ** 2 + (Z / az) ** 2) <= 1.0 + x, y, z = X[p], Y[p], Z[p] + dx = g[1] - g[0] + # graded density (heavier on +x, +z) -> off-axis COM & products of inertia + rho = solid_rho * (1.0 + 0.6 * (x / ax) + 0.4 * (z / az)) + m = rho * dx**3 + x = x + center[0]; y = y + center[1]; z = z + center[2] + n = x.size + g_acc = 9.81 + # one-sided lateral force (acts mostly on +x half) -> real torque about COM + fx = m * g_acc * (0.8 * (x - center[0]) / ax + 0.3) + fy = -m * g_acc + m * g_acc * 1.6 * np.clip(0.20 - y, 0.0, None) / 0.20 + fz = m * g_acc * (0.5 * (x - center[0]) / ax) + return (np.ascontiguousarray(x), np.ascontiguousarray(y), + np.ascontiguousarray(z), np.ascontiguousarray(m), + np.ascontiguousarray(fx), np.ascontiguousarray(fy), + np.ascontiguousarray(fz)) + + +# -------------------------------------------------------------------------- +# Reference: faithful numpy reimplementation of RigidBodyMoments. +# -------------------------------------------------------------------------- +def reference_mi(x, y, z, m, fx, fy, fz): + mi = np.zeros(16, dtype=np.float64) + mi[0] = np.sum(m) + mi[1] = np.sum(m * x); mi[2] = np.sum(m * y); mi[3] = np.sum(m * z) + mi[4] = np.sum(m * (y * y + z * z)) + mi[5] = np.sum(m * (x * x + z * z)) + mi[6] = np.sum(m * (x * x + y * y)) + mi[7] = -np.sum(m * x * y) + mi[8] = -np.sum(m * x * z) + mi[9] = -np.sum(m * y * z) + mi[10] = np.sum(fx); mi[11] = np.sum(fy); mi[12] = np.sum(fz) + mi[13] = np.sum(y * fz - z * fy) + mi[14] = np.sum(z * fx - x * fz) + mi[15] = np.sum(x * fy - y * fx) + return mi + + +def finalize(mi, omega): + """RigidBodyMoments finalize (rigid_body.py:128-207). Host-side, numpy.""" + m = mi[0] + cx, cy, cz = mi[1] / m, mi[2] / m, mi[3] / m + cm = np.array([cx, cy, cz]) + ixx = mi[4] - (cy * cy + cz * cz) * m + iyy = mi[5] - (cx * cx + cz * cz) * m + izz = mi[6] - (cx * cx + cy * cy) * m + ixy = mi[7] + cx * cy * m + ixz = mi[8] + cx * cz * m + iyz = mi[9] + cy * cz * m + I = np.array([[ixx, ixy, ixz], [ixy, iyy, iyz], [ixz, iyz, izz]]) + fx, fy, fz = mi[10], mi[11], mi[12] + force = np.array([fx, fy, fz]) + ac = force / m + tx = mi[13] - (cy * fz - cz * fy) + ty = mi[14] - (-cx * fz + cz * fx) + tz = mi[15] - (cx * fy - cy * fx) + tau = np.array([tx, ty, tz]) + w = np.asarray(omega, dtype=np.float64) + omega_dot = np.linalg.solve(I, tau - np.cross(w, I @ w)) + return dict(total_mass=m, cm=cm, I=I, force=force, ac=ac, torque=tau, + omega_dot=omega_dot) + + +# -------------------------------------------------------------------------- +# Warp atomic_add reduction kernels (single body -> 16-slot accumulator). +# -------------------------------------------------------------------------- +@wp.kernel +def reduce_f32(x: wp.array(dtype=wp.float32), y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), m: wp.array(dtype=wp.float32), + fx: wp.array(dtype=wp.float32), fy: wp.array(dtype=wp.float32), + fz: wp.array(dtype=wp.float32), mi: wp.array(dtype=wp.float32)): + i = wp.tid() + xi = x[i]; yi = y[i]; zi = z[i]; mm = m[i] + fxi = fx[i]; fyi = fy[i]; fzi = fz[i] + wp.atomic_add(mi, 0, mm) + wp.atomic_add(mi, 1, mm * xi); wp.atomic_add(mi, 2, mm * yi) + wp.atomic_add(mi, 3, mm * zi) + wp.atomic_add(mi, 4, mm * (yi * yi + zi * zi)) + wp.atomic_add(mi, 5, mm * (xi * xi + zi * zi)) + wp.atomic_add(mi, 6, mm * (xi * xi + yi * yi)) + wp.atomic_add(mi, 7, -mm * xi * yi); wp.atomic_add(mi, 8, -mm * xi * zi) + wp.atomic_add(mi, 9, -mm * yi * zi) + wp.atomic_add(mi, 10, fxi); wp.atomic_add(mi, 11, fyi) + wp.atomic_add(mi, 12, fzi) + wp.atomic_add(mi, 13, yi * fzi - zi * fyi) + wp.atomic_add(mi, 14, zi * fxi - xi * fzi) + wp.atomic_add(mi, 15, xi * fyi - yi * fxi) + + +@wp.kernel +def reduce_f64acc(x: wp.array(dtype=wp.float32), y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), m: wp.array(dtype=wp.float32), + fx: wp.array(dtype=wp.float32), fy: wp.array(dtype=wp.float32), + fz: wp.array(dtype=wp.float32), + mi: wp.array(dtype=wp.float64)): + # f32 particle data, but accumulate in f64 (the recommended mitigation). + i = wp.tid() + xi = wp.float64(x[i]); yi = wp.float64(y[i]); zi = wp.float64(z[i]) + mm = wp.float64(m[i]) + fxi = wp.float64(fx[i]); fyi = wp.float64(fy[i]); fzi = wp.float64(fz[i]) + wp.atomic_add(mi, 0, mm) + wp.atomic_add(mi, 1, mm * xi); wp.atomic_add(mi, 2, mm * yi) + wp.atomic_add(mi, 3, mm * zi) + wp.atomic_add(mi, 4, mm * (yi * yi + zi * zi)) + wp.atomic_add(mi, 5, mm * (xi * xi + zi * zi)) + wp.atomic_add(mi, 6, mm * (xi * xi + yi * yi)) + wp.atomic_add(mi, 7, -mm * xi * yi); wp.atomic_add(mi, 8, -mm * xi * zi) + wp.atomic_add(mi, 9, -mm * yi * zi) + wp.atomic_add(mi, 10, fxi); wp.atomic_add(mi, 11, fyi) + wp.atomic_add(mi, 12, fzi) + wp.atomic_add(mi, 13, yi * fzi - zi * fyi) + wp.atomic_add(mi, 14, zi * fxi - xi * fzi) + wp.atomic_add(mi, 15, xi * fyi - yi * fxi) + + +@wp.kernel +def reduce_f64(x: wp.array(dtype=wp.float64), y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), m: wp.array(dtype=wp.float64), + fx: wp.array(dtype=wp.float64), fy: wp.array(dtype=wp.float64), + fz: wp.array(dtype=wp.float64), mi: wp.array(dtype=wp.float64)): + i = wp.tid() + xi = x[i]; yi = y[i]; zi = z[i]; mm = m[i] + fxi = fx[i]; fyi = fy[i]; fzi = fz[i] + wp.atomic_add(mi, 0, mm) + wp.atomic_add(mi, 1, mm * xi); wp.atomic_add(mi, 2, mm * yi) + wp.atomic_add(mi, 3, mm * zi) + wp.atomic_add(mi, 4, mm * (yi * yi + zi * zi)) + wp.atomic_add(mi, 5, mm * (xi * xi + zi * zi)) + wp.atomic_add(mi, 6, mm * (xi * xi + yi * yi)) + wp.atomic_add(mi, 7, -mm * xi * yi); wp.atomic_add(mi, 8, -mm * xi * zi) + wp.atomic_add(mi, 9, -mm * yi * zi) + wp.atomic_add(mi, 10, fxi); wp.atomic_add(mi, 11, fyi) + wp.atomic_add(mi, 12, fzi) + wp.atomic_add(mi, 13, yi * fzi - zi * fyi) + wp.atomic_add(mi, 14, zi * fxi - xi * fzi) + wp.atomic_add(mi, 15, xi * fyi - yi * fxi) + + +@wp.kernel +def transform(x: wp.array(dtype=wp.float64), y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + cm: wp.vec3d, vc: wp.vec3d, omega: wp.vec3d, + u: wp.array(dtype=wp.float64), v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64)): + # RigidBodyMotion.initialize: v_particle = vc + omega x r (r = pos - cm). + i = wp.tid() + rx = x[i] - cm[0]; ry = y[i] - cm[1]; rz = z[i] - cm[2] + u[i] = vc[0] + omega[1] * rz - omega[2] * ry + v[i] = vc[1] + omega[2] * rx - omega[0] * rz + w[i] = vc[2] + omega[0] * ry - omega[1] * rx + + +def run_reduce(kernel, arrays, dtype): + n = arrays[0].shape[0] + mi = wp.zeros(16, dtype=dtype, device=DEV) + wp.launch(kernel, dim=n, inputs=list(arrays) + [mi], device=DEV) + wp.synchronize_device(DEV) + return mi.numpy().astype(np.float64) + + +def main(): + print(f"warp {wp.config.version} device={wp.get_device(DEV)}") + x, y, z, m, fx, fy, fz = make_body() + n = x.size + print(f"synthetic body: {n} particles, solid_rho=500\n") + + # Two references: from full-f64 inputs, and from the SAME f32-rounded inputs + # the GPU f32/f64acc kernels actually see. Comparing each GPU result to the + # reference built from *its own* inputs isolates "is the reduction exact?" + # from "do f32 inputs carry less precision?" (the latter is inherent, fine). + x32, y32, z32, m32, fx32, fy32, fz32 = ( + a.astype(np.float32).astype(np.float64) for a in (x, y, z, m, fx, fy, fz)) + ref64 = reference_mi(x, y, z, m, fx, fy, fz) + ref_f32in = reference_mi(x32, y32, z32, m32, fx32, fy32, fz32) + + # device arrays + def arr(a, dt): + return wp.array(a.astype(dt), dtype=(wp.float32 if dt == np.float32 + else wp.float64), device=DEV) + a32 = [arr(a, np.float32) for a in (x, y, z, m, fx, fy, fz)] + a64 = [arr(a, np.float64) for a in (x, y, z, m, fx, fy, fz)] + + mi_f32 = run_reduce(reduce_f32, a32, wp.float32) + mi_f64acc = run_reduce(reduce_f64acc, a32, wp.float64) + mi_f64 = run_reduce(reduce_f64, a64, wp.float64) + + def relerr(a, b): + b = np.where(np.abs(b) < 1e-30, 1.0, b) + return np.max(np.abs((a - b) / b)) + + e_f32 = relerr(mi_f32, ref_f32in) + e_f64acc = relerr(mi_f64acc, ref_f32in) + e_f64 = relerr(mi_f64, ref64) + print("=== 16-slot mi reduction: GPU vs numpy RigidBodyMoments " + "(max rel err, each vs the ref from its OWN inputs) ===") + print(f" f32 accum (f32 data) : {e_f32:.3e} " + f"<- f32 atomic_add round-off") + print(f" f64 accum (f32 data, MITIG) : {e_f64acc:.3e} " + f"<- reduction exact given f32 inputs") + print(f" f64 accum (f64 data) : {e_f64:.3e}") + print(f" (f64acc vs full-f64 ref: {relerr(mi_f64acc, ref64):.2e} -- the " + f"residual is the f32 INPUT precision, inherent to the f32 path)") + + # ---- non-determinism: 20 relaunches, spread of each strategy ---- + def spread(kernel, arrays, dtype, k=20): + runs = np.stack([run_reduce(kernel, arrays, dtype) for _ in range(k)]) + return np.max(runs, axis=0) - np.min(runs, axis=0), runs[0] + sp32, _ = spread(reduce_f32, a32, wp.float32) + sp64a, _ = spread(reduce_f64acc, a32, wp.float64) + sp64, _ = spread(reduce_f64, a64, wp.float64) + # express spread as relative to the reference magnitude + denom = np.where(np.abs(ref64) < 1e-30, 1.0, np.abs(ref64)) + print("\n=== run-to-run NON-DETERMINISM over 20 launches (max rel spread) ===") + print(f" f32 accum (f32 data) : {np.max(sp32/denom):.3e} " + f"{'<- non-associative atomic_add' if np.max(sp32)>0 else '(bit-exact)'}") + print(f" f64 accum (f32 data, MITIG): {np.max(sp64a/denom):.3e}") + print(f" f64 accum (f64 data) : {np.max(sp64/denom):.3e}") + + # ---- full host finalize: f64-data path vs numpy reference (clean f64 + # correctness check), then the f32-path physical values for sanity. ---- + omega = np.array([0.3, -0.5, 0.2]) # nonzero -> exercises w x (I w) + R = finalize(ref64, omega) + G = finalize(mi_f64, omega) + Gf32 = finalize(mi_f64acc, omega) + print("\n=== host finalize (f64 path) vs numpy RigidBodyMoments ===") + for key in ("total_mass", "cm", "force", "ac", "torque", "omega_dot"): + a, b = np.atleast_1d(G[key]), np.atleast_1d(R[key]) + print(f" {key:11s} max abs err {np.max(np.abs(a-b)):.3e} " + f"value={np.array2string(b, precision=5)}") + print(f" inertia tensor max abs err {np.max(np.abs(G['I']-R['I'])):.3e}") + print(f" [f32 path] torque={np.array2string(Gf32['torque'], precision=5)} " + f"omega_dot={np.array2string(Gf32['omega_dot'], precision=5)}") + + # ---- device rigid-transform (RigidBodyMotion) vs numpy ---- + vc = np.array([0.4, -0.1, 0.05]) + cm = R["cm"] + u = wp.zeros(n, dtype=wp.float64, device=DEV) + v = wp.zeros(n, dtype=wp.float64, device=DEV) + w = wp.zeros(n, dtype=wp.float64, device=DEV) + wp.launch(transform, dim=n, + inputs=[a64[0], a64[1], a64[2], + wp.vec3d(*cm), wp.vec3d(*vc), wp.vec3d(*omega), u, v, w], + device=DEV) + wp.synchronize_device(DEV) + rx, ry, rz = x - cm[0], y - cm[1], z - cm[2] + u_ref = vc[0] + omega[1] * rz - omega[2] * ry + v_ref = vc[1] + omega[2] * rx - omega[0] * rz + w_ref = vc[2] + omega[0] * ry - omega[1] * rx + terr = max(np.max(np.abs(u.numpy() - u_ref)), + np.max(np.abs(v.numpy() - v_ref)), + np.max(np.abs(w.numpy() - w_ref))) + print(f"\n=== device rigid-transform (v = vc + omega x r) vs numpy ===") + print(f" max abs err: {terr:.3e}") + + # ---- verdict (thresholds reflect what each path CAN achieve) ---- + spread_f32 = np.max(sp32 / denom) + spread_f64acc = np.max(sp64a / denom) + od_rel = (np.max(np.abs(G['omega_dot'] - R['omega_dot'])) / + max(np.max(np.abs(R['omega_dot'])), 1e-300)) + ok_acc = e_f64 < 1e-11 and e_f64acc < 1e-10 # reduction exact given inputs + ok_det = spread_f64acc < 1e-11 # f64 accum ~ deterministic + ok_fin = od_rel < 1e-9 and np.max(np.abs(G['I'] - R['I'])) < 1e-10 + ok_xf = terr < 1e-12 + f32_risk = spread_f32 > 1e-7 # f32 accum is NOT determini. + print("\n=== VERDICT ===") + print(f" reduction exact (f64 accum, given f32 in) : {ok_acc} " + f"(err {e_f64acc:.1e})") + print(f" f64 accum ~deterministic : {ok_det} " + f"(spread {spread_f64acc:.1e})") + print(f" fp32 accum IS non-deterministic (risk) : {f32_risk} " + f"(spread {spread_f32:.1e}) -> mitigated by f64 accum") + print(f" host finalize matches (incl real torque) : {ok_fin} " + f"(omega_dot rel {od_rel:.1e})") + print(f" device rigid-transform matches : {ok_xf}") + passed = ok_acc and ok_det and ok_fin and ok_xf + print(f"\n P0 {'PASS' if passed else 'FAIL'} -- atomic_add rigid-body " + f"reduction {'is VIABLE; design decision: accumulate in f64' + if passed else 'needs rework'}.") + return 0 if passed else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/warp_floating_box_runner.py b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/warp_floating_box_runner.py new file mode 100644 index 000000000..d9e199b2b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/warp_floating_box_runner.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +"""Warp 3D dam-break surge coupled to a floating rigid box (ADR-0006 P3).""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +DAM_PACKET = HERE.parent / '2026-06-18_warp-dam-break-3d-runner' +sys.path.insert(0, str(DAM_PACKET)) +from dam_break_3d_runner import ( # noqa: E402 + GRAVITY, REF_C0, WarpDamBreak3DRunner, damp_factor, +) + +from pysph.base.utils import get_particle_array # noqa: E402 +from pysph.base.warp_nnps import UniformGridWarpNNPS # noqa: E402 +from pysph.base.warp_sph import ( # noqa: E402 + create_rigid_body_state, wc_sph_dam_break_rigid_step, +) + + +def make_box(dx, h, rho0, body_density=500.0, + center=(2.35, 0.0, 0.30), size=(0.32, 0.28, 0.20)): + """Create a shell-sampled rectangular body with physical total mass.""" + axes = [] + for length in size: + n = max(3, int(round(length / dx)) + 1) + axes.append(np.linspace(-0.5 * length, 0.5 * length, n)) + ix, iy, iz = np.meshgrid( + np.arange(len(axes[0])), np.arange(len(axes[1])), + np.arange(len(axes[2])), indexing='ij') + shell = ((ix == 0) | (ix == len(axes[0]) - 1) | + (iy == 0) | (iy == len(axes[1]) - 1) | + (iz == 0) | (iz == len(axes[2]) - 1)) + X, Y, Z = np.meshgrid(*axes, indexing='ij') + xyz = np.column_stack((X[shell], Y[shell], Z[shell])) + xyz += np.asarray(center) + n = len(xyz) + total_mass = body_density * np.prod(size) + zeros = np.zeros(n) + pa = get_particle_array( + name='body', x=xyz[:, 0], y=xyz[:, 1], z=xyz[:, 2], + h=np.full(n, h), m=np.full(n, total_mass / n), + rho=np.full(n, rho0), p=zeros.copy(), cs=zeros.copy(), + u=zeros.copy(), v=zeros.copy(), w=zeros.copy(), + arho=zeros.copy(), fx=zeros.copy(), fy=zeros.copy(), fz=zeros.copy(), + V=zeros.copy(), rho0=zeros.copy(), backend='warp') + pa.add_property('body_id', type='int', data=np.zeros(n, dtype=np.int32)) + return pa + + +def run(args): + base = WarpDamBreak3DRunner( + dx=args.dx, hdx=args.hdx, rho0=args.rho0, c0=args.c0, + alpha=args.alpha, beta=0.0, xsph_eps=args.xsph_eps, + n_damp=args.n_damp, adaptive_dt=True, cfl=args.cfl, + nboundary_layers=args.nboundary_layers) + fluid, wall = base.create_particles() + body = make_box( + args.dx, base.h0, args.rho0, body_density=args.body_density, + center=(args.box_x, 0.0, args.box_z)) + state = create_rigid_body_state(body, nbody=1) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall, body], radius_scale=2.0) + + initial_xyz = np.column_stack((body.x.copy(), body.y.copy(), body.z.copy())) + initial_cm = np.average(initial_xyz, axis=0, weights=body.m) + time_now = 0.0 + dt_history = [] + step = 0 + while step < args.steps and (args.tf is None or time_now < args.tf): + scale = damp_factor(step, args.n_damp) + dt_used = wc_sph_dam_break_rigid_step( + nnps, state, fluid_index=0, wall_indices=(1,), rigid_index=2, + dt=base.dt, rho0=args.rho0, c0=args.c0, alpha=args.alpha, + beta=0.0, gamma=7.0, kernel='wendland', + xsph_eps=args.xsph_eps, gz=-GRAVITY, adaptive_dt=True, + cfl=args.cfl, dt_min=0.0, dt_max=np.inf, + adaptive_dt_scale=scale, step_dt_max=np.inf, + push=(step == 0), return_dt=True) + dt_history.append(dt_used) + time_now += dt_used + step += 1 + + fluid.gpu.pull('x', 'y', 'z', 'rho', 'u', 'v', 'w') + wall.gpu.pull('x', 'y', 'z') + body.gpu.pull('x', 'y', 'z', 'rho', 'u', 'v', 'w', 'fx', 'fy', 'fz') + body_xyz = np.column_stack((body.x, body.y, body.z)) + body_vel = np.column_stack((body.u, body.v, body.w)) + final_cm = np.average(body_xyz, axis=0, weights=body.m) + vc = state.vc.numpy().reshape(1, 3)[0] + omega = state.omega.numpy().reshape(1, 3)[0] + d0 = np.linalg.norm(initial_xyz - initial_xyz[0], axis=1) + d1 = np.linalg.norm(body_xyz - body_xyz[0], axis=1) + all_finite = all(np.isfinite(a).all() for a in ( + fluid.x, fluid.y, fluid.z, fluid.rho, body_xyz, body_vel, + body.rho, vc, omega)) + metrics = { + 'fluid_particles': int(len(fluid.x)), + 'wall_particles': int(len(wall.x)), + 'body_particles': int(len(body.x)), + 'steps': step, 'time': float(time_now), 'dx': args.dx, + 'all_finite': bool(all_finite), + 'device_error': int(state.error.numpy()[0]), + 'initial_cm': initial_cm.tolist(), 'final_cm': final_cm.tolist(), + 'cm_displacement': (final_cm - initial_cm).tolist(), + 'vc': vc.tolist(), 'omega': omega.tolist(), + 'body_force': np.sum(np.c_[body.fx, body.fy, body.fz], axis=0).tolist(), + 'body_rho_min': float(body.rho.min()), + 'body_rho_max': float(body.rho.max()), + 'fluid_rho_min': float(fluid.rho.min()), + 'fluid_rho_max': float(fluid.rho.max()), + 'relative_geometry_drift': float( + np.max(np.abs(d1 - d0)) / max(float(d0.max()), 1e-30)), + 'dt_min': float(np.min(dt_history)), + 'dt_max': float(np.max(dt_history)), + } + if args.output: + path = Path(args.output) + path.parent.mkdir(parents=True, exist_ok=True) + np.savez( + path, metrics=json.dumps(metrics, sort_keys=True), + fluid_x=fluid.x, fluid_y=fluid.y, fluid_z=fluid.z, + fluid_u=fluid.u, fluid_v=fluid.v, fluid_w=fluid.w, + body_x=body.x, body_y=body.y, body_z=body.z, + body_u=body.u, body_v=body.v, body_w=body.w, + wall_x=wall.x, wall_y=wall.y, wall_z=wall.z) + return metrics + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--dx', type=float, default=0.10) + p.add_argument('--hdx', type=float, default=1.3) + p.add_argument('--steps', type=int, default=20) + p.add_argument('--tf', type=float, default=None) + p.add_argument('--rho0', type=float, default=1000.0) + p.add_argument('--c0', type=float, default=REF_C0) + p.add_argument('--alpha', type=float, default=0.25) + p.add_argument('--xsph-eps', type=float, default=0.5) + p.add_argument('--cfl', type=float, default=0.3) + p.add_argument('--n-damp', type=int, default=50) + p.add_argument('--nboundary-layers', type=int, default=1) + p.add_argument('--body-density', type=float, default=500.0) + p.add_argument('--box-x', type=float, default=2.35) + p.add_argument('--box-z', type=float, default=0.30) + p.add_argument('--output', default=None) + args = p.parse_args() + metrics = run(args) + print(json.dumps(metrics, indent=2, sort_keys=True)) + if not metrics['all_finite'] or metrics['device_error']: + raise SystemExit('coupled run failed finiteness/device-error gate') + + +if __name__ == '__main__': + main() diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/experiment.md b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/experiment.md new file mode 100644 index 000000000..c917215f2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/experiment.md @@ -0,0 +1,124 @@ +--- +type: experiment +id: 2026-07-06_warp-adaptive-particle-resolution-p0 +created: 2026-07-06T10:45:00 CEST +author: @kunalpuri-prediqt +aspect: validation-benchmarks +status: active +last_checked: 2026-07-06T11:18:54 CEST +--- + +# Experiment: Warp adaptive particle resolution P0 + +## Purpose + +Establish the non-adaptive fixed-obstacle baseline and select evidence-backed +3D split/merge primitives before designing the multilevel NNPS or device pool. + +## Setup + +- Existing `WarpDamBreak3DRunner`, extended additively with + `--with-obstacle`; fluid index 0, wall index 1, obstacle index 2. +- Local active Warp environment and GPU recorded with every output. +- Candidate split patterns drawn from Vacondio et al. 2016 and the audited + Muta--Ramachandran source, evaluated first with NumPy density reconstruction. + +## Hypothesis + +The existing multi-solid WCSPH driver will run the fixed Kleefsman obstacle +without backend changes. The published icosahedral/central 3D split should have +lower density error than a cubic eight-child split, but its particle-growth +cost may justify an iterative split/merge alternative on the GPU. + +## Execution + +1. Run a coarse 1--5 step obstacle smoke case and save metrics/state. +2. Run a longer coarse transient only after the smoke remains finite. +3. Ingest exact published 3D stencil parameters and reproduce density-error + curves in a standalone kill test. +4. Compare candidate allocation count, neighbor count, isotropy, conservation, + and density error in fp32/fp64. + +## Results + +### Reference/source audit + +- The open Muta--Ramachandran PySPH implementation confirms the desired + parallel workflow, but its automatic background path is restricted to 2D, + several target-mass equations have explicit 3D FIXMEs, and its GPU particle + lifecycle raises `NotImplementedError`. It is a process reference, not a 3D + implementation to transplant. +- Vacondio et al. compare four 3D stencils and select a 12-vertex icosahedron + plus center (13 daughters) with Wendland `epsilon=0.65`, `alpha=0.70`. + +### Fixed-obstacle runner checkpoint + +Hardware: NVIDIA GeForce RTX 4060 Laptop GPU, 8 GiB, `sm_89`; Warp 1.14.0, +CUDA toolkit 12.9, driver API reported as 13.2; fp32 backend. + +At `dx=0.10`, the shared `DamBreak3DGeometry` creates 1,000 fluid, 3,824 wall, +and 4 fixed obstacle particles. The first obstacle execution paid the known +generated-Wendland cache load (three generated modules reported 40.410, +54.966, and 52.890 seconds). Warm results: + +```text +case steps t elapsed finite +obstacle smoke 1 0.000001171 cached true +obstacle startup 20 0.003095018 3.16 s true +obstacle first impact 250 0.258454926 5.76 s true +``` + +First-impact metrics: + +```text +surge_front_x: 2.4901464 m +max_height: 0.9764588 m +fluid rho: 983.44897 .. 1022.19061 kg/m^3 +fluid p: -16.946 .. 25.564 kPa +wall p: 0 .. 49.672 kPa +obstacle p: 23.696 .. 150.147 kPa +kinetic_energy: 292.19788 J +device obstacle drift: 0 exactly (step 1 vs step 250 arrays) +process max RSS: 350,756 KiB +``` + +The no-obstacle one-step compatibility run also remains finite with the original +1,000 fluid + 3,824 wall arrays and reports `obstacle_particles=0`. + +### Split-stencil density kill test + +`split_stencil_density_kill.py` implements the paper's constrained global +density-error minimization with the exact PySPH/Warp 3D WendlandQuintic C2 +formula. A deterministic tensor integration converges at 81/101/121 points per +axis. At 101 points and the paper's `epsilon=0.65`, `alpha=0.70`: + +```text +stencil daughters optimized E equal-mass E +cubic + center 9 1.2586503e-3 1.2687077e-3 +icosahedron + center 13 3.5803204e-4 5.5872945e-4 +``` + +The icosahedral constrained masses are 12 x `0.0739476671` on the shell plus +`0.1126279952` at the center; mass sums to exactly 1 in the calculation. This +confirms the icosahedral advantage and the value of unequal masses. However, +the reproduction does **not** match the paper's Table 1 value (`E=8.326e-5`, +`min/max mass=0.33`): the current calculation gives `E=3.58032e-4` and +`min/max=0.656566`. Grid convergence rules out quadrature resolution as the +cause. Kernel/smoothing-length convention or an unrecorded stencil detail must +be resolved before the published masses are treated as an oracle. + +## Conclusion + +The existing multi-solid Warp backend can run the fixed Kleefsman obstacle +without backend changes. This resolves the obstacle-wiring part of P0. P0 +remains active: exact 3D split constants, density-error kill tests, probes, and +uniform coarse/fine baselines are still required before ADR-0007. The first +density kill test correctly blocks the ADR because it exposes a paper/PySPH +convention mismatch. + +## Follow-ups + +- Resolve the Vacondio/PySPH Wendland or stencil convention mismatch exposed by + the converged density-error reproduction. +- Add obstacle probe/impulse metrics and uniform coarse/fine baselines. +- Select ADR-0007 only after scientific and GPU-cost evidence agree. diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step20.npz b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step20.npz new file mode 100644 index 000000000..c2eee94f8 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step20.npz differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step250.npz b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step250.npz new file mode 100644 index 000000000..e301a5fe1 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-dx100-step250.npz differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-smoke-dx100-step1.npz b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-smoke-dx100-step1.npz new file mode 100644 index 000000000..a6ba79d28 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/obstacle-smoke-dx100-step1.npz differ diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split-stencil-density-ngrid101.json b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split-stencil-density-ngrid101.json new file mode 100644 index 000000000..6ddaf77c5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split-stencil-density-ngrid101.json @@ -0,0 +1,33 @@ +{ + "cubic_plus_center": { + "alpha": 0.7, + "center_mass_fraction": 0.1190748679598036, + "daughters_with_center": 9, + "epsilon": 0.65, + "equal_mass_integrated_density_error": 0.0012687077198617184, + "grid_points_per_axis": 101, + "integrated_density_error": 0.0012586502578159197, + "mass_sum": 1.0, + "min_max_mass_ratio": 0.9247597195925219, + "vertex_mass_fraction": 0.11011564150502455, + "vertices": 8 + }, + "icosahedron_plus_center": { + "alpha": 0.7, + "center_mass_fraction": 0.11262799518247202, + "daughters_with_center": 13, + "epsilon": 0.65, + "equal_mass_integrated_density_error": 0.0005587294531998937, + "grid_points_per_axis": 101, + "integrated_density_error": 0.00035803203580248407, + "mass_sum": 1.0, + "min_max_mass_ratio": 0.6565655985292331, + "vertex_mass_fraction": 0.07394766706812733, + "vertices": 12 + }, + "kernel": "PySPH WendlandQuintic C2 3D", + "paper_icosahedron_reference": { + "integrated_density_error": 8.326e-05, + "min_max_mass_ratio": 0.33 + } +} diff --git a/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split_stencil_density_kill.py b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split_stencil_density_kill.py new file mode 100644 index 000000000..111e87902 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/experiments/2026-07-06_warp-adaptive-particle-resolution-p0/split_stencil_density_kill.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +"""Reproduce the Vacondio 3D split-stencil density minimization. + +This is deliberately NumPy-only. It isolates the scientific stencil and mass +weights before any GPU allocation or Warp kernel design is allowed to depend on +them. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + + +def wendland_c2_3d(r, h): + """PySPH/Warp WendlandQuintic: support ``r/h < 2``.""" + q = r / h + out = np.zeros_like(q) + mask = q < 2.0 + tmp = 1.0 - 0.5 * q[mask] + out[mask] = ( + 21.0 / (16.0 * np.pi * h**3) * tmp**4 * (2.0 * q[mask] + 1.0) + ) + return out + + +def cubic_vertices(): + vertices = np.asarray([ + (x, y, z) + for x in (-1.0, 1.0) + for y in (-1.0, 1.0) + for z in (-1.0, 1.0) + ]) + return vertices / np.linalg.norm(vertices, axis=1)[:, None] + + +def icosahedron_vertices(): + phi = 0.5 * (1.0 + np.sqrt(5.0)) + vertices = [] + for a in (-1.0, 1.0): + for b in (-phi, phi): + vertices.extend(((0.0, a, b), (a, b, 0.0), (b, 0.0, a))) + vertices = np.unique(np.asarray(vertices), axis=0) + return vertices / np.linalg.norm(vertices, axis=1)[:, None] + + +def integrate_stencil(vertices, epsilon, alpha, ngrid): + """Solve the symmetry-reduced, mass-constrained least-squares problem.""" + limit = 2.0 + epsilon + 0.05 + axis = np.linspace(-limit, limit, ngrid) + spacing = axis[1] - axis[0] + nvertices = len(vertices) + + numerator = 0.0 + denominator = 0.0 + cached = [] + for z in axis: + x, y = np.meshgrid(axis, axis, indexing='ij') + points = np.stack((x, y, np.full_like(x, z)), axis=-1) + parent = wendland_c2_3d(np.linalg.norm(points, axis=-1), 1.0) + center = wendland_c2_3d(np.linalg.norm(points, axis=-1), alpha) + shell = np.zeros_like(parent) + for vertex in vertices: + shell += wendland_c2_3d( + np.linalg.norm(points - epsilon * vertex, axis=-1), alpha + ) + direction = shell - nvertices * center + numerator += np.sum((parent - center) * direction) + denominator += np.sum(direction * direction) + cached.append((parent, center, shell)) + + vertex_mass = numerator / denominator + center_mass = 1.0 - nvertices * vertex_mass + error = 0.0 + equal_error = 0.0 + equal_mass = 1.0 / (nvertices + 1) + for parent, center, shell in cached: + reconstruction = vertex_mass * shell + center_mass * center + equal_reconstruction = equal_mass * (shell + center) + error += np.sum((parent - reconstruction)**2) + equal_error += np.sum((parent - equal_reconstruction)**2) + error *= spacing**3 + equal_error *= spacing**3 + + return { + 'vertices': nvertices, + 'daughters_with_center': nvertices + 1, + 'epsilon': epsilon, + 'alpha': alpha, + 'vertex_mass_fraction': float(vertex_mass), + 'center_mass_fraction': float(center_mass), + 'mass_sum': float(nvertices * vertex_mass + center_mass), + 'min_max_mass_ratio': float( + min(vertex_mass, center_mass) / max(vertex_mass, center_mass) + ), + 'integrated_density_error': float(error), + 'equal_mass_integrated_density_error': float(equal_error), + 'grid_points_per_axis': ngrid, + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--ngrid', type=int, default=101) + parser.add_argument('--epsilon', type=float, default=0.65) + parser.add_argument('--alpha', type=float, default=0.70) + parser.add_argument('--output') + args = parser.parse_args() + + result = { + 'kernel': 'PySPH WendlandQuintic C2 3D', + 'paper_icosahedron_reference': { + 'integrated_density_error': 8.326e-5, + 'min_max_mass_ratio': 0.33, + }, + 'cubic_plus_center': integrate_stencil( + cubic_vertices(), args.epsilon, args.alpha, args.ngrid + ), + 'icosahedron_plus_center': integrate_stencil( + icosahedron_vertices(), args.epsilon, args.alpha, args.ngrid + ), + } + text = json.dumps(result, indent=2, sort_keys=True) + print(text) + if args.output: + Path(args.output).write_text(text + '\n') + return 0 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/glossary.md b/.ai/implementations/blast-from-the-past/glossary.md new file mode 100644 index 000000000..ea4d08547 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/glossary.md @@ -0,0 +1,9 @@ +# Glossary + +- **blast-from-the-past** - Implementation track exploring NVIDIA Warp for PySPH GPU particle dynamics. +- **Warp** - NVIDIA Warp, the candidate GPU programming/runtime layer for this implementation. Confirm exact package version and docs before ADRs depend on API details. +- **NNPS** - Nearest-neighbor particle search; a core PySPH hot path and major GPU integration target. +- **ParticleArray** - PySPH particle storage abstraction with typed properties and optional device helpers. +- **Device helper** - Existing PySPH/Compyle bridge that mirrors particle arrays to GPU/device arrays. +- **Boundary** - The approved set of host files/modules this implementation may touch. +- **Experiment** - A tracked benchmark, validation run, or parameter study whose result may inform an ADR. diff --git a/.ai/implementations/blast-from-the-past/host-project-notes.md b/.ai/implementations/blast-from-the-past/host-project-notes.md new file mode 100644 index 000000000..91ac84fb5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/host-project-notes.md @@ -0,0 +1,44 @@ +# Host Project Notes + +Just enough about PySPH for `blast-from-the-past` to integrate cleanly. This is not a host catalogue. + +## Stack and Toolchain + +- Language/runtime: Python plus Cython extension modules and C++-mode Cython for several low-level paths. +- Build command: `python setup.py build_ext --inplace` or `make build`. +- Default test command: `python -m pytest -m "not slow" pysph` or `make test`. +- Full test command: `python -m pytest pysph` or `make testall`. +- Parallel/Zoltan test command from CI: `python -m pytest -v -m 'slow or parallel'`. +- Lint/format: no explicit root `ruff`, `black`, `isort`, `flake8`, or `mypy` config found during scaffold discovery. Confirm with team before introducing new style tooling. + +## Integration Boundary + +- `pysph/**/*.pxd` - public Cython declarations and extension ABI surface. +- `pysph/**/*.pyx` - implementation files for Cython particle arrays, NNPS, kernels, MPI exchange, and mesh tooling. +- `pysph/base/gpu_nnps.py` - re-export module for GPU NNPS classes. + +## Boundary Surface Observed During Discovery + +- `pysph/base/gpu_nnps_base.pxd` declares `GPUNeighborCache`, `GPUNNPS`, and `BruteForceNNPS`. +- `pysph/base/gpu_nnps_base.pyx` implements GPU neighbor cache allocation, GPU-to-CPU neighbor retrieval, bounds computation, and brute-force PyOpenCL neighbor kernels. +- `pysph/base/gpu_nnps.py` re-exports `GPUNeighborCache`, `GPUNNPS`, `BruteForceNNPS`, `ZOrderGPUNNPS`, `StratifiedSFCGPUNNPS`, `GPUDomainManager`, and `OctreeGPUNNPS`. +- Broader `.pxd/.pyx` surface includes `ParticleArray`, `DomainManager`, `NNPS`, CPU/GPU NNPS variants, kernels, point/linalg helpers, `ParallelManager`, and mesh tools. + +## Host Conventions We Inherit + +- Pytest default excludes `slow` tests via `setup.cfg` and `tox.ini`. +- Build/test commands should follow the existing Makefile and CI conventions unless an ADR approves a change. +- Cython files use `# cython: language_level=3, embedsignature=True` in many active modules. Confirm with team before changing Cython compiler directives. + +## Pre-Existing Host AI Configs + +- None found during scaffold discovery among `.cursorrules`, `AGENTS.md`, `CLAUDE.md`, `OPENAI.md`, `.aider.conf.yml`, and `.github/copilot-instructions.md`. + +## Secrets Locations + +- (none identified; do not copy secrets into `.ai/`) + +## Out-of-Scope Zones + +- Host application code outside `pysph/**/*.pxd`, `pysph/**/*.pyx`, and the discovered GPU NNPS export file unless a boundary amendment is approved. +- General SPH formulation redesign. diff --git a/.ai/implementations/blast-from-the-past/implementation.md b/.ai/implementations/blast-from-the-past/implementation.md new file mode 100644 index 000000000..5fe837972 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/implementation.md @@ -0,0 +1,74 @@ +--- +implementation: blast-from-the-past +host_project: PySPH +created: 2026-06-15T07:19:08 CET +owner: @kunalpuri-prediqt +status: active +--- + +# blast-from-the-past + +## Summary + +Nostalgia and exploration with NVIDIA Warp as a path toward PySPH on GPUs. + +## Goal + +PySPH on GPUs. + +## Non-goals + +Do not reinvent SPH formulations; focus on GPU execution and integration. + +## Success criteria + +Blazing fast particle dynamics. Confirm with team: define concrete timing, throughput, hardware, and correctness thresholds before first benchmark claims. + +## Integration boundary + +The set of host files, modules, and interfaces this implementation interacts with. Anything outside this list is out of scope unless approved through a boundary amendment. + +- `pysph/**/*.pxd` - Cython declarations and ABI/public extension surfaces that may constrain Warp integration. +- `pysph/**/*.pyx` - Cython implementation files that may need wrapping, comparison, or future implementation changes. +- `pysph/base/gpu_nnps.py` - GPU NNPS export surface read during discovery because it re-exports the active GPU NNPS classes. +- `pysph/base/warp_*.py` - Python Warp prototype helpers for ParticleArray, + NNPS, and SPH equation/integrator checkpoints. +- `pysph/base/tests/test_warp_*.py` - focused tests for the Python Warp + prototype helpers. +- `CODEBASE_UNDERSTANDING.md` - repository architecture snapshot retained as a + host-level reference at the implementation owner's request. + +## Boundary amendments + +- 2026-06-16 - Added Python Warp prototype files and focused Warp tests to the + active implementation boundary for the repeated-step leapfrog checkpoint. +- 2026-06-21 - Added the owner-provided root `CODEBASE_UNDERSTANDING.md` + architecture snapshot so it can be versioned with the curated implementation + spec rather than left as an unexplained untracked file. + +## Aspects + +- `warp-backend` - Tracks NVIDIA Warp API choices, kernel model, memory layout assumptions, and how Warp maps onto PySPH GPU abstractions. +- `gpu-nnps` - Tracks neighbor-search design and performance around `GPUNNPS`, caches, and GPU neighbor lists. +- `particle-memory` - Tracks ParticleArray/device data ownership, transfers, dtype/precision, and compatibility with existing device helpers. +- `cython-boundary` - Tracks what remains in `.pxd/.pyx`, what can be wrapped or bypassed, and how to preserve ABI/API expectations. +- `validation-benchmarks` - Tracks baselines, timings, correctness checks, and acceptance thresholds for fast particle dynamics. +- `host-integration` - Tracks CLI/build/test integration, compatibility with existing GPU paths, and boundary amendments. + +## Key references + +See `.ai/implementations/blast-from-the-past/references/index.md`. Headline items: + +- Prabhu - human/internal reference; details to be captured in reference notes. +- NVIDIA Warp documentation - Confirm with team: add exact URL/version before using API details as decision evidence. + +## Milestones + +- Define measurable performance and correctness targets - target date: Confirm with team. +- Establish baseline GPU NNPS and particle-dynamics benchmark - target date: Confirm with team. +- Prototype Warp-backed path inside approved boundary - target date: Confirm with team. + +## Stakeholders + +- @kunalpuri-prediqt - implementation owner and user handle for closeouts. +- @prabhu - commit reviewer and key reference. diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-15_nest-implementation-memory-under-implementations-directory.md b/.ai/implementations/blast-from-the-past/plans/2026-06-15_nest-implementation-memory-under-implementations-directory.md new file mode 100644 index 000000000..a4776c026 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-15_nest-implementation-memory-under-implementations-directory.md @@ -0,0 +1,79 @@ +--- +type: plan +id: 2026-06-15_nest-implementation-memory-under-implementations-directory +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-15T00:00:00 CET +status: approved +aspects: [host-integration] +host_files: [] +within_boundary: true +--- + +# Plan: nest implementation memory under implementations directory + +## Goal + +Restructure the implementation memory so `blast-from-the-past` lives under `.ai/implementations/blast-from-the-past/`, with implementation-specific plans, decisions, ADR index/graph, reviews, updates, aspects, references, experiments, prompts, templates, skills, and scripts scoped inside that directory. + +## Context + +User preference: "id like that each implementation resides in its own directory under .ai so some thing like .ai/implementations/ and all the plans/decisions/adrs concerning that implementation are scoped within that directory". + +Current scaffold places implementation memory directly under `.ai/`. This is fine for one implementation but will not scale cleanly if the repository has multiple independent implementation memories. + +## Approach + +1. Create `.ai/implementations/blast-from-the-past/`. +2. Move implementation-scoped directories/files into that directory: + - `AGENTS.md`, `README.md`, `current.md`, `implementation.md`, `host-project-notes.md`, `conventions.md`, `glossary.md` + - `plans/`, `decisions/`, `reviews/`, `updates/`, `skills/`, `aspects/`, `references/`, `experiments/`, `prompts/`, `templates/`, `scripts/` +3. Leave a minimal top-level `.ai/README.md` explaining the multi-implementation layout. +4. Add a top-level `.ai/AGENTS.md` router that points agents to `.ai/implementations/blast-from-the-past/AGENTS.md`. +5. Update the root `AGENTS.md` pointer to the nested implementation contract. +6. Update script path assumptions so scripts still find the implementation root when run from their nested location. +7. Update references inside moved Markdown files from `.ai/...` to `.ai/implementations/blast-from-the-past/...` where needed. +8. Update the pre-commit hook to run the nested validator. +9. Regenerate the nested decision index/graph. +10. Run: + - `python .ai/implementations/blast-from-the-past/scripts/update-decision-graph.py` + - `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` + - `.git/hooks/pre-commit` + - `git diff --check -- .ai AGENTS.md` + +## Files expected to change + +- `.ai/` memory files only. +- root `AGENTS.md` pointer. +- `.git/hooks/pre-commit`. +- No host application code. + +## Tests / validation + +- Nested decision graph regeneration must pass. +- Nested validator must pass. +- Pre-commit hook must pass. +- `git diff --check -- .ai AGENTS.md` must pass. + +## Risks + +- Script path assumptions may break after moving scripts deeper. +- Markdown links and contract text may still point at old top-level locations. +- The top-level `.ai` needs enough routing information to make future implementation selection obvious without duplicating implementation memory. + +## Out of scope + +- No changes to PySPH host application code. +- No changes to implementation aspects or technical scope. +- No commit. + +## Estimated effort + +M - broad file movement and script path updates, but no host code changes. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-15T07:31:16 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-15_nnps-solver-agnostic-spec-and-warp-prototype.md b/.ai/implementations/blast-from-the-past/plans/2026-06-15_nnps-solver-agnostic-spec-and-warp-prototype.md new file mode 100644 index 000000000..4ecacbf24 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-15_nnps-solver-agnostic-spec-and-warp-prototype.md @@ -0,0 +1,72 @@ +--- +type: plan +id: 2026-06-15_nnps-solver-agnostic-spec-and-warp-prototype +author: codex +agent: codex +created: 2026-06-15T09:30:00 CET +status: approved +aspects: [gpu-nnps, particle-memory, warp-backend, validation-benchmarks] +host_files: [pysph/base/nnps_base.pyx, pysph/base/gpu_nnps_base.pyx, pysph/base/gpu_nnps_base.pxd, pysph/base/gpu_nnps.py, pysph/solver/application.py] +within_boundary: false +--- + +# Plan: NNPS Solver-Agnostic Spec And Warp Prototype + +## Trigger + +After the Warp ParticleArray mirror checkpoint, the next high-level migration +step is neighbor search. The codebase understanding document identifies NNPS as +the bridge between particle storage and generated equation loops. + +## Objective + +Define the solver-agnostic NNPS contract and prepare a Warp NNPS prototype that +can consume Warp-backed ParticleArray data without requiring full solver or SPH +equation migration. + +## Proposed Work + +1. Capture NNPS/domain/update/query/cache semantics in + `spec/nnps/`. +2. Add CPU-vs-Warp NNPS correctness experiments. +3. Implement a minimal Warp brute-force NNPS for correctness. +4. Implement a Warp cell-list NNPS for performance relevance. +5. Add explicit host integration only after the direct NNPS API passes. + +## Progress + +- User approved beginning NNPS implementation with: "ok. lets begin with the + NNPS implementation with warp". +- Added `pysph/base/warp_nnps.py` with `BruteForceWarpNNPS`. +- Added focused correctness tests in `pysph/base/tests/test_warp_nnps.py`. +- Added experiment packet + `experiments/2026-06-15_warp-nnps-bruteforce-baseline/`. +- Smoke benchmark confirms CPU and Warp average neighbor counts match, while + Warp brute force is slower due per-query kernel launch and readback. +- Added cached flat-neighbor-list mode to reduce per-query launch/readback + overhead. +- Added `UniformGridWarpNNPS`, which builds source cell lists on the device and + queries adjacent cells. +- Focused tests now cover the grid path in 1D/2D/3D, multiple arrays, variable + smoothing length, and update-after-mutation cases. + +## Initial Success Criteria + +- Spec identifies query contract, update timeline, domain/ghost semantics, + cache behavior, parallel boundary, and verification fixtures. +- First experiment compares CPU NNPS and Warp NNPS neighbor sets. +- Warp NNPS benchmark reports update, query, cache, and readback time + separately. + +## Out Of Scope + +- Migrating generated SPH equation kernels. +- Replacing MPI/Zoltan partitioning. +- Supporting every CPU NNPS variant immediately. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-15T10:05:00 CET +- Approval, verbatim quote: + > ok. lets begin with the NNPS implementation with warp diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-15_particle-array-solver-agnostic-spec-and-warp-migration.md b/.ai/implementations/blast-from-the-past/plans/2026-06-15_particle-array-solver-agnostic-spec-and-warp-migration.md new file mode 100644 index 000000000..6ba44ae0f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-15_particle-array-solver-agnostic-spec-and-warp-migration.md @@ -0,0 +1,110 @@ +--- +type: plan +id: 2026-06-15_particle-array-solver-agnostic-spec-and-warp-migration +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-15T00:00:00 CET +status: approved +aspects: [particle-memory, cython-boundary, warp-backend, host-integration, validation-benchmarks] +host_files: [pysph/base/particle_array.pxd, pysph/base/particle_array.pyx, pysph/base/utils.py, pysph/base/device_helper.py, pysph/base/tests/test_particle_array.py, pysph/base/tests/test_utils.py] +within_boundary: false +--- + +# Plan: particle array solver agnostic spec and warp migration + +## Goal + +Produce a solver-agnostic implementation specification for PySPH's base particle-array data structures, then use that spec to decide and plan the first NVIDIA Warp migration step for particle arrays. + +## Context + +User request: start the first implementation using NVIDIA Warp for particle array classes, using the attached solver-agnostic spec prompt to review base array data structures first. The top-level `CODEBASE_UNDERSTANDING.md` is a whole-codebase reference document. The local environment on `prediqt-02` can be activated with `source $HOME/prediqt/activate`. + +The attached prompt requires every spec claim to be labeled `[OBSERVED]`, `[INFERRED]`, or `[UNKNOWN]`, with `file:line` references for observed claims. It also requires starting with `open-questions.md`, tracing entry points/call graph, reading tests/docs before deep implementation files, and writing a `spec/` directory of Markdown files. + +Boundary note: this plan reads tests, docs, and Python helpers outside the `.pxd/.pyx` implementation boundary as discovery evidence only. It does not modify host application code. + +## Approach + +Phase 1 - solver-agnostic reference spec, no host code migration: + +1. Read relevant tests/docs first: + - `pysph/base/tests/test_particle_array.py` + - `pysph/base/tests/test_utils.py` + - relevant docs/tutorial references to `ParticleArray` + - top-level `CODEBASE_UNDERSTANDING.md` +2. Trace one representative lifecycle end-to-end: + - particle array creation through `get_particle_array` + - property/constant allocation + - host/device helper attachment + - output serialization/readback path where relevant +3. Read core implementation: + - `pysph/base/particle_array.pxd` + - `pysph/base/particle_array.pyx` + - `pysph/base/utils.py` + - `pysph/base/device_helper.py` + - targeted references from NNPS/solver/output only where needed for call placement and host contract. +4. Create spec files under `.ai/implementations/blast-from-the-past/spec/particle-array/`: + - `open-questions.md` + - `00-overview.md` + - `01-mesh-geometry.md` + - `02-timeline.md` + - `03-data-structures.md` + - `04-boundary.md` + - `05-parallelism.md` + - `06-host-contract.md` + - `07-variants.md` + - `08-interfaces.md` + - `09-verification.md` + - `10-porting.md` + - `glossary.md` +5. Update aspect context/open questions for durable discoveries. +6. If the spec implies an architectural choice for Warp memory ownership, create an ADR before any migration code. + +Phase 2 - Warp migration planning, gated: + +1. Activate the environment with `source $HOME/prediqt/activate` for runtime checks. +2. Probe availability/version of NVIDIA Warp and existing PySPH build/test state. +3. Create a separate implementation plan for the first code migration step, scoped to particle-array/device memory only. +4. Do not modify host application code in this plan unless the user explicitly approves a follow-up migration plan. + +## Files expected to change + +- `.ai/implementations/blast-from-the-past/spec/particle-array/**` +- `.ai/implementations/blast-from-the-past/aspects/particle-memory/*` +- `.ai/implementations/blast-from-the-past/aspects/cython-boundary/*` +- `.ai/implementations/blast-from-the-past/aspects/warp-backend/*` +- Possibly `.ai/implementations/blast-from-the-past/decisions/*` if an ADR is warranted. +- No PySPH host code in Phase 1. + +## Tests / validation + +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai AGENTS.md` +- For environment probing only after spec: `source $HOME/prediqt/activate && python -c "import warp; print(warp.__version__)"` if Warp is expected to be installed. +- Existing host tests are not required for Phase 1 because no host code changes are made; later migration plans must include focused tests. + +## Risks + +- The attached prompt is solver-agnostic CFD-oriented, while `ParticleArray` is infrastructure rather than a numerical flux/closure module; sections about governing equations, mesh, and boundary conditions will contain many `[UNKNOWN]` or "not applicable to this data structure" entries. +- `CODEBASE_UNDERSTANDING.md` is currently untracked; it can be read as user-provided context but should not be committed unless explicitly requested. +- Runtime Warp availability may differ from expected environment state. +- A premature migration could bake in the wrong ownership model; the spec/ADR gate is intended to avoid that. + +## Out of scope + +- Rewriting `ParticleArray` or `DeviceHelper` in this plan. +- Changing build dependencies or package metadata in this plan. +- Migrating NNPS, equation evaluation, integrators, or solver loop in this plan. +- Committing `CODEBASE_UNDERSTANDING.md`. + +## Estimated effort + +M/L - the spec is multi-file and citation-heavy; migration code requires a follow-up plan. + +## Approval + +- [ ] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-15T07:49:02 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-15_warp-particle-array-device-mirror-prototype.md b/.ai/implementations/blast-from-the-past/plans/2026-06-15_warp-particle-array-device-mirror-prototype.md new file mode 100644 index 000000000..bf274be47 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-15_warp-particle-array-device-mirror-prototype.md @@ -0,0 +1,97 @@ +--- +type: plan +id: 2026-06-15_warp-particle-array-device-mirror-prototype +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-15T08:42:00 CET +status: approved +aspects: [particle-memory, warp-backend, cython-boundary, host-integration, validation-benchmarks] +host_files: [pysph/base/particle_array.pyx, pysph/base/particle_array.pxd, pysph/base/device_helper.py, pysph/base/tests/test_particle_array.py, pysph/base/tests/test_device_helper.py] +within_boundary: false +--- + +# Plan: Warp ParticleArray Device Mirror Prototype + +## Goal + +Prototype the first NVIDIA Warp-backed ParticleArray path by adding a DeviceHelper-like mirror while preserving existing host `BaseArray` ownership and public ParticleArray behavior. + +## Context + +The solver-agnostic spec in `.ai/implementations/blast-from-the-past/spec/particle-array/` identifies ParticleArray as a mutable host particle table with an optional device mirror. Warp `1.14.0` imports in the active environment. + +ADR-0002 proposes starting with a Warp mirror rather than replacing host storage. + +This plan is outside the original `.pxd/.pyx` boundary because a practical mirror prototype likely touches Python helper/test files. No code should begin until this boundary expansion is approved. + +## Approach + +1. Confirm the desired backend name: tentatively `backend='warp'`. +2. Add a minimal Warp helper behind the existing ParticleArray device-helper boundary. +3. Implement device array creation plus selective/full `push()` and `pull()` for scalar-stride properties and constants. +4. Add Local-first `align_particles()` for `tag` and scalar properties. +5. Extend alignment to strided properties. +6. Add focused tests mirroring existing DeviceHelper push/pull and alignment cases. +7. Initial implementation proved mirror and alignment behavior; user then approved continuing. +8. Add Warp-backed remove, remove-tagged, add, append, extend, and extract behavior through the ParticleArray public methods. +9. Expand tests across the full prototype surface and add tutorial-style documentation. + +## Files expected to change + +- `pysph/base/particle_array.pyx` +- `pysph/base/particle_array.pxd` if the backend hook needs declaration changes +- `pysph/base/device_helper.py` or a new `pysph/base/warp_device_helper.py` +- `pysph/base/tests/test_device_helper.py` or a new Warp-focused test module +- `.ai/implementations/blast-from-the-past/**` +- `docs/source/tutorial/warp_particle_array.rst` +- `docs/source/index.rst` + +## Tests / validation + +- `source $HOME/prediqt/activate` only if no environment is already active. +- `python -c "import warp; print(warp.__version__)"` +- Focused Warp helper tests for creation, push, pull, scalar alignment, and strided alignment. +- Expanded Warp helper tests for dtype policy, full sync, readback modes, remove/remove-tagged, add/default fill, append missing properties/constants, empty clone, extract, resize/extend, max, and errors. +- Existing CPU ParticleArray tests to confirm host behavior is unchanged. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai AGENTS.md pysph/base` + +## Risks + +- Warp dynamic resizing may not match Compyle `Array` semantics directly. +- Implicit host readback behavior can hide stale device data unless synchronization points are explicit. +- Strided alignment is easy to get semantically wrong. +- Adding `backend='warp'` may require changes in backend resolution outside ParticleArray. + +## Out of scope + +- Migrating NNPS. +- Migrating SPH equations or integrators. +- Replacing host `BaseArray` ownership. +- Replacing host `BaseArray` ownership. +- Making performance claims beyond functional smoke checks. + +## Estimated effort + +M - small enough for a first prototype, but touches backend selection and test plumbing. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-15T08:03:01 CEST +- Approval, verbatim quote: + > alright. commit and continue + +Commit note: the commit part of that message was superseded by "actually hold off on the commit"; the implementation continuation remains approved. + +Scope continuation: + +- Approved by: @kunalpuri-prediqt at 2026-06-15T11:57:32 CEST +- Approval, verbatim quote: + > continue + +Documentation/test continuation: + +- Approved by: @kunalpuri-prediqt at 2026-06-15T12:12:52 CEST +- Approval, verbatim quote: + > can you write the tests covering all aspects and also a tutorial style document explaning how to use the new warp particle array class diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md new file mode 100644 index 000000000..b581cb21d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md @@ -0,0 +1,107 @@ +--- +type: plan +id: 2026-06-16_warp-artificial-viscosity-momentum-term +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T13:30:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +within_boundary: false +--- + +# Plan: Warp artificial viscosity momentum term + +## Goal + +Add Monaghan-style artificial viscosity to the current Warp WCSPH momentum path +and expose it through the elliptical-drop runner so the next GPU runs have the +first missing stabilizing term. + +## Context + +The committed Warp elliptical-drop runner advances finite prototype states but +still uses only inviscid pressure-gradient acceleration. PySPH's +`pysph.sph.wc.basic.MomentumEquation` combines pressure and artificial +viscosity: + +```text +a_i = -sum_j m_j * (p_i/rho_i^2 + p_j/rho_j^2 + Pi_ij) * grad(W_ij) +``` + +where `Pi_ij` is nonzero only for approaching pairs: + +```text +v_ij dot x_ij < 0 +mu_ij = HIJ * (v_ij dot x_ij) / (R2IJ + EPS) +Pi_ij = (-alpha*c_ij*mu_ij + beta*mu_ij^2) * RHOIJ1 +c_ij = 0.5*(c_i + c_j) +RHOIJ1 = 1/rho_ij = 2/(rho_i + rho_j) +``` + +The current Warp prototype does not yet carry `cs`; this slice can use constant +`c0` for both particles as a first WCSPH-equivalent approximation matching the +elliptical-drop configuration's constant reference speed of sound. + +## Approach + +1. Extend the Warp pressure-gradient kernel path to optionally include + artificial viscosity with parameters `alpha`, `beta`, `c0`, and `eps`. +2. Keep the existing inviscid default behavior unchanged by defaulting + `alpha=0.0`, `beta=0.0`. +3. Add a convenience function or parameterized path so + `wc_sph_leapfrog_step()` can request the viscous momentum term. +4. Add CPU-reference tests for the artificial-viscosity acceleration in a small + fixture with both approaching and separating particle pairs. +5. Add CLI options to the Warp elliptical-drop runner: `--alpha` and `--beta`, + then run at least the existing smoke case with `alpha=0.1`, `beta=0.0`. +6. Update experiment docs and memory with the new result. + +## Files expected to change + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- current/aspect/daily/session memory updates + +Boundary note: these files are part of the approved Python Warp prototype +surface, but the memory validator treats the `warp_*.py` boundary entry +literally rather than as a glob. Mark this plan as `within_boundary: false` and +call it out again in review. + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past` + +## Risks + +- Constant `c0` is an approximation until a `cs` property/Tait EOS path is + added. +- The current CubicSpline kernel still differs from PySPH elliptical-drop's + Gaussian kernel. +- This is still not a full validation against the analytical elliptical-drop + result. + +## Out of scope + +- Tait EOS and per-particle sound speed. +- XSPH correction. +- Gaussian kernel support. +- PySPH `Application`/`Solver` integration. +- PR creation. + +## Estimated effort + +One focused implementation session. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T13:35:58 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md new file mode 100644 index 000000000..8991a2814 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md @@ -0,0 +1,168 @@ +--- +type: plan +id: 2026-06-16_warp-continuity-density-leapfrog-parity +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T23:58:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp Continuity-Density Leapfrog Parity + +## Goal + +Make the resolved elliptical-drop comparison apples to apples by adding a Warp +step path that evolves density with `ContinuityEquation` and the WCSPH +integrator density stages, matching the default PySPH no-scheme CPU +Application path. + +The existing resolved `nx=100` output is diagnostic only: it showed Warp is +faster per step, but it also exposed that the Warp runner was recomputing +density with summation density while PySPH evolves `rho` through `arho`. + +## Baseline to match + +PySPH `pysph/examples/elliptical_drop_no_scheme.py` uses: + +- `TaitEOS` in a non-real group; +- `ContinuityEquation(dest='fluid', sources=['fluid'])`; +- `MomentumEquation(dest='fluid', sources=['fluid'], alpha=0.1, beta=0.0)`; +- `XSPHCorrection(dest='fluid', sources=['fluid'])`; +- `WCSPHStep`, where `rho` is advanced from saved `rho0` with `arho` in the + integrator stages. + +The Warp parity path should therefore compute `arho` on device and integrate +`rho` on device, rather than replacing `rho` each force evaluation with +summation density. + +## Approach + +### Phase 1 - Device density-stage kernels + +- Add small Warp kernels for WCSPH density integration stages: + - save/restore stage inputs if needed (`rho0` at the beginning of a step); + - update `rho = rho0 + 0.5*dt*arho` for the midpoint stage; + - update `rho = rho0 + dt*arho` for the full stage. +- Keep these kernels device-resident and reuse existing ParticleArray + properties. If temporary storage is required, prefer existing PySPH-style + properties (`rho0`) when present and create only the minimum helper needed + for the experiment path. + +### Phase 2 - Selectable WCSPH force density mode + +- Extend the internal Warp WCSPH acceleration helper so density preparation is + selectable: + - `density_mode='summation'` keeps existing behavior and tests stable; + - `density_mode='continuity'` assumes current `rho` is authoritative, + computes EOS from it, computes momentum, and computes `arho` with + `compute_continuity()`. +- Preserve no-unnecessary-transfer behavior: + - no full host particle pulls during repeated stepping; + - no full host pushes after the initial setup; + - adaptive timestep continues to pull only the reduced scalar `dt`. + +### Phase 3 - Continuity leapfrog step + +- Add continuity-density support to `wc_sph_leapfrog_step()` through a + conservative keyword, for example `density_mode='summation'`. +- For `density_mode='continuity'`, mirror the PySPH `WCSPHStep` staging as + closely as the current KDK-style helper permits: + - compute force/EOS/continuity at the current state; + - select adaptive `dt`; + - advance velocity and density to the intermediate state; + - drift positions with velocity/XSPH correction; + - rebuild NNPS from device positions; + - recompute force/EOS/continuity; + - advance velocity and density to the full step. +- Keep summation-density behavior as the default until the continuity path is + validated. + +### Phase 4 - Tests + +- Add focused tests proving the new density-stage kernels match CPU reference + arithmetic. +- Add a focused `wc_sph_leapfrog_step(..., density_mode='continuity')` test + against a hand-computed CPU reference using `ContinuityEquation` and the same + Tait/EOS + pressure-gradient chain. +- Preserve the existing summation-density leapfrog test. + +### Phase 5 - Rerun resolved comparison + +- Update the Warp elliptical-drop runner and resolved comparison script to use + `density_mode='continuity'` for the PySPH Application comparison. +- Rerun the default `nx=100` resolved comparison at: + - `t = 0.0008`; + - `t = 0.0038`. +- Update the experiment report to clearly separate: + - the previous summation-density diagnostic run; + - the new continuity-density parity run. + +## Files expected to change + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- New/updated resolved comparison outputs under the same experiment directory. +- Memory/context: + - `.ai/implementations/blast-from-the-past/current.md` + - `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` + - `.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md` + - `.ai/implementations/blast-from-the-past/aspects/particle-memory/context.md` + - `.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md` + - session log for this slice + +No boundary amendment is expected because `pysph/base/warp_sph.py` and +`pysph/base/tests/test_warp_sph.py` are already inside the approved prototype +boundary. + +## Tests / validation + +- Focused test suite: + `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- Resolved comparison script: + `python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-continuity --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000` +- Confirm CPU and Warp both report finite metrics at each checkpoint. +- Confirm side-by-side images exist and include exact ellipse overlays. +- Confirm the continuity run no longer uses summation density during repeated + Warp stepping. +- Confirm no full-array host/device transfers occur inside the repeated Warp + stepping loop beyond the scalar adaptive timestep handoff. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai/implementations/blast-from-the-past pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py` + +## Success criteria + +- Existing summation-density leapfrog behavior remains tested and unchanged by + default. +- New continuity-density leapfrog behavior passes focused CPU-reference tests. +- The resolved `nx=100` comparison runs through `t=0.0008` and `t=0.0038` + using `density_mode='continuity'` on Warp. +- The reported CPU/Warp step counts, timestep ranges, shape metrics, density + ranges, kinetic energy, and wall times are updated from the continuity run. +- Any remaining difference is described as a numerical/integrator parity issue, + not hidden behind a formulation mismatch. + +## Risks + +- The current Warp helper is KDK-shaped while PySPH `WCSPHStep` is predictor + corrector/PEC-style. Matching density evolution may expose a remaining + integrator-stage mismatch even after replacing summation density. +- If `rho0` storage is absent from the ad-hoc Warp ParticleArray, the runner + may need to add the property during setup. +- Adaptive timestep behavior may still differ if the force estimate uses a + different acceleration stage than PySPH's solver. +- The resolved `nx=100` rerun may take several minutes again. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T23:59:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-elliptical-drop-application-runner.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-elliptical-drop-application-runner.md new file mode 100644 index 000000000..63356838e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-elliptical-drop-application-runner.md @@ -0,0 +1,108 @@ +--- +type: plan +id: 2026-06-16_warp-elliptical-drop-application-runner +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T12:45:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [] +within_boundary: true +--- + +# Plan: Warp elliptical drop application runner + +## Goal + +Add a runnable Warp elliptical-drop-style application experiment that creates +the standard elliptical-drop initial particle patch, advances it with the +current Warp NNPS + WCSPH leapfrog prototype, and records enough metrics to +decide the next physics/integration work. + +## Context + +The existing PySPH `pysph.examples.elliptical_drop` application uses the normal +PySPH solver path with `WCSPHScheme`, Gaussian kernel, EPEC integrator, Tait +EOS, artificial viscosity, and XSPH correction. The current Warp prototype does +not yet implement that full formulation. It has: + +- `UniformGridWarpNNPS`; +- CubicSpline summation density; +- isothermal EOS; +- inviscid pressure-gradient acceleration; +- KDK leapfrog; +- device-side periodic position wrapping, but not periodic neighbor distances. + +So this plan creates a first GPU runner/smoke application, not a validated +drop-physics result. The runner should make the gap explicit and produce data +that guides the next equation work. + +## Approach + +1. Create a new experiment packet under + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/`. +2. Add a Python runner script with an application-style class, for example + `WarpEllipticalDropRunner`, that: + - accepts `--nx`, `--steps`, `--dt`, `--rho0`, `--c0`, `--p0`, and output + path options; + - creates the elliptical-drop initial circular patch using the same geometry + and initial velocity field as the PySPH example; + - initializes required Warp properties (`rho`, `p`, `au`, `av`, `aw`); + - advances with `UniformGridWarpNNPS` and `wc_sph_leapfrog_step`; + - pulls final arrays once at the end; + - writes a compact `.npz` result with final state and scalar metrics such as + particle count, final time, min/max coordinate bounds, kinetic energy, and + finite-value checks. +3. Add `run_correctness.sh` for a small smoke run, likely `nx=8` or `nx=10` + with a tiny number of steps, to keep it stable and quick. +4. Add experiment documentation with: + - what to expect; + - success criteria; + - explicit note that this is not the full PySPH elliptical-drop physics yet; + - next missing equations for a faithful run. +5. Run the wrapper and record raw output. + +## Files expected to change + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py` +- `.ai/implementations/blast-from-the-past/current.md` +- relevant aspect/session/daily memory updates + +No host package file is expected to change in this first runner slice. + +## Tests / validation + +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai/implementations/blast-from-the-past` + +## Risks + +- The current Warp formulation is incomplete for the published elliptical-drop + benchmark, so success criteria must focus on runnable GPU state evolution, + finite values, and output generation. +- The pressure-gradient-only dynamics may be physically rough or unstable for + aggressive `nx`, `dt`, or step counts. +- This does not yet exercise PySPH's `Application`/`Solver` plumbing directly; + it is an application-style experiment runner around the Warp prototype. + +## Out of scope + +- Full PySPH `Application` CLI integration. +- Matching the analytical elliptical-drop locus. +- Tait EOS, artificial viscosity, XSPH, Gaussian kernel support, or adaptive + timestep. +- PR creation. + +## Estimated effort + +One focused implementation session. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T12:30:36 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md new file mode 100644 index 000000000..b577a3c72 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md @@ -0,0 +1,103 @@ +--- +type: plan +id: 2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T11:58:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_nnps.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_nnps.py, pysph/base/tests/test_warp_sph.py] +within_boundary: false +--- + +# Plan: Warp repeated step leapfrog and periodic refresh + +## Goal + +Add the first repeated-step Warp dynamics path: + +- rebuild `UniformGridWarpNNPS` from device-updated particle coordinates; +- add a minimal leapfrog/KDK-style WCSPH step on the device; +- add periodic position wrapping for drifted coordinates; +- keep host/device transfers out of the inner step except the existing small + neighbor-length sizing readback; +- validate with focused correctness tests against CPU reference calculations. + +## Context + +The current one-step `wc_sph_euler_step()` computes density, pressure, +pressure-gradient acceleration, and velocity/position update on the GPU. It is +not safe for repeated GPU stepping yet because `UniformGridWarpNNPS.update()` +unconditionally pushes host `x/y/z/h` back to the device before rebuilding. +That clobbers device-updated positions from the previous step. + +The existing equation helpers already expose `push=False` for downstream +stages; the repeated loop should make the device arrays authoritative after the +initial push. + +## Approach + +1. Extend `UniformGridWarpNNPS.update()` with a narrow option such as + `push=True`, preserving current behavior for host-side mutation tests while + allowing repeated GPU loops to call `update(push=False)`. +2. Add Warp kernels/helpers in `pysph/base/warp_sph.py` for: + - leapfrog half-kick; + - drift; + - optional periodic wrap over provided bounds; + - a convenience `wc_sph_leapfrog_step(...)` that evaluates acceleration, + half-kicks, drifts/wraps, refreshes NNPS from device data, reevaluates + acceleration, and completes the half-kick. +3. Keep equation helpers from pushing stale host data in the repeated loop. + Use existing `push=False` where available and add it only where needed. +4. Add tests: + - `UniformGridWarpNNPS.update(push=False)` sees device-side position changes + while default `update()` still supports host mutation; + - periodic wrap keeps coordinates inside the supplied domain; + - leapfrog step matches a CPU fixture for one small non-periodic step; + - repeated stepping does not regress to stale host coordinates. + +## Files expected to change + +- `pysph/base/warp_nnps.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_nnps.py` +- `pysph/base/tests/test_warp_sph.py` +- Possibly the active WCSPH experiment doc if validation results or scope need + to be recorded. + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- pysph/base/warp_nnps.py pysph/base/warp_sph.py pysph/base/tests/test_warp_nnps.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past` + +## Risks + +- This plan touches Python host files outside the current integration boundary. + The review should either amend the boundary or explicitly flag the drift. +- Periodic wrapping positions is only part of periodic boundary behavior. + Correct neighbor interactions across periodic seams need minimum-image + distance and periodic cell lookup; this plan adds the wrap step and tests it, + but only adds full periodic neighbor-distance handling if it remains small + and localized. +- Leapfrog with summation-density WCSPH is a prototype integrator, not yet a + full PySPH scheme/integrator replacement. + +## Out of scope + +- Public Cython ABI/API changes. +- Application/CLI integration. +- Full equation-codegen integration. +- Artificial viscosity, energy equation, XSPH, or adaptive timestep logic. +- Removing all remaining NNPS host readback for neighbor-array sizing. + +## Estimated effort + +One focused implementation session plus validation. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T12:08:27 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md new file mode 100644 index 000000000..6e9d09fa9 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md @@ -0,0 +1,161 @@ +--- +type: plan +id: 2026-06-16_warp-resolved-elliptical-drop-performance-comparison +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T23:09:00 CEST +status: approved +aspects: [validation-benchmarks, warp-backend, gpu-nnps, particle-memory, host-integration] +host_files: [] +within_boundary: true +--- + +# Plan: Resolved Elliptical-Drop Performance and Results Comparison + +## Goal + +Run a resolved elliptical-drop case and compare Warp GPU results/performance +against PySPH CPU results at meaningful checkpoint times. + +The target is not another tiny smoke. The target is a production-oriented +validation artifact with: + +- matched initial condition and formulation; +- CPU PySPH baseline output; +- Warp GPU output; +- timing/per-step performance metrics; +- result metrics and side-by-side images. + +## Baseline case + +Use PySPH's elliptical-drop formulation as the reference: + +- `Gaussian(dim=2)`; +- `rho0 = 1.0`; +- `c0 = 1400.0`; +- `gamma = 7.0`; +- `hdx = 1.3`; +- `alpha = 0.1`; +- `beta = 0.0`; +- XSPH correction enabled; +- adaptive timestep enabled with `cfl = 0.3`; +- canonical output times: `t = 0.0008` and `t = 0.0038`. + +Resolution target: + +- Default resolved case: `nx=100`. +- Use smaller cases only as debugging fallbacks if `nx=100` exposes a blocking + runtime, stability, or output issue. +- Record any smaller fallback honestly as a fallback, not as the resolved + benchmark. + +## Approach + +### Phase 1 - Baseline runner + +- Add a resolved comparison runner/script under: + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/`. +- Run PySPH CPU baseline using the existing no-scheme/Application path where + practical so the output matches PySPH solver behavior. +- If full Application output is too slow for the first pass, run the existing + CPU PySPH-primitive baseline and clearly label it as a fallback. + +### Phase 2 - Warp resolved run + +- Run the Warp elliptical-drop runner with: + - Gaussian kernel / radius scale 3.0; + - Tait EOS / per-particle `cs`; + - artificial viscosity; + - XSPH; + - adaptive dt. +- Capture checkpoint arrays at the same target times as the CPU baseline. +- Keep repeated-step arrays device-authoritative; pull full particle arrays + only at checkpoint/output times. + +### Phase 3 - Performance metrics + +- Record wall-clock runtime for CPU and Warp runs. +- Record: + - total steps; + - final simulated time; + - min/max/mean dt; + - particle count; + - average step time; + - checkpoint output time overhead if separately measurable. +- Include hardware/runtime summary from the active machine. + +### Phase 4 - Result metrics and images + +- Generate side-by-side images at each checkpoint: + - CPU PySPH; + - Warp GPU; + - optional exact ellipse overlay from `pysph.examples.elliptical_drop.exact_solution`. +- Compute and record: + - `x_min/x_max`, `y_min/y_max`; + - major/minor axis estimates; + - density min/max; + - kinetic energy; + - radius/shape summary; + - particle count and all-finite checks. +- Summarize CPU-vs-Warp deltas for the above metrics. + +## Files expected to change + +- New resolved comparison script(s) under + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/`. +- New output images and small summary artifacts under the same experiment. +- The experiment markdown: + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- Memory/context: + - `.ai/implementations/blast-from-the-past/current.md` + - `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` + - `.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md` + - session log for this slice + +No core host code changes are expected. If the resolved run exposes a core Warp +bug or missing capability, stop and write a new implementation plan before +changing `pysph/base/*`. + +## Tests / validation + +- Existing focused suite: + `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- Resolved comparison script exits 0. +- CPU and Warp outputs exist at the target checkpoint times actually reached. +- Side-by-side images exist and are non-empty. +- Summary metrics report `all_finite == true` for CPU and Warp. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai/implementations/blast-from-the-past` + +## Success criteria + +Minimum success: + +- The default resolved case at `nx=100` runs CPU and Warp through at least + `t = 0.0008`, unless a blocking issue is documented with a smaller fallback. +- CPU/Warp side-by-side image and metrics are recorded. +- Runtime and average step time are recorded for both. + +Stretch success: + +- `nx=100` reaches both `t = 0.0008` and `t = 0.0038`. +- Exact ellipse overlay and major/minor-axis deltas are recorded. +- Performance summary includes speedup and transfer/checkpoint overhead notes. + +## Risks + +- Full PySPH Application baseline may be slow at `nx=100`; the plan allows a + smaller fallback only to diagnose blockers. +- `c0=1400` can make timestep very small. Runtime may dominate before physics + issues appear. +- The current Warp path still has position wrapping only for periodic support, + but elliptical drop does not require periodic boundaries. +- Any mismatch between the CPU PySPH Application integrator and the standalone + Warp runner must be documented rather than hidden. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T23:14:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md new file mode 100644 index 000000000..6c072aa63 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md @@ -0,0 +1,131 @@ +--- +type: plan +id: 2026-06-16_warp-tait-eos-and-sound-speed +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T14:05:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +within_boundary: false +--- + +# Plan: Warp Tait EOS and Per-Particle Sound Speed + +## Goal + +Add the PySPH WCSPH Tait equation of state to the Warp prototype, compute +per-particle sound speed `cs`, and use pair-averaged `cs` in the artificial +viscosity term. This removes the constant-`c0` approximation from the current +Warp momentum path. + +## Context + +PySPH's `pysph.sph.wc.basic.TaitEOS` computes: + +```text +ratio = rho / rho0 +p = p0 + (rho0*c0*c0/gamma) * (ratio**gamma - 1) +cs = c0 * ratio**(0.5*(gamma - 1)) +``` + +PySPH's WCSPH `MomentumEquation` uses: + +```text +cij = 0.5 * (d_cs[d_idx] + s_cs[s_idx]) +Pi_ij = (-alpha*cij*mu_ij + beta*mu_ij^2) * RHOIJ1 +``` + +The committed Warp artificial-viscosity slice currently uses constant `c0` for +`cij`. Tait EOS plus `cs` should make that term match PySPH more closely. + +## Production Elliptical-Drop Readiness + +This slice will not make the Warp elliptical-drop runner production-ready by +itself. It should upgrade the physics smoke run, but production/published +elliptical drop still needs: + +- Gaussian kernel support or an accepted kernel-equivalence decision. +- XSPH correction. +- Adaptive timestep and CFL/dt tracking. +- Baseline comparison against PySPH's elliptical-drop outputs at the named + times. +- Clear correctness/timing thresholds for accepting the run as a benchmark. +- Application/Solver integration or an explicit decision that the standalone + runner is sufficient for the first benchmark. + +## Approach + +1. Add Warp float32/float64 Tait EOS kernels that write both `p` and `cs`. +2. Add `compute_tait_eos(pa, rho0, c0, gamma=7.0, p0=0.0, ...)`. +3. Keep `compute_isothermal_eos()` intact so earlier tests and comparisons + remain valid. +4. Update artificial-viscosity kernels to accept source/destination `cs` arrays + and use `cij = 0.5*(d_cs + s_cs)` instead of constant `c0`. +5. Update `_compute_wcsph_acceleration()`, `wc_sph_leapfrog_step()`, and + `wc_sph_euler_step()` with an EOS mode or boolean that defaults to current + isothermal behavior, while allowing `eos='tait'`. +6. Update the elliptical-drop runner to default to Tait EOS with `gamma=7.0` + and ensure the particle array carries `cs`. +7. Add tests: + - direct Tait EOS pressure and `cs` against CPU reference; + - artificial viscosity using pair-averaged `cs`; + - a small WCSPH step with Tait EOS where final `p`, `cs`, and acceleration + match CPU reference. +8. Run the focused Warp SPH/NNPS suite and the elliptical-drop smoke wrapper. +9. Update experiment and memory files with the new results. + +## Files expected to change + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- `.ai/implementations/blast-from-the-past/current.md` +- `.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` +- `.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md` +- session log for this slice + +## Boundary note + +These files are part of the approved Python Warp prototype surface, but the +memory validator treats `pysph/base/warp_*.py` literally rather than as a glob. +This plan is marked `within_boundary: false` and should call that out again in +review. + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- Optional small ramp: `nx=16`, `steps=5`, `dt=1.0e-5`, Tait EOS, `alpha=0.1`. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past` + +## Risks + +- Negative or very low density ratios can make fractional-power `cs` invalid; + the focused tests and smoke should watch for non-finite values. +- Switching the runner default from isothermal to Tait changes smoke metrics. +- Momentum correctness will still be incomplete until XSPH and Gaussian kernel + support land. + +## Out of scope + +- XSPH correction. +- Gaussian kernel support. +- Adaptive timestep integration. +- Production benchmark acceptance thresholds. +- PR creation. + +## Estimated effort + +One focused implementation session after approval. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T13:57:05 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md new file mode 100644 index 000000000..277847e95 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md @@ -0,0 +1,175 @@ +--- +type: plan +id: 2026-06-16_warp-xsph-gaussian-adaptive-baseline +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-16T17:35:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +within_boundary: false +--- + +# Plan: Warp XSPH, Gaussian Kernel, Adaptive DT, and PySPH Comparison + +## Goal + +Move the Warp elliptical-drop runner from a finite smoke workload toward an +apples-to-apples validation case by adding: + +1. optional XSPH correction on the GPU; +2. Gaussian kernel support on the GPU; +3. adaptive timestep metrics/reduction on the GPU; +4. CPU PySPH baseline comparison with side-by-side images. + +The runner must avoid unnecessary host/device transfers: no full particle-array +pulls during stepping. Transfers are limited to necessary scalar adaptive +timestep values and explicit checkpoint/output arrays for validation and plots. + +## Context + +Current committed state has: + +- `UniformGridWarpNNPS.update(push=False)` for device-authoritative refresh. +- KDK leapfrog drift/kick on Warp. +- CubicSpline density, pressure-gradient, artificial viscosity, Tait EOS, and + per-particle `cs`. +- A standalone Warp elliptical-drop runner that writes metrics and `.npz` + outputs after a run. + +PySPH references: + +- `pysph.sph.basic_equations.XSPHCorrectionForLeapFrog` computes only the XSPH + correction term: + +```text +ax_i = -eps * sum_j m_j * (u_i - u_j) * W_ij * 2/(rho_i + rho_j) +``` + +- `LeapFrogStep` uses the correction during position updates as + `x += dt * (u + ax)`. +- `pysph.base.kernels.Gaussian` has radius scale `3.0` and: + +```text +W(q) = sigma_g * exp(-q*q), q < 3 +dW/dq = -2*q*exp(-q*q)*sigma_g, q < 3 +``` + +- WCSPH adaptive-timestep terms include `dt_cfl`, `dt_force`, and integrator + reductions over those per-particle fields. + +## Approach + +### Phase 1 - XSPH on GPU + +- Add Warp kernels for XSPH correction writing `ax`, `ay`, `az`. +- Add `compute_xsph_correction(nnps, eps=0.5, ...)`. +- Add a drift variant that uses `u + ax`, `v + ay`, `w + az`. +- Wire optional `xsph_eps=None` or `0.0` into `wc_sph_leapfrog_step()`. +- For KDK, compute XSPH after the first half-kick and before drift, so the + correction uses half-step velocities. +- Add focused CPU-reference tests. + +### Phase 2 - Gaussian Kernel on GPU + +- Add Gaussian `W` and `dW/dq` Warp functions for float32/float64. +- Make equation kernels select `kernel='cubic'` or `kernel='gaussian'`. +- Ensure the runner and NNPS use `radius_scale=3.0` for Gaussian. +- Add focused tests comparing Gaussian density and pressure-gradient values + against PySPH `Gaussian(dim=2)`. +- Switch the elliptical-drop runner default to Gaussian. + +### Phase 3 - Adaptive DT on GPU + +- Add per-particle device fields as needed: `dt_cfl`, `dt_force`, and any + minimal temporary fields. +- Update momentum/adaptive kernels to compute: + - CFL factor comparable to PySPH WCSPH momentum; + - force factor from acceleration magnitude; + - min/selected timestep via GPU reductions. +- In the Python runner, pull only the final scalar timestep per step. This is a + necessary transfer while stepping is orchestrated from Python and launch + parameters are host scalars. +- Add `--adaptive-dt`, `--cfl`, `--n-damp`, `--dt-min`, and `--dt-max` style + runner controls, following PySPH semantics where practical. +- Add tests for device-computed timestep factors and a short adaptive smoke. + +### Phase 4 - CPU PySPH baseline and side-by-side images + +- Add an experiment comparison script under the elliptical-drop experiment + directory. +- Run a CPU PySPH baseline with the matched formulation: + Gaussian + Tait EOS + artificial viscosity + optional XSPH + adaptive dt. +- Capture checkpoints at PySPH example output times where practical + (`0.0008`, `0.0038`) or document a shorter smoke comparison if runtime is too + high. +- Generate side-by-side images from CPU and Warp checkpoints, likely scatter + plots colored by speed or density plus optional outline/axis-equal panels. +- Record metrics: particle count, time, dt history summary, bounds, density + range, kinetic energy, and image paths. + +## Files expected to change + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `pysph/base/warp_nnps.py` if Gaussian radius-scale handling needs NNPS + support beyond runner configuration. +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- New comparison/plot scripts under + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- `.ai/implementations/blast-from-the-past/current.md` +- `.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` +- `.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md` +- session log for this slice + +## Boundary note + +These files are part of the approved Python Warp prototype surface, but the +memory validator treats `pysph/base/warp_*.py` literally rather than as a glob. +This plan is marked `within_boundary: false` and should call that out again in +review. + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh` +- Short adaptive Gaussian+XSPH GPU smoke with no full particle pulls inside the + step loop. +- CPU PySPH baseline smoke/comparison script. +- Side-by-side image generation check. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past` + +## Risks / sequencing + +- This is a larger slice than the prior equation kernels. If it gets too large, + stop after a defined checkpoint in this order: XSPH, Gaussian, adaptive dt, + baseline/images. +- Adaptive timestep may require one scalar host transfer per step; that is + necessary with the current Python launch loop and should be documented. +- Full PySPH baseline at example output times may be slower than the smoke + budget. If so, run a smaller/narrower baseline first and record it honestly. +- Gaussian radius scale changes neighbor counts, so the runner must construct + `UniformGridWarpNNPS(radius_scale=3.0)` for Gaussian. + +## Production-readiness expectation + +Completing this plan should make the runner much closer to a production +elliptical-drop validation path. It still may not be production-ready until the +side-by-side baseline comparison has accepted tolerances and the team decides +whether standalone runner output is sufficient or PySPH `Application/Solver` +integration is required. + +## Estimated effort + +One long implementation session, likely with checkpointing after each phase. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-16T22:27:35 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md new file mode 100644 index 000000000..67dfbdd68 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md @@ -0,0 +1,148 @@ +--- +type: plan +id: 2026-06-17_warp-adaptive-timestep-policy-parity +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-17T08:24:00 CEST +status: approved +aspects: [warp-backend, particle-memory, validation-benchmarks, host-integration] +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp Adaptive Timestep Policy Parity + +## Goal + +Match PySPH's adaptive timestep policy closely enough that the resolved +elliptical-drop CPU/GPU step counts are comparable under the same timestep +controller. + +The continuity-density comparison already fixed the physics/formulation +mismatch. The remaining step-count difference is now mostly policy: + +- PySPH CPU: 1393 steps, `dt_mean ~= 2.740e-6`, `dt_max ~= 2.781e-6`; +- Warp GPU: 1804 steps, `dt_mean ~= 2.106e-6`, `dt_max ~= 2.109e-6`. + +The Warp resolved runner currently caps `dt_max` at the initial timestep, while +PySPH's adaptive solver allows the timestep to grow after applying the +`n_damp` ramp. + +## PySPH behavior to mirror + +From `pysph/solver/solver.py` and `pysph/sph/integrator.py`: + +- The integrator computes a local adaptive timestep as + `cfl * min(hmin/dt_cfl_fac, sqrt(hmin/sqrt(dt_force_fac)), hmin/dt_visc_fac)`. +- The solver starts from `_get_undamped_timestep()`, where + `_get_undamped_timestep()` is `self.dt / self._damping_factor`. +- For early steps, `n_damp` applies: + `0.5 * (sin(pi * (-0.5 + (count + 1)/n_damp)) + 1.0)`. +- Output-time handling can temporarily shorten `self.dt`, but PySPH preserves + `_prev_dt` and restores it after the output step. Solver output records + undamped `dt`, not necessarily the shortened checkpoint step. + +## Approach + +### Phase 1 - Focused timestep-policy helper + +- Add a small Python-side helper for the experiment runner that mirrors PySPH's + solver timestep policy: + - track `count`; + - track damping factor; + - compute the undamped adaptive candidate from the device-reduced Warp + timestep helper; + - apply the `n_damp` sine ramp; + - cap only for output-time landing as a temporary step, then restore the + previous undamped candidate for the next step. +- Keep the device work unchanged: adaptive factors remain computed/reduced on + device, with only scalar `dt` copied to host. + +### Phase 2 - Warp helper interface + +- If needed, expose a lower-level Warp adaptive timestep candidate that returns + the undamped device-reduced value before runner-level damping/output-time + policy. +- Preserve current `compute_wcsph_adaptive_timestep()` behavior for existing + tests/callers unless a compatibility-preserving option is clearer. +- Avoid full host particle-array pulls/pushes inside the repeated stepping loop. + +### Phase 3 - Runner and resolved comparison + +- Update `resolved_elliptical_drop_comparison.py` to use the PySPH-like policy + when `--warp-density-mode continuity` is used. +- Add CLI flags if useful: + - `--warp-timestep-policy {current,pysph}`; + - default the resolved comparison to `pysph` policy; + - preserve the old behavior for diagnostics. +- Record both the actual shortened step history and PySPH-style restored + reported timestep history if needed to interpret output. + +### Phase 4 - Validation + +- Add focused tests for the damping factor and output-time restore behavior. +- Run the focused Warp suite: + `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- Run a small `nx=8` resolved comparison to check wiring. +- Run the resolved `nx=100` Application comparison through `t=0.0008` and + `t=0.0038`. +- Update the experiment report with: + - CPU/Warp step counts; + - `dt_min`, `dt_mean`, `dt_max`; + - shape/density/energy deltas; + - whether step counts now match or what remaining difference remains. + +## Files expected to change + +- `pysph/base/warp_sph.py` only if a lower-level adaptive candidate helper or + option is needed. +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- New/updated lightweight resolved summary/images under the same experiment. +- Memory/context: + - `.ai/implementations/blast-from-the-past/current.md` + - `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` + - `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md` + - `.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md` + - session log for this slice + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- `python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 8 --output-times 0.0008 --prefix timestep-policy-smoke-nx8 --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/timestep-policy-smoke --max-steps 100000` +- `python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-timestep-policy --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000` +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- .ai/implementations/blast-from-the-past pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py` + +## Success criteria + +- Warp no longer hard-caps the resolved run to the initial timestep when using + the PySPH-like policy. +- `n_damp=50` changes the early Warp timestep history in the same way PySPH + does. +- Output-time landing does not permanently shrink the next Warp timestep. +- The resolved `nx=100` comparison remains finite and visually/metric-wise + close to PySPH CPU. +- Step-count differences are either eliminated or reduced and explained with a + concrete remaining policy/integrator reason. + +## Risks + +- PySPH records/restores timestep values around output differently from the + raw physical step actually taken, so we may need to store both histories to + explain comparisons honestly. +- Exact step-count parity may still differ if PySPH's acceleration evaluation + timing updates `dt_cfl`/`dt_force` at a subtly different point in the PEC + cycle. +- The full `nx=100` Application rerun takes several minutes. + +## Approval + +- [ ] Plan posted in chat +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-17T08:25:00 CEST +- Approval, verbatim quote: + > approved diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md new file mode 100644 index 000000000..b73b25e24 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md @@ -0,0 +1,210 @@ +--- +type: plan +id: 2026-06-17_warp-fuse-neighbor-loop-equations +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-17T17:40:00 CEST +revised: 2026-06-17T18:05:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, validation-benchmarks] +adr: ADR-0003 +host_files: + - pysph/base/warp_codegen.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_codegen.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp Fuse Neighbor-Loop Equations Via Dynamic Code Generation + +## Goal + +Build a minimal, composable Warp equation-group **code generator** (ADR-0003) +and make the continuity-density PEC half-stage its first consumer: continuity + +pressure gradient + artificial viscosity + XSPH composed into a single, +dynamically generated, JIT-compiled kernel that traverses the shared neighbor +cache exactly once. Then redo the million-particle headline benchmark. + +This realizes PySPH's composition model on the GPU (fusion as a property of +grouping) instead of hand-writing a one-off fused kernel, and it removes the +`f32`/`f64` duplication for the fused path. + +Current state after the cache-reuse slice (`nx=565`, 1,002,885 particles, fixed +10 steps): PySPH CPU 57.48 s; Warp 4.51 s; speedup 12.7450110864745x; segmented +step wall 0.098335–0.138290 s with cache time 0.036122–0.052126 s, so the four +separate equation traversals are roughly half of each step's wall time. + +## Context + +Feasibility is already proven (recorded in ADR-0003): a runtime-assembled kernel +body, materialized via `linecache` + `exec` and wrapped with +`wp.Kernel(func=..., source=...)`, JIT-compiled in ~1.5 s and ran correctly on +the RTX 4060. + +In `_wc_sph_pec_continuity_step`, each PEC half-stage currently runs four +separate neighbor-loop launches against one shared `stage_cache` +(`compute_pressure_gradient`, `compute_artificial_viscosity`, +`compute_continuity`, `compute_xsph_correction`), each a launch + device sync, +each re-reading neighbor indices and recomputing the same per-pair geometry. The +Tait/Isothermal EOS feeding pressure/viscosity is a cheap per-particle kernel +that must run before the fused kernel (it reads `p`, `cs`). + +## Approach + +### 1. Code generator (`pysph/base/warp_codegen.py`) + +- `WarpEquation` base class. Each block declares: + - `src_arrays`, `dst_arrays`, `out_arrays` (property names); + - which shared per-pair quantities it needs from + `{dx, dy, dz, rij, hij, grad, wij, vijx, vijy, vijz}`; + - `scalars` it consumes (e.g. `alpha`, `beta`, `eps`); + - source snippets `initialize()`, `loop()`, `post_loop()` as strings using a + small, documented variable vocabulary (`i`, `j`, the shared quantities, the + `s_`/`d_` arrays, and a `TYPE` token replaced with the dtype). +- `build_group_kernel(equations, dtype, dim_static=None)`: + - unions arrays into a stable kernel signature + (`s_*`, `d_*`, `starts`, `lengths`, `neighbors`, `dim`, `kernel_id`, + scalars, then `d_*` outputs); + - emits `i = wp.tid()`, each block's `initialize`, one neighbor loop that + computes only the requested shared quantities **once** + (`grad = _kernel_dwdq_(rij, hij, dim, kernel_id)/(hij*rij)` guarded by + `rij > tiny`; `wij = _kernel_value_(...)`), then each block's `loop`, + then each block's `post_loop`; + - `exec`s the templated source into a namespace seeded with `wp` and the + existing device `wp.func`s (`_kernel_dwdq_f32/f64`, `_kernel_value_f32/f64`), + registers it in `linecache`, and wraps it with `wp.Kernel(func=..., source=)`; + - caches the compiled kernel by structural signature key + `(equation kinds + structural flags, dtype, dim handling)` so each unique + group compiles once. +- Validate up front that a generated kernel can call the existing module-level + `wp.func`s (the spike covered only built-ins). + +### 2. Equation blocks for the four continuity-stage equations + +Port the per-pair math of `_continuity`, `_pressure_gradient`, +`_artificial_viscosity` (Monaghan, pair-averaged `cs`, `vdotx < 0` guard), and +`_xsph_correction` into blocks. Accumulation order is preserved relative to the +hand kernels: pressure gradient and viscosity use distinct accumulators summed +in `post_loop` (`d_au[i] = au_p + au_v`), matching the existing two-kernel +semantics; continuity and XSPH keep their own accumulators. + +### 3. Helper + integration + +- `compute_wcsph_accel_continuity(nnps, pa_index, alpha, beta, eps, kernel, + cache, push)`: ensures arrays/`cs`, builds the group kernel for the present + dtype, does a single `wp.launch` + one `wp.synchronize_device`. +- Factor EOS out into `_apply_wcsph_eos(...)` shared by both paths. +- Refactor `_wc_sph_pec_continuity_step` **only**: per half-stage build the + cache (unchanged), apply EOS, then call the fused helper instead of the four + separate helpers; XSPH now comes from the fused kernel so its separate call is + removed and `use_xsph` is derived from `xsph_eps`. + +### 4. Leave untouched + +`_compute_wcsph_acceleration` (summation path + Euler step), every existing +per-equation helper and its f32/f64 kernels (kept as the trusted oracle and for +the summation path), and the adaptive `compute_wcsph_adaptive_timestep` +(`_wcsph_dt_factors` still runs its own traversal, reading the fused +`au,av,aw`). The adaptive-factor and summation paths migrate to the generator in +a later slice (ADR-0003 follow-up). + +## Files expected to change + +- `pysph/base/warp_codegen.py` (new) +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_codegen.py` (new) +- `pysph/base/tests/test_warp_sph.py` +- `.ai/.../experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- a new compact summary folder + `.ai/.../experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step-fused-eqns/` +- `.ai/.../aspects/warp-backend/context.md`, + `.ai/.../aspects/validation-benchmarks/context.md` +- `.ai/.../current.md`, `.ai/.../updates/daily/2026-06-17.md` +- session log + review artifact before commit +- ADR-0003 flipped to `Accepted` on approval + +Boundary note: `pysph/base/warp_*.py` is already in the implementation +boundary, so `warp_codegen.py` and `test_warp_codegen.py` are within boundary; +the review will record the addition. + +## Tests / validation + +- New `test_warp_codegen.py`: + - a generated trivial kernel compiles, runs, and caches (second build is a + cache hit, not a recompile); + - a generated kernel can call the existing device `wp.func`s; + - a generated single-equation kernel matches the corresponding hand-written + helper on small input. +- `test_warp_sph.py` additions: + - the generated 4-equation group matches the four separate helpers on the same + input (tight `allclose` on `arho, au, av, aw, ax, ay, az`); + - the continuity-density `wc_sph_leapfrog_step()` issues one fused equation + launch per half-stage and does **not** call the four per-equation helpers + (monkeypatch-count); + - the existing two-cache-builds-per-step test still holds; + - the existing `test_warp_wc_sph_leapfrog_continuity_mode_matches_cpu_pec_state` + CPU-parity test still passes (now exercising the generated kernel). +- `python -m pytest -q pysph/base/tests/test_warp_sph.py + pysph/base/tests/test_warp_nnps.py pysph/base/tests/test_warp_codegen.py` +- Segmented one-step `nx=565` profile: one equation-kernel launch per stage; new + equation/step wall time. +- Headline million-particle fixed-step Warp run (`nx=565`, 10 steps, identical + physics to the cache-reuse run); compare to 4.51 s and CPU 57.48 s. +- Warp-only adaptive `nx=100` resolved run; compare metrics to the committed + continuity-density adaptive values (guards the adaptive path against the fp32 + shift in `au,av,aw`). +- `python .ai/.../scripts/validate-memory.py`; `git diff --check`. + +## Success criteria + +- A generated, cached group kernel runs the continuity half-stage as one + neighbor-loop launch instead of four, confirmed by a launch/helper-count test + and the segmented profile. +- `test_warp_codegen.py` passes and the existing focused suite still passes + (currently 41 passed). +- The million-particle fixed-step Warp wall time improves materially from + 4.51 s / 10 steps. +- CPU/GPU final-state deltas stay near floating-point scale (headline deltas may + shift from ~1e-8 toward ~1e-6 from fp32 accumulation reordering; reported). +- The adaptive `nx=100` resolved metrics match committed values to fp32 scale. +- No multi-hour run is launched. + +## Risks + +- Codegen correctness/ergonomics: a malformed snippet yields an opaque Warp + compile error. Mitigated by validating generated kernels against the + hand-written oracle and by keeping the snippet vocabulary small and documented. +- `wp.func` resolution from a generated/exec'd kernel namespace is assumed to + work via the function globals; an explicit early test covers it before the + full build. +- fp32 accumulation reordering shifts `au,av,aw` ~1e-7 vs the separate kernels; + bounded by 1e-5 parity tolerances and the order-preserving accumulators. +- Register pressure from one larger kernel could cut occupancy and offset + traversal savings; the segmented profile and benchmark are the arbiters. +- The adaptive path reads the fused `au,av,aw`; a tiny fp32 shift could perturb + substep counts over a long run. Guarded by re-validating `nx=100` resolved. +- Transitional duplication (blocks + hand kernels for the same four equations); + accepted, with migration tracked as an ADR-0003 follow-up. + +## Out of scope + +- Migrating the per-equation helpers, summation-density, and + adaptive-timestep-factor paths onto the generator (follow-up slice). +- Gradient/adjoint codegen, multi-group/iterated-group orchestration, + cross-array (`src != dst`) fused groups. +- Reducing the neighbor-cache build itself (now the largest per-step cost). +- Full `nx=565`, `tf=0.0076` GPU-only elliptical-drop run. + +## Estimated effort + +One focused implementation/benchmarking session: generator + 4 blocks + tests ++ integration + benchmark (~350–450 LOC across the two host modules and tests). + +## Approval + +- [x] Plan posted in chat (revised to the generator design after the + "Generator now" direction was chosen) +- Approved by: @kunalpuri-prediqt at 2026-06-17T18:10:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md new file mode 100644 index 000000000..ce0f0c41e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md @@ -0,0 +1,137 @@ +--- +type: plan +id: 2026-06-17_warp-reuse-neighbor-cache-per-stage +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-17T16:55:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, validation-benchmarks] +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp Reuse Neighbor Cache Per Stage + +## Goal + +Redo the million-particle headline benchmark after reducing the Warp +continuity-density WCSPH step from eight neighbor-cache builds per step toward +two cache builds per step. + +The current fixed-step `nx=565` benchmark shows: + +- 1,002,885 particles; +- PySPH CPU Application: 57.48 s / 10 steps; +- Warp GPU: 7.17 s / 10 steps; +- speedup: 8.01673640167364x. + +The segmentation run showed the main bottleneck: each full Warp step builds the +same same-array neighbor cache eight times. Each cache contains about 45M +neighbor entries, and cache construction alone is roughly 70% of the GPU step +time. + +## Context + +In the continuity-density PEC path, each half-stage currently calls: + +- pressure gradient; +- artificial viscosity; +- continuity; +- XSPH; + +and each helper calls `nnps.build_neighbor_cache_gpu()` internally. The NNPS +is only updated after the half-stage drift and final stage, so all four +neighbor-loop equations within a stage can safely consume the same cache. + +## Approach + +1. Extend the existing Warp equation helpers with an optional `cache=None` + parameter: + - `compute_summation_density`; + - `compute_continuity`; + - `compute_pressure_gradient`; + - `compute_artificial_viscosity`; + - `compute_xsph_correction`; + - `compute_wcsph_adaptive_timestep`, if useful for the adaptive path. +2. Preserve the public/default behavior: when `cache is None`, helpers build + the cache internally as they do today. +3. In `_wc_sph_pec_continuity_step`, build one cache before each half-stage's + neighbor-loop equations, then pass it into acceleration/continuity/XSPH + helpers for that stage. +4. Keep NNPS rebuilds after coordinate updates exactly where they are now: + after the half-stage and final stage. +5. Add focused tests that monkeypatch/count `build_neighbor_cache_gpu()`: + - direct helper calls still build one cache by default; + - a continuity-density `wc_sph_leapfrog_step()` with viscosity and XSPH + builds two equation caches per step instead of eight. +6. Rerun the bounded million-particle headline benchmark: + - CPU fixed-step Application baseline can reuse the recorded 57.48 s if no + CPU inputs changed, but rerun if needed for a clean same-session number; + - Warp fixed-step `nx=565`, 10 steps, same physics/settings; + - record compute and wall-time numbers. + +## Files expected to change + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/` for a compact new summary JSON +- `.ai/implementations/blast-from-the-past/current.md` +- `.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md` +- session log for this slice +- review artifact before commit + +## Tests / validation + +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` +- Focused cache-count test for the continuity-density step. +- Segmented one-step `nx=565` timing to verify two cache builds per step. +- Fixed-step million-particle benchmark: + +```text +python pysph/examples/elliptical_drop_no_scheme.py --nx 565 --tf 0.000003732778967800475 --timestep 0.0000003732778967800475 --no-adaptive-timestep --n-damp 0 --pfreq 10 --fname million-pysph --directory ... --logfile '' --quiet +python .../warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output ... +``` + +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` +- `git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past` + +## Success criteria + +- The full continuity-density step with artificial viscosity and XSPH builds + two equation neighbor caches per step, not eight. +- Focused tests still pass. +- The million-particle fixed-step Warp timing improves materially from the + previous 7.17 s / 10 steps. +- CPU/GPU final-state deltas remain near the previous small values. +- No full-duration multi-hour run is launched. + +## Risks + +- Passing a stale cache across a coordinate update would be incorrect. The + implementation must only reuse the cache within a stage before drift/stage + coordinate updates. +- Reusing one cache also reuses the flat neighbor allocation for several + kernels; this should be correct, but tests need to cover numerical parity. +- We may reveal a second bottleneck after cache builds are reduced, such as + separate equation-kernel traversals or synchronization after every helper. + +## Out of scope + +- Fusing pgrad, viscosity, continuity, and XSPH into a single kernel. +- Removing all synchronizations. +- Full `nx=565`, `tf=0.0076` GPU-only elliptical-drop run. +- MPI/OpenMP CPU benchmark changes. + +## Estimated effort + +One focused implementation/benchmarking session. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-17T17:13:22 CEST +- Approval, verbatim quote: + > approved diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md new file mode 100644 index 000000000..ff4460a51 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md @@ -0,0 +1,168 @@ +--- +type: plan +id: 2026-06-18_warp-3d-dam-break-lobovsky-benchmark +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-18T20:45:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, validation-benchmarks] +adr: ADR-0005 +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py + - pysph/base/tests/test_warp_codegen.py +within_boundary: true +--- + +# Plan: Warp 3D dam-break Lobovsky benchmark + +## Goal + +Add a 3D dam-break benchmark (PySPH Lobovsky no-obstacle case first) to the Warp +backend, validated against the shipped CPU reference. Implement the four WCSPH +features the 2D elliptical drop never exercised -- gravity, multi-array stepping, +solid walls, and the WendlandQuintic kernel -- as **purely additive** extensions +(ADR-0005) so the 2D elliptical-drop path and its on-disk Warp kernel cache stay +**byte-identical**. + +## Context + +A 5-area investigation confirmed the lower layers are already fully 3D and +3D-validated: `warp_nnps.py` bins on `(ix,iy,iz)` with `cid = ix + iy*nx + +iz*nx*ny` and a 27-cell `range(-1,2)^3` walk (`:249-376`, `:983-997`); +`warp_codegen.py` emits `dx/dy/dz`, `vijx/vijy/vijz`, a 3D triple cell loop and +3D `cid` under `dim` guards (`:222-261`, `:368-405`); `warp_sph.py` carries +`dim==3` kernel normalization and `z`/`w` integration in every step path. +Existing dim=3 parity tests pass (`test_warp_sph.py:361,512,604`; +`test_warp_nnps.py:252,286`). + +Confirmed-absent (grep) and needed by a dam-break: no gravity term anywhere; all +drivers are single-array (`src==dst`, `wc_sph_leapfrog_step`/ +`_wc_sph_pec_continuity_step` at warp_sph.py:1888/1840); no fixed-wall handling +or `TaitEOSHGCorrection`; `_kernel_id` (warp_sph.py:1066) knows only +cubic(0)/gaussian(1) while both CPU references use `WendlandQuintic(dim=3)`. The +2D-ness otherwise lives only in the elliptical-drop runner (`dim=2`, 2D disk IC, +2D radius metric). + +CPU reference: `pysph/examples/dam_break/dam_break_3d_lobovsky.py` (container +~5.367 x 0.5 x 1.5, fluid column 2.0 x 0.5 x 1.0, `dx=H/30`, `hdx=1.3`, +`rho0=1000`, `gamma=7`, `alpha=0.25`, `beta=0`, `gz=-9.81`, `c0~73`, +`WendlandQuintic(dim=3)`, `tf=2.5`, `n_damp=50`); geometry via +`DamBreak3DGeometry` (`pysph/examples/_db_geometry.py:250-432`), scheme +`WCSPHScheme` + `EPECIntegrator`/`WCSPHStep`. + +## Approach + +Sequence: pin 3D tests -> Wendland -> gravity -> multi-array/walls -> runner -> +validate & record. Each backend addition is additive; ids 0/1, existing emitted +source, and single-array drivers are never edited. + +1. **Pin 3D coverage first (no production change).** Add a dim=3 codegen grid + test (a `_KernelSum`-style grid kernel with `nz>1`, real z coords) to + `test_warp_codegen.py`, and dim=3 variants of the fused-accel, grid-direct, + and adaptive-timestep parity tests to `test_warp_sph.py` (the `_cpu_wcsph_dt` + helper already handles dim=3). Locks the "2D byte-identical" invariant as + enforceable before any edit. +2. **WendlandQuintic (`warp_sph.py`).** Add `_wendland_quintic_*` value + dwdq + device funcs with `dim==1/2/3` normalization, mirror the cubic/gaussian + structure (warp_sph.py:16-280), route through `_kernel_value_*`/`_kernel_dwdq_*`, + and extend `_kernel_id` (`:1066`) with a new id for `'wendland'`. Ids 0/1 + unchanged. Add a dim=3 CPU-parity test vs `pysph.base.kernels.WendlandQuintic`. +3. **Gravity (`warp_sph.py`).** Add `_apply_body_force_*` (`u+=gx*dt`; + `v+=gy*dt` if `dim>1`; `w+=gz*dt` if `dim>2`) and a host driver + `apply_body_force(pa, gx, gy, gz, dt, dim, ramp)`. Thread optional + `gx=gy=gz=0` + `n_damp` ramp into the dam-break step (not the elliptical-drop + step). Never fold `g` into generated equation source. Add an exact-arithmetic + gravity test. +4. **Multi-array + solid walls (`warp_sph.py`).** Add a `wc_sph_dam_break_step` + that, per destination array, sums equation-group contributions over a source + list (fluid from `[fluid, wall]`, wall `drho/dt` from `[fluid]`) using the + already-`src!=dst` groups; integrate only fluid positions/velocities (walls + fixed). Add a `_tait_eos_hg_correction_*` device kernel (clamp `p>=0`, + recompute `rho`) for solid arrays. Add a dim=3 two-array integration smoke + test (all_finite over a few steps) and a two-array dim=3 NNPS test. +5. **Runner experiment packet.** New dir + `experiments/2026-06-18_warp-dam-break-3d-runner/` mirroring the + elliptical-drop packet: `dam_break_3d_runner.py` (Runner class; IC via + `DamBreak3DGeometry` or a replica; `UniformGridWarpNNPS(dim=3, [fluid,wall])`; + gravity + adaptive dt + `n_damp`; `_metrics` adds z-extent/max-height/3D KE), + `run_correctness.sh` smoke wrapper, a tier-1 `compare_warp_pysph_dam_break_3d.py` + (hand-rolled CPU equations + `LinkedListNNPS(dim=3)` + Wendland), and a tier-2 + `resolved_dam_break_3d_comparison.py` (subprocess the real PySPH Application, + load via `pysph.solver.utils.load`, step Warp to matched checkpoints). + +## Files expected to change + +- `pysph/base/warp_sph.py` (Wendland, body force, HG correction, multi-array + dam-break step) -- additive only +- `pysph/base/tests/test_warp_sph.py`, `pysph/base/tests/test_warp_codegen.py` + (dim=3 + new-kernel parity tests) +- New `experiments/2026-06-18_warp-dam-break-3d-runner/` (runner, smoke wrapper, + tier-1/tier-2 comparison scripts, experiment.md, per-run summary JSON) +- ADR-0005 (registered), aspect contexts (warp-backend, validation-benchmarks), + current.md, daily, session log, review artifact + +## Tests / validation + +Three tiers, all gating on `all_finite` and a stable step count: + +- **Unit/parity (pytest):** dim=3 codegen 27-cell grid path; dim=3 fused / + grid-direct WCSPH accel; dim=3 adaptive timestep; WendlandQuintic value+dwdq vs + `pysph.base.kernels.WendlandQuintic`; the gravity kernel (exact `g*dt`); the + `TaitEOSHGCorrection` solid kernel; a dim=3 two-array (fluid+wall) integration + smoke. **Regression guard:** assert the cubic/gaussian summation kernel source + string is byte-identical before/after (2D path unchanged). +- **Tier-1 smoke parity:** hand-rolled CPU reimplementation (continuity, Tait + + HG on walls, pressure gradient, Monaghan AV, XSPH, gravity, adaptive dt, PEC + ordering) with `LinkedListNNPS(dim=3)` + Wendland on a small grid, compared + field-by-field to Warp. +- **Tier-2 resolved parity:** subprocess `dam_break_3d_lobovsky.py`, load output, + step Warp to matched checkpoint times; report per-checkpoint CPU-vs-Warp signed + deltas. Observables: per-particle x/u/v/w/rho/p at short horizon; global KE; + surge-front x-position vs time; max height; probe-point pressure + (`p/(rho g H)` vs `t sqrt(g/H)`) vs `db_exp_data.get_lobovsky_data()`. +- `validate-memory.py`; `git diff --check`. + +## Risks + +- **Disk-cache stability (top constraint):** any incidental edit to existing + generated source busts the 2D cache. Mitigated by additive-only design (new id, + separate gravity kernel default `g=0`, separate driver) + the byte-identical + source regression test. +- **Gravity placement / `n_damp` ramp:** must match the CPU + `MomentumEquation`+`WCSPHStep` ordering or the free surface evolves wrong while + passing `all_finite`. Guarded by tier-1 parity at short horizon. +- **Wall fidelity:** `TaitEOSHGCorrection` + fixed-particle handling is the crux + of wall repulsion; a single wall layer can leak if `h/dx`/`radius_scale` differ + from the reference. Match `dx`, `h=hdx*dx`, `m=rho0*dx^3` exactly. +- **EPEC vs PEC:** CPU ref uses EPEC, Warp uses PEC. Either run the CPU ref with + `PECIntegrator` for strict parity or document the difference. To be decided in + step 5. +- **fp32 vs fp64 + chaos:** long-horizon per-particle parity is meaningless; + rely on aggregate/experimental observables for the headline. +- **3D memory/perf:** `ncells = nx*ny*nz` grows cubically; per-update host + readback of x/y/z/h is heavier in 3D. Correctness-only here; profile before any + speed claim. (Not optimizing the 27-cell `dzc` loop -- would change the 2D + path.) + +## Out of scope + +- SPHERIC/Kleefsman obstacle case (third array); a cubic/gaussian dam-break + variant; performance/cross-GPU characterization (separate follow-up). +- Any edit to existing kernel ids 0/1, generated equation source, or the + single-array elliptical-drop step. +- Non-cubic periodic boxes; narrowing the flat host-query path. + +## Estimated effort + +Large overall, unevenly distributed: NNPS/codegen 3D = zero code (additive tests +only); `warp_sph.py` physics features = the bulk of production code (medium-large, +all additive); runner + IC + two-tier harness = medium (reuses scaffolding); +tests = medium. Ship Lobovsky no-obstacle before SPHERIC. + +## Approval + +- [x] Plan posted in chat and approved +- Approved by: @kunalpuri-prediqt at 2026-06-18T20:50:00 CEST +- Approval, verbatim quote: + > approved diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-neighbor-traversal.md b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-neighbor-traversal.md new file mode 100644 index 000000000..a74d7e4bf --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-neighbor-traversal.md @@ -0,0 +1,163 @@ +--- +type: plan +id: 2026-06-18_warp-grid-direct-neighbor-traversal +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-18T10:30:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps, validation-benchmarks] +adr: ADR-0004 +host_files: + - pysph/base/warp_codegen.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_codegen.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Grid-direct neighbor traversal for the WCSPH continuity hot path + +## Goal + +Eliminate flat neighbor-list materialization (`build_neighbor_cache_gpu`: count +traversal -> exclusive scan -> ~4 MB host readback -> `wp.empty(total)` -> +fill traversal) from the continuity-density PEC path (ADR-0004). After ADR-0003 +there is one fused equation launch per half-stage, so the flat list is built +only to be read once. Replace it by having the fused kernel walk the uniform +grid cell list directly. Per half-stage drops from {grid build + 2 build +traversals + readback + alloc + 1 consume traversal} to {grid build + 1 +grid-direct traversal}. + +Baseline (fused-equations slice, committed `429fa23e`): million-particle fixed +step `nx=565`, 10 steps -- cache build ~0.034-0.046 s/step is the dominant term; +best warm 10-step wall 3.77 s (`15.25x` vs CPU 57.48 s). Adaptive `nx=100` +resolved: exactly 1393 steps, Warp wall 10.83-14.33 s. + +## Context + +`build_neighbor_cache_gpu` (warp_nnps.py:963) builds a flat CSR list with two +neighbor traversals (`_grid_neighbor_lengths` at :305 to count, +`_grid_neighbor_fill` to fill) over the cell block produced by `_build_grid` +(:906, cached per `update()`). The continuity hot path has two neighbor +consumers: `compute_wcsph_accel_continuity` (the fused group kernel, always) and +`compute_wcsph_adaptive_timestep`'s `_wcsph_dt_factors` (warp_sph.py:1247, only +when `adaptive_dt`). The flat build also backs the summation path, the +per-equation oracle helpers, the host query API, and `compute_neighbor_sum` -- +all off the continuity hot path and left untouched. + +## Approach + +### 1. Code generator (`pysph/base/warp_codegen.py`) + +- Add `neighbor_mode='flat'|'grid'` to `generate_group_source` and + `build_group_kernel` (default `'flat'` -- existing behavior, host query/oracle + path, and all current tests unchanged). Thread the mode into `_cache_key` + so flat and grid variants compile and cache independently. +- Grid signature replaces the flat `starts/lengths/neighbors` with the grid + query arrays/bounds, matching `_grid_neighbor_lengths`: + `cell_starts, cell_counts, cell_particles, xmin, ymin, zmin, cell_size, + nx, ny, nz, ncells, radius_scale`. `dim`, `kernel_id`, equation scalars, and + output arrays keep their positions after the neighbor section in both modes. +- Split `_emit_geometry` into a `phase` parameter: `pre` emits `dx,dy,dz,rij2`; + `post` emits `rij,hij,grad,wij` and relative velocities; `all` (flat) emits + the full sequence in the existing order (so flat output is byte-identical). +- Grid loop body: compute the destination cell index, iterate + `dzc,dyc,dxc in range(-1,2)` with the in-bounds + `cid` checks, then + `for pos in range(cell_starts[cid], cell_starts[cid]+cell_counts[cid])`, + `j = wp.int32(cell_particles[pos])`, the `pre` geometry, the support cutoff + `hi_ = radius_scale*d_h[i]; hj_ = radius_scale*s_h[j]; + if rij2 < hi_*hi_ or rij2 < hj_*hj_:` and -- inside the guard -- the `post` + geometry then each block's `loop` snippet. `dx,dy,dz` are computed once and + reused by the cutoff and the gradient. In grid mode force `x,y,z,h` into the + signature even if the equations did not request them (the cutoff needs them). +- Re-indentation: the grid loop nests the body deeper than flat, so geometry + lines and `loop` snippets (authored at the flat 8-space indent) are reindented + by a fixed delta when emitted in grid mode. `initialize`/`post_loop` stay at + indent 4 (outside the loop) in both modes. Add `neighbor_mode` to + `GroupKernel` so the launcher binds the right inputs. + +### 2. `warp_sph.py` + +- `compute_wcsph_accel_continuity` grows `neighbor_mode='flat'`. In grid mode it + calls `nnps._build_grid(src_index)` + reads `nnps._bounds`, builds the + grid-mode group kernel, binds the grid arrays/bounds + `radius_scale` in + signature order, does one launch + one sync, and never touches the flat cache. +- Add `_wcsph_dt_factors_{f32,f64}` grid-direct variants (hand kernels mirroring + warp_sph.py:1247 with the cell-block loop + cutoff) and a grid path in + `compute_wcsph_adaptive_timestep`. +- `_wc_sph_pec_continuity_step`: drop both `build_neighbor_cache_gpu` calls; + build the grid once per half-stage (cached per `update()`), pass grid arrays + to both consumers via the new grid paths. + +### 3. Leave untouched + +`build_neighbor_cache_gpu` and the flat path (summation density, per-equation +oracle helpers, host `get_nearest_particles`, `compute_neighbor_sum`), the +flat `_wcsph_dt_factors`, the Euler step, `_apply_wcsph_eos`. + +## Files expected to change + +- `pysph/base/warp_codegen.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_codegen.py` +- `pysph/base/tests/test_warp_sph.py` +- experiment.md + a new summary folder under + `experiments/2026-06-16_warp-elliptical-drop-runner/` +- aspect contexts (warp-backend, gpu-nnps, validation-benchmarks), current.md, + daily, session log, review artifact +- ADR-0004 registered in decisions/index.json + graph.md + +## Tests / validation + +- `test_warp_codegen.py`: a grid-mode generated kernel compiles and caches + (mode is part of the key, distinct from flat); grid-mode vs flat-mode kernels + produce identical outputs on a small multi-cell fixture sharing one cell list. +- `test_warp_sph.py`: the continuity half-stage calls `build_neighbor_cache_gpu` + **zero** times on the continuity path (monkeypatch count) and issues the + grid-direct launch; fused grid-direct vs separate-helper oracle parity on + `arho, au, av, aw, ax, ay, az`; existing CPU PEC-parity test still passes. +- Adaptive `nx=100` resolved guard: still exactly 1393 steps; deltas at fp32 + scale vs committed Warp. +- Segmented million-particle (`nx=565`, 10 steps) profile: cache-build term -> 0 + on the continuity path; report new per-step wall and headline vs CPU 57.48 s + and the prior best 3.77 s. +- `python .ai/.../scripts/validate-memory.py`; `git diff --check`. + +## Success criteria + +- The continuity path builds zero flat neighbor caches (test-asserted). +- Fused grid-direct matches the oracle and the CPU PEC state at fp32 scale; the + adaptive `nx=100` run keeps exactly 1393 steps. +- The million-particle per-step wall drops materially with the cache-build term + removed; reported in the review with the segmented profile. +- The focused suite passes (currently 47) plus the new grid tests. +- No multi-hour run is launched. + +## Risks + +- Membership parity: grid-direct must apply the same support cutoff the flat + build applied, or it would include/exclude neighbors differently. Guarded by + the grid-vs-flat and grid-vs-oracle parity tests. +- Register pressure / branch divergence: the cell walk inside the (now single) + equation kernel may cut occupancy and offset the saved build. The segmented + profile is the arbiter; fallback is ADR-0004's incremental-tuning alternative. +- fp32 neighbor-visitation-order change (cell order vs CSR order) shifts sums + ~1e-7; bounded, and the adaptive step-count guard re-validates. +- Codegen indentation: the deeper grid nesting reindents snippets; a mistake + surfaces as a Warp compile error. Mitigated by the grid-vs-flat parity test + and keeping `initialize`/`post_loop` at the unchanged indent. + +## Out of scope + +- Migrating dt-factors / summation / oracle helpers onto the generator + (ADR-0003 follow-up). +- Removing `build_neighbor_cache_gpu` entirely (still backs the host query API). +- Grid-direct for cross-array (`src != dst`) groups, periodic-distance support. +- Full `nx=565`, `tf=0.0076` GPU-only elliptical-drop run. + +## Approval + +- [x] Plan posted in chat and approved +- Approved by: @kunalpuri-prediqt at 2026-06-18T10:30:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-summation-step-paths.md b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-summation-step-paths.md new file mode 100644 index 000000000..dc46ad43b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-summation-step-paths.md @@ -0,0 +1,77 @@ +--- +type: plan +id: 2026-06-18_warp-grid-direct-summation-step-paths +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-18T14:00:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps] +adr: ADR-0004 +within_boundary: true +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +--- + +# Plan: Grid-direct the summation Euler/KDK step paths + +## Goal + +Remove flat-cache builds from all device step paths. The continuity PEC path is +already grid-direct (ADR-0004); extend grid-direct to the summation Euler +(`wc_sph_euler_step`) and KDK leapfrog (`_compute_wcsph_acceleration`) paths. A +pure flat->grid neighbor-source switch, NO fusing, preserving the exact +composition (pressure-gradient overwrite -> viscosity additive), so the only +numerical change is neighbor visitation order (the fp32 reorder already +validated for continuity). After this, `build_neighbor_cache_gpu` is used only +by the host `get_nearest_particles` query API, `compute_neighbor_sum`, and the +flat-mode oracle/cross-array tests. + +## Approach (`warp_sph.py`) + +1. Add `neighbor_mode='flat'` to the five standalone equation helpers + (`compute_summation_density`, `compute_continuity`, `compute_pressure_gradient`, + `compute_artificial_viscosity`, `compute_xsph_correction`), threaded to + `_run_equation_group`. Default flat keeps the oracle/cross-array tests + unchanged. +2. `_compute_wcsph_acceleration` and `wc_sph_euler_step`: pass + `neighbor_mode='grid'` to summation density, pressure gradient, viscosity + (accumulate), and the continuity-mode branch. Keep separate launches (no + fusing). +3. The summation branch of `wc_sph_leapfrog_step`: pass `neighbor_mode='grid'` + to its `compute_wcsph_adaptive_timestep` and `compute_xsph_correction` calls. + +## Tests / validation + +- `test_warp_wc_sph_euler_step_matches_cpu_expected_state` and + `test_warp_wc_sph_leapfrog_step_matches_cpu_expected_state` still pass + (grid-direct fp32 reorder; loosen a tolerance only if the shift is + demonstrably fp32-scale, as accepted for continuity). +- Add assertions: the summation Euler and KDK leapfrog steps build zero flat + caches (monkeypatch `build_neighbor_cache_gpu`). +- Continuity adaptive `nx=100` guard unchanged (`1393` steps); focused suite + green; `validate-memory`; adversarial-review workflow before sign-off. + +## Success criteria + +- No device step path builds a flat neighbor cache; `build_neighbor_cache_gpu` + remains only for the host query API, `compute_neighbor_sum`, and flat-mode + oracle/cross-array tests. Summation Euler/KDK parity holds at fp32 scale. + +## Risks + +- fp32 visitation-order shift on the summation parity tests (bounded ~1e-6, + validated pattern). The summation-density grid cutoff matches the flat cache's + (`radius_scale*h`), so the neighbor set is identical. + +## Out of scope + +- Fusing the summation accel; deleting `build_neighbor_cache_gpu`; periodic + distance; the production results report. + +## Approval + +- [x] Plan posted in chat and approved +- Approved by: @kunalpuri-prediqt at 2026-06-18T14:00:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md new file mode 100644 index 000000000..5b139176f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md @@ -0,0 +1,140 @@ +--- +type: plan +id: 2026-06-18_warp-migrate-neighbor-kernels-onto-generator +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-18T12:30:00 CEST +status: approved +aspects: [warp-backend, gpu-nnps] +adr: ADR-0003 +within_boundary: true +host_files: + - pysph/base/warp_codegen.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_codegen.py + - pysph/base/tests/test_warp_sph.py +--- + +# Plan: Migrate remaining neighbor-loop kernels onto the generator (ADR-0003 follow-up) + +## Goal + +Make `warp_codegen` the single source for every neighbor-loop kernel and retire +the duplicated hand-written `@wp.kernel`s. Behavior-preserving consolidation +(perf-neutral): the standalone equation helpers keep their public signatures, +I/O, cross-array support, and flat default; only the kernel they launch changes +from a hand kernel to a generated single-block group. This is ADR-0003's +recorded follow-up; no new ADR. + +Not in scope: fusing/grid-directing the summation path, narrowing +`build_neighbor_cache_gpu`, periodic distance. + +## Context + +Hand kernels duplicating generator blocks (to retire, ~14): `_summation_density`, +`_continuity`, `_pressure_gradient`, `_artificial_viscosity`, +`_xsph_correction` (f32+f64 each), and `_wcsph_dt_factors{,_grid}` (f32+f64). + +Key semantic detail: the standalone helpers compose via **read-modify-write**. +`_artificial_viscosity` does `au = d_au[i]; au += ...; d_au[i] = au` so the +summation path (`_compute_wcsph_acceleration`: pressure gradient then viscosity, +two launches) accumulates viscosity onto the pressure-gradient acceleration. +Pressure gradient / continuity / xsph / summation density **overwrite** their +outputs (first/only contribution). Generator blocks currently overwrite (write a +shared `_acc` once), which is correct for the fused continuity group but would +clobber for the standalone additive viscosity. + +The CFL dt-factor is a per-particle **max-reduction** over neighbors plus a +neighbor-independent `dt_force`; both fit the generator because block `loop` +snippets are free-form (`_acc_dt_cfl = wp.max(...)`). + +Tests call the public helpers, never the inner kernels; the standalone helpers +have cross-array tests (`src != dst`). The generator already emits `s_`/`d_` +arrays separately, so cross-array is preserved. + +## Approach + +### 1. Generator: add `accumulate_outputs` (`warp_codegen.py`) + +- `generate_group_source(..., accumulate_outputs=False)`: when True, initialize + each accumulator from the existing output (`_acc_ = d_[i]`) instead + of `TYPE(0.0)`, so the group adds to the destination arrays (read-modify-write). +- Thread through `build_group_kernel`; include in `_cache_key` and `GroupKernel`. + +### 2. New blocks (`warp_sph.py`) + +- `SummationDensity` (`src_arrays=('m',)`, `out_arrays=('rho',)`, + `requires=('rij','hij','wij')`, `loop: _acc_rho += s_m[j]*wij`). +- `WcsphCflFactor` (`dst_arrays=('au','av','aw')`, `out_arrays=('dt_cfl','dt_force')`, + `scalars=('c0',)`, `requires=('dx','dy','dz','rij2','hij','vij*')`): + `loop` does the `rij2>1e-12` guard + `_acc_dt_cfl = wp.max(_acc_dt_cfl, + |hij*vdotx/rij2| + c0)`; `post_loop` sets `d_dt_force[i] = au^2+av^2+aw^2`. + Runs in flat and grid via `neighbor_mode` (de-duplicates both dt-factors + kernels into one block). + +### 3. Shared launcher + repoint helpers + +- Add `_run_equation_group(nnps, src_index, dst_index, blocks, scalar_values, + kernel, cache, neighbor_mode, accumulate_outputs)` that builds the group, binds + inputs in the generator's canonical order (src arrays, dst arrays, neighbor + section [flat cache or `_grid_launch_args`], dim, kernel_id, scalars, outputs), + launches once, syncs. +- Repoint, preserving each public helper's signature/push/return/cross-array and + **flat default**: + - `compute_summation_density` -> `[SummationDensity()]`, overwrite. + - `compute_continuity` -> `[ContinuityEquation()]`, overwrite. + - `compute_pressure_gradient` -> `[PressureGradient()]`, overwrite. + - `compute_artificial_viscosity` -> `[ArtificialViscosity()]`, **accumulate=True**. + - `compute_xsph_correction` -> `[XSPHCorrection()]`, overwrite. + - `compute_wcsph_adaptive_timestep` -> `[WcsphCflFactor()]` (flat default; + continuity step keeps passing `grid`), then the existing init/reduce/finalize + reductions unchanged. +- Refactor `compute_wcsph_accel_continuity` to use `_run_equation_group` too + (DRY; same fused group, grid default). +- `_compute_wcsph_acceleration` (summation path) is unchanged in structure: + pressure gradient (overwrite) then viscosity (accumulate) then continuity, now + generator-backed -- byte-for-byte same composition. + +### 4. Retire the hand kernels + +Delete the ~14 duplicated `@wp.kernel`s. Keep the EOS kernels (per-particle, no +neighbor loop), the integrator kernels, and the dt init/reduce/finalize +reduction kernels. + +## Tests / validation + +- Full focused suite stays green (every helper is now generator-backed and is + covered by the existing CPU-reference parity + cross-array tests; the additive + viscosity test guards `accumulate=True`). +- `test_warp_fused_accel_matches_separate_helpers` becomes a fusion-consistency + check (both sides generator-backed); kept. +- New `test_warp_codegen.py` test: `accumulate_outputs=True` adds to the existing + output (vs overwrite for False) on a small fixture. +- Adaptive `nx=100` resolved guard keeps exactly `1393` steps (validates the + migrated dt-factors in grid mode). +- Quick million fixed-step profile: continuity path unchanged, no regression. +- `validate-memory.py`; `git diff --check`; then an adversarial-review workflow + over the diff before sign-off. + +## Success criteria + +- The ~14 hand neighbor-loop kernels are gone; the generator is the single + source. Focused suite passes; adaptive guard keeps 1393 steps; CPU-parity and + cross-array deltas stay at fp32 scale; no million-particle regression. + +## Risks + +- Additive vs overwrite semantics: viscosity must stay additive + (`accumulate=True`); guarded by `test_warp_artificial_viscosity_matches_cpu_and_adds_to_acceleration`. +- I/O-contract drift in the rewritten helpers (push/pull/return/cross-array); + guarded by the per-helper CPU + cross-array tests. +- fp32 single-block-group vs hand-kernel ordering ~1e-7; within parity tolerances. +- CFL `max` via free-form snippet relies on `_acc` init 0 being a valid identity + (factors > 0); true here. + +## Approval + +- [x] Plan posted in chat and approved +- Approved by: @kunalpuri-prediqt at 2026-06-18T12:30:00 CEST +- Approval, verbatim quote: + > APPROVED diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-periodic-minimum-image-distance.md b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-periodic-minimum-image-distance.md new file mode 100644 index 000000000..8cc49b23c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-periodic-minimum-image-distance.md @@ -0,0 +1,110 @@ +--- +type: plan +id: 2026-06-18_warp-periodic-minimum-image-distance +author: @kunalpuri-prediqt +agent: claude +created: 2026-06-18T15:00:00 CEST +status: approved +aspects: [gpu-nnps, warp-backend, validation-benchmarks] +adr: ADR-0004 +within_boundary: true +host_files: + - pysph/base/warp_codegen.py + - pysph/base/warp_nnps.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_codegen.py + - pysph/base/tests/test_warp_sph.py +--- + +# Plan: Periodic minimum-image neighbor distance (grid-direct path) + +## Goal + +True periodic neighbors for the WCSPH device paths: find neighbors across +periodic boundaries (wrapped cell walk) and use minimum-image distances. Today +`wrap_periodic` only wraps particle *positions*; the cell walk has hard +`ix>=0 and ix= 3` cells per periodic dim and `cell_size >= radius_scale*h` + with box >= 2*support (standard SPH periodic constraint); guard with a clear + error. +- `wp.round` at exactly half-box is a measure-zero tie; bounded. +- The periodic variant adds per-dim runtime branches; only compiled/used when a + periodic box is set, so the validated non-periodic path is untouched. + +## Out of scope + +- Ghost-particle approach; periodic support for the flat + `build_neighbor_cache_gpu` host-query path (device paths are all grid-direct); + mixed periodic/mirror domains; cross-array periodic. + +## Approval + +- [x] Plan posted in chat; purpose clarified; direction confirmed +- Approved by: @kunalpuri-prediqt at 2026-06-18T15:00:00 CEST +- Approval, verbatim quote (AskUserQuestion selection): + > Do (2) then prepare (1) diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-device-resident-rigid-body-p2.md b/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-device-resident-rigid-body-p2.md new file mode 100644 index 000000000..9bdf310a5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-device-resident-rigid-body-p2.md @@ -0,0 +1,165 @@ +--- +type: plan +id: 2026-06-20_warp-device-resident-rigid-body-p2 +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-20T06:10:44 CEST +status: approved +aspects: [warp-backend, particle-memory, validation-benchmarks] +adr: ADR-0006 +within_boundary: true +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +--- + +# Plan: Device-resident rigid-body 6-DOF integration (ADR-0006 P2) + +## Goal + +Keep rigid-body dynamics on the GPU: reduce body moments, finalize force/torque +and angular acceleration, integrate COM linear/angular state with RK2, and +rigid-transform every body particle without a per-stage host copy or host +6-DOF solve. Host transfers occur only when the caller explicitly requests +output or validation data. + +## Context + +P1 already reduces each body's 16 moment/force/torque terms on the GPU into an +f64 Warp array, but `compute_rigid_body_moments()` immediately copies that tiny +array to NumPy and finalizes the 6-DOF solve on the host. That was selected for +implementation convenience and parity, not because Warp cannot perform the +solve. The user approved changing direction after calling out the architectural +cost: a host finalize introduces synchronization, blocks a fully device-resident +step/CUDA-graph future, and scales poorly with body count. + +The production path should therefore remain on-device. The existing +`_rigid_finalize_moments()` stays as a trusted NumPy oracle/debug API. This is an +amendment to ADR-0006's host-finalize choice; its additive-backend, f64-reduction, +and sibling-driver decisions remain unchanged. + +## Approach + +### 1. Persistent device rigid state + +- Add an internal state/buffer object for `nbody` bodies containing f64 device + arrays for the raw 16-slot reduction, mass, COM, force, acceleration, inertia, + torque, angular acceleration, COM velocity, angular velocity, and saved RK2 + state. Keep the static `body_id` array on-device. +- Allocate once and reuse across stages/steps. Do not allocate or call + `.numpy()` in the production stage path. +- Particle properties remain in their configured dtype (normally fp32); compact + rigid state and reduction stay f64 for stable sums and the 3x3 solve. + +### 2. Device finalize and 3x3 solve + +- Add a one-thread-per-body Warp kernel that converts the 16 reduced terms into + COM, parallel-axis inertia, force/COM acceleration, and torque about COM using + the same formulas as `_rigid_finalize_moments()`. +- Compute `omega_dot = inv(I) * (torque - omega x (I*omega))` on-device using an + explicit symmetric 3x3 adjugate/determinant solve. Guard non-positive mass and + singular/near-singular inertia with clear host-entry validation or a device + error flag checked only at explicit validation/output boundaries. +- Refactor the P1 reduction into a device-returning internal launcher. Preserve + `compute_rigid_body_moments()` by making it call the device path and pull the + result only because that public helper explicitly asks for host results. + +### 3. Device RK2 integration and rigid transform + +- Add additive Warp kernels mirroring `RK2StepRigidBody` and + `RigidBodyMotion`: + - initialize/save `x0/y0/z0`, `vc0`, and `omega0`; + - midpoint stage: `vc = vc0 + 0.5*dt*ac`, + `omega = omega0 + 0.5*dt*omega_dot`; + - full stage: `vc = vc0 + dt*ac`, + `omega = omega0 + dt*omega_dot`; + - compute each particle velocity as + `v = vc + omega x (x - cm)` and update stage position from its saved + position, matching PySPH's RK2 rigid-body semantics. +- Expose a small P2 entry point that performs reduction -> device finalize -> + RK2 stage -> device motion for synthetic/applied body forces. P3 will compose + this primitive into `wc_sph_dam_break_rigid_step`; do not modify the existing + fixed-wall `wc_sph_dam_break_step`. + +### 4. Decision and experiment memory + +- Amend ADR-0006 to replace host production integration with the device-resident + design, while retaining `_rigid_finalize_moments()` as the oracle. +- Update the floating-body experiment with P2 commands/results and the explicit + no-host-round-trip invariant; regenerate the decision graph if ADR frontmatter + changes. + +## Files expected to change + +- `pysph/base/warp_sph.py` — persistent rigid state, device finalize/solve, + RK2/motion kernels, and P2 entry point. +- `pysph/base/tests/test_warp_sph.py` — numerical parity and residency/invariant + tests. +- `.ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_*.md` + — record the approved device-resident amendment. +- `.ai/implementations/blast-from-the-past/experiments/2026-06-19-warp-floating-body-rigid/experiment.md` + — P2 evidence. +- Plan/review/session/current/aspect memory required by the operating contract. + +## Tests / validation + +- Device finalize parity for asymmetric one- and two-body fixtures, fp32 + particle data and fp64 particle data: mass, COM, inertia, force, acceleration, + torque, and `omega_dot` match `_rigid_finalize_moments()` at dtype-appropriate + tolerances. +- RK2 midpoint and full-stage parity against a faithful NumPy implementation of + `RK2StepRigidBody` + `RigidBodyMotion`, including nonzero translation, + rotation, off-diagonal inertia, force, and torque. +- Rigid invariants: pairwise body-particle distances remain fixed to numerical + tolerance; zero force/torque preserves linear/angular velocity; pure + translation and pure rotation behave correctly; two bodies remain isolated. +- Residency guard: production P2 stepping does not invoke `.numpy()`, ParticleArray + pull, or host moment finalization inside either stage. One final pull for test + assertions is allowed. +- Existing P1 host helper tests remain green, proving compatibility. +- Existing 2D generated-source golden/cache guard remains green; no + `warp_codegen` or shared kernel router edits. +- Focused command: + `python -m pytest -q pysph/base/tests/test_warp_sph.py -k rigid pysph/base/tests/test_warp_codegen.py::test_2d_path_generated_source_is_byte_identical_to_golden`. +- `validate-memory.py`, `git diff --check`, and an adversarial review before + requesting @prabhu sign-off. + +## Success criteria + +- The complete P2 production stage is device-resident and numerically matches + the NumPy/PySPH formulas. +- No per-stage host synchronization/copy is introduced by rigid integration. +- P1 compatibility and the existing fixed-wall/2D paths remain unchanged. + +## Risks + +- A nearly singular inertia tensor can destabilize the explicit 3x3 solve; + reject degenerate bodies and test the error path. +- RK2 semantics can drift if COM/velocity is evaluated from the wrong stage; + midpoint/full-stage oracle tests pin ordering. +- Updating positions via stage velocities approximates orientation in the same + way as PySPH's reference step; long-run rigidity drift should be measured in + P4. A quaternion/orientation-matrix integrator is a later option if the + reference-compatible path drifts visibly. +- f64 device atomics/math require supported NVIDIA hardware; this was already + accepted and exercised by P0/P1. + +## Out of scope + +- Liu fluid/body coupling and NumberDensity pre-pass (P3). +- Collision/contact handling, assembled dam-break case, and rendering (P3/P4). +- CUDA graph capture itself; this slice removes the rigid-body host barrier that + would prevent it. +- Compyle/Python 3.14 repair and real CPU Application parity. + +## Estimated effort + +One substantial implementation session, approximately 250-400 LOC across the +backend and focused tests, followed by review. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-20T06:12:47 CEST +- Approval, verbatim quote: + > approved diff --git a/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md b/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md new file mode 100644 index 000000000..22c2874e4 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md @@ -0,0 +1,183 @@ +--- +type: plan +id: 2026-06-20_warp-liu-fluid-rigid-coupling-p3 +author: @kunalpuri-prediqt +agent: codex +created: 2026-06-20T19:08:57 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +adr: ADR-0006 +within_boundary: true +host_files: + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_sph.py +--- + +# Plan: Liu fluid–rigid coupling and sibling dam-break driver (ADR-0006 P3) + +## Goal + +Couple the existing device-resident rigid-body integrator to WCSPH fluid using +the real PySPH `LiuFluidForce` formulation, then run the first physically +meaningful Warp GPU case in which a dam-break surge moves a floating box. +Keep particle and compact rigid state device-resident, preserve the existing +fixed-wall driver unchanged, and produce numerical plus visual evidence. + +## Context + +P2 can integrate force/torque entirely on the GPU, but it currently receives +prescribed forces. P3 must generate those forces from SPH interactions. + +Source audit found two details that sharpen the old handoff: + +- `LiuFluidForce` itself does **not** consume `V`, although its historical + signature includes it. `NumberDensity` remains useful as a one-time static + body-volume pre-pass and parity diagnostic, but it must not be presented as + numerically required by Liu or rebuilt every step. +- A single fluid-destination Liu kernel would need atomic writes into source + body force arrays. fp32 atomic sums were already rejected by P0 as + nondeterministic. P3 will therefore use two deterministic generated passes: + fluid acceleration (`body -> fluid`) and the equal-and-opposite body reaction + (`fluid -> body`, destination-register accumulation). This duplicates one + neighbor walk but avoids source atomics and gives each body particle a stable + force before the existing f64 rigid reduction. + +## Approach + +### 1. Additive Liu equation blocks + +- Add `LiuFluidAcceleration(WarpEquation)`: + destination fluid, source body, outputs only fluid `au/av/aw`, implementing + `-m_body * (p_body/rho_body^2 + p_fluid/rho_fluid^2) * DWIJ`. +- Add `LiuBodyReaction(WarpEquation)`: + destination body, source fluid, outputs body `fx/fy/fz`; use the reversed + gradient sign so each interaction is equal-and-opposite to fluid momentum. +- Add `NumberDensity(WarpEquation)` (`V = sum W`) for a one-time self-neighbor + body pre-pass. Keep it outside the stepping hot path because rigid relative + geometry—and therefore `V`—does not change. +- All blocks are additive new structural cache entries. Do not edit existing + generated equation source, kernel routers, or `_WCSPH_DAM_BREAK_FLUID_BLOCKS`. + +### 2. Rigid density and force staging + +- Add a device density save/stage path for the rigid body: + `rho0 <- rho` once per physical step, then + `rho = rho0 + stage*dt*arho` at midpoint/full stages. This updates body + pressure response without sending the body through `wcsph_pec_stage`, which + would incorrectly integrate its particles as independent fluid points. +- Add a device body-force initializer that zeros `fx/fy/fz` and seeds + `m*[gx,gy,gz]` before Liu reaction accumulation. +- Body EOS is Tait-HG; body continuity comes from fluid neighbors only, matching + the shipped rigid examples' pressure-response pattern. + +### 3. New sibling driver: `wc_sph_dam_break_rigid_step` + +- Inputs: fluid index, fixed-wall indices, rigid-body index/state, dt/physics + controls. Never edit `wc_sph_dam_break_step`. +- Save fluid/wall WCSPH state, body density state, and P2 rigid state once. +- At each EPEC force evaluation: + 1. EOS: fluid Tait; fixed walls and body Tait-HG. + 2. Zero fluid/wall outputs; initialize body force with gravity. + 3. Existing fused pressure+AV+continuity over fluid and fixed walls only. + 4. Body contribution to **fluid continuity exactly once** via a standalone + `ContinuityEquation` pass—this resolves the recorded `arho` double-count. + 5. Liu fluid acceleration and deterministic body reaction passes. + 6. Body continuity from fluid; wall continuity from fluid; fluid-only XSPH; + fluid gravity. +- Predictor: fluid/walls PEC half-stage; body density half-stage; P2 rigid RK2 + half-stage; update NNPS from device positions. +- Corrector: reevaluate all forces at midpoint, then full fluid/wall, body + density, and rigid-body stages; update NNPS again. +- Adaptive dt remains the fluid device reduction plus the existing scalar + handoff. A rigid/contact dt criterion is deferred unless the transient proves + it necessary. + +### 4. First coupled runner and visual checkpoint + +- Extend `experiments/2026-06-19-warp-floating-body-rigid/` with a runner for a + rectangular floating box in the validated 3D dam-break flume. +- Start coarse enough for rapid transient iteration, then run a medium case to + the first surge/body impact. Save checkpoints for body COM/orientation proxy, + linear/angular velocity, force/torque, fluid density bounds, energy, and + finiteness. +- Produce a review image with the actual fluid particles/surface plus the box; + label it as a scientific render. Photorealistic Blender animation remains P4 + because Blender/ffmpeg are absent on this host. + +## Files expected to change + +- `pysph/base/warp_sph.py` — additive Liu/NumberDensity blocks, rigid density + stage, body-force initialization, and sibling coupled driver. +- `pysph/base/tests/test_warp_sph.py` — pairwise parity, equal/opposite force, + staging/order, existing-driver guard, and first-transient tests. +- `.ai/implementations/blast-from-the-past/experiments/2026-06-19-warp-floating-body-rigid/` + — coupled runner, metrics, and visual artifact. +- ADR/plan/review/session/aspect/current memory required by the contract. + +## Tests / validation + +- `NumberDensity` self-neighbor values match a CPU Wendland reference. +- One fluid/body pair and asymmetric many-particle fixture match a direct NumPy + Liu reference in fp32/fp64. +- Total fluid force plus body reaction is zero at interaction precision; body + torque matches direct `sum(r x f)`. +- Determinism: repeated body-reaction runs are stable because there are no + source atomics; P2 f64 reduction remains stable. +- Rigid density midpoint/full stages match NumPy and do not alter rigid + position/velocity independently. +- One coupled EPEC step matches a hand-rolled CPU/NumPy stage oracle for fluid + acceleration/density, body density, reaction force/torque, COM velocity, + angular velocity, and positions. +- First-plunge transient: initially supported/near-rest body then incoming + surge; check finite state, physically directed vertical/horizontal response, + body geometry, momentum reaction, and reproducibility. Do not use static + Archimedes draft as the sole gate—it cannot distinguish coupling models. +- Existing `wc_sph_dam_break_step` behavior test passes unchanged; 2D generated + source golden guard remains byte-identical. +- Focused suite, final warm full Warp SPH suite, `validate-memory.py`, + `git diff --check`, and adversarial review before @prabhu sign-off. + +## Success criteria + +- A dam-break surge moves and rotates the box from computed Liu forces, with no + per-stage particle-state host transfer and no body `arho` double-count. +- Fluid/body interaction force is equal-and-opposite within dtype tolerance; + body force/torque and one-step state match the direct oracle. +- Existing fixed-wall and 2D paths remain unchanged. +- A medium coupled run remains finite through first impact and yields an actual + review image plus quantitative metrics. + +## Risks + +- Two-pass coupling is deterministic but traverses body/fluid neighbors twice; + profile after correctness before considering a mixed-dtype atomic/fused path. +- Independently accumulated equal/opposite passes can differ at fp32 rounding + scale; gate total momentum with a dtype-derived tolerance. +- Body density/EOS staging order is easy to get subtly wrong; one-step and + midpoint oracle tests pin it. +- No collision/contact force is included initially. Choose the first-impact + horizon so the body does not penetrate a fixed wall; add the already-deferred + wall collision only if the case requires it. +- P2 is implemented but cannot be committed until @prabhu gives exact `LGTM` + on its review. P3 should remain an uncommitted stacked change until that gate + clears, or start only after sign-off if review isolation is preferred. + +## Out of scope + +- Photorealistic Blender/Cycles animation and ffmpeg stitching (P4). +- Rigid-wall or rigid-rigid contact unless required by the selected transient. +- Quaternion/orientation-matrix integration; P4 evaluates measured drift first. +- Compyle/Python 3.14 repair and full shipped CPU rigid Application parity. +- Coupling-kernel fusion or CUDA graph capture. + +## Estimated effort + +One to two substantial implementation sessions, approximately 350-550 LOC in +backend/tests plus the experiment runner and review artifacts. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-06-20T19:13:48 CEST +- Approval, verbatim quote: + > take this as @prabhu: LGTM for P2. approved for P3 diff --git a/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md b/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md new file mode 100644 index 000000000..671f74c4f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md @@ -0,0 +1,319 @@ +--- +type: plan +id: 2026-07-06_warp-dynamic-adaptive-particle-resolution +author: @kunalpuri-prediqt +agent: codex +created: 2026-07-06T09:57:06 CEST +status: approved +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: + - pysph/base/warp_adaptive.py + - pysph/base/warp_device_helper.py + - pysph/base/warp_nnps.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_adaptive.py + - pysph/base/tests/test_warp_device_helper.py + - pysph/base/tests/test_warp_nnps.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp dynamic adaptive particle resolution + +## Goal + +Add single-GPU, device-resident dynamic particle refinement and coarsening to +the Warp WCSPH path so a 3D dam-break can use fine particles only in important +regions. Demonstrate the method on a fixed-obstacle dam-break with materially +fewer active particles and lower time-to-solution than a uniform simulation at +the same finest spacing, while preserving relevant physical observables. + +This plan is deliberately bounded to the first credible adaptive production +path: fixed solid boundaries, geometry-driven resolution, a global timestep, +and one GPU. Local timestepping, solution-driven error estimation, moving rigid +bodies/contact, distributed memory, and multi-GPU execution are follow-ups. + +## Context + +The existing Warp backend already provides per-particle `m` and `h`, symmetric +variable-support neighbor inclusion, grid-direct fused WCSPH equations, +adaptive global timesteps, multi-array fixed solids, and a validated 3D +dam-break. These are necessary but not sufficient for adaptive resolution: + +- `UniformGridWarpNNPS` chooses one cell size from the global largest `h` and + reads coordinates/`h` to the host while recomputing bounds. A few coarse + particles would therefore inflate neighbor work for every fine particle. +- `WarpDeviceHelper` can add, remove, and resize particles, but those operations + currently use host NumPy copies/concatenation. Runtime APR must allocate, + initialize, deactivate, and compact particles on the device. +- The WCSPH equations accept variable `m`/`h`, but the adaptive formulation + still needs a selected 3D split/merge operator, variable-`h` consistency + correction, property transfer, particle regularization, and free-surface/ + solid-boundary safeguards. +- The smallest active `h` controls the current global CFL step. APR can still + save memory and neighbor work, but the fine region throttles all particles; + this limitation must be measured honestly. + +The primary algorithmic reference is Muta and Ramachandran, *Efficient and +Accurate Adaptive Resolution for Weakly-Compressible SPH* (2021), including +its open PySPH implementation (`https://gitlab.com/pypr/adaptive_sph`). Its +background resolution field, conservative split/merge workflow, neighbor-count +control, variable-`h` correction, and particle shifting are the starting point, +not a drop-in implementation: that work is 2D, EDAC-oriented, and does not +validate this 3D free-surface WCSPH dam-break. The 3D split/coalescence method +of Vacondio et al. (2016) and published GPU APR resource-management work are +additional evidence to evaluate before ADR-0007 selects the production design. + +## Approach + +### P0 - Freeze requirements, references, and non-adaptive baselines + +1. Add reference notes for the Muta--Ramachandran PySPH implementation, + Vacondio's conservative 3D splitting/coalescing method, and a GPU APR memory + management implementation. Record which equations and dimensional claims + are directly reusable and which require new validation. +2. Create an APR experiment packet with hardware, software versions, commands, + exact initial conditions, and separate correctness/performance outputs. +3. Add the fixed-obstacle geometry to the Warp dam-break runner without APR: + use the existing third solid array and `solid_indices=(1, 2)`. Validate this + fixed-resolution case before using it to judge adaptivity. +4. Record uniform-fine and uniform-coarse baselines at sizes that fit the local + GPU: particle count, peak device memory, neighbor candidates/accepted + neighbors, step time, CFL history, mass, kinetic energy, surge front, maximum + height, and obstacle pressure/impulse probes. +5. Run small 3D kill tests for candidate split patterns and merge selection. + Compare mass/momentum conservation, density reconstruction error, neighbor + count, isotropy, and GPU-friendly ownership. Do not choose a convenient + eight-child cube without evidence. +6. Create ADR-0007 from the evidence. It must decide the split pattern, merge + ownership rule, target-resolution representation, multilevel NNPS contract, + adaptation cadence, boundary policy, and whether inactive-capacity storage + or dense compaction is the canonical stepping representation. + +Checkpoint: P0 has its own review and commit. No APR performance claim is made. + +### P1 - Exact multilevel variable-resolution NNPS + +1. Add a multilevel grid mode to `warp_nnps.py`. Quantize smoothing lengths to + bounded resolution levels and build one device cell list per level with cell + size proportional to that level's support radius. +2. Implement cross-level traversal that preserves the current symmetric + inclusion rule: a pair is accepted when it falls inside either particle's + support. The traversal must inspect every potentially overlapping source + level without forcing fine particles through cells sized by global `hmax`. +3. Move bounds, per-level counts, offsets, scans, and grid metadata to the + device. A small scalar readback may initially configure launches, but there + must be no per-particle coordinate or `h` readback in the steady update. +4. Expose per-level diagnostics: active particles, occupied cells, candidate + pairs, accepted neighbors, and rebuild time. Keep the existing uniform-grid + mode unchanged for all current callers. +5. Integrate the new traversal into the generated equation-group launcher and + adaptive-timestep neighbor reduction as a new explicit neighbor mode. + +Checkpoint acceptance: + +- Exact neighbor-set parity with brute force for randomized 3D distributions, + empty levels, boundaries, and at least four levels spanning `h_max/h_min=16`. +- Fused equation outputs match the existing brute-force/grid oracle within + dtype tolerance on mixed-`m`, mixed-`h` fixtures. +- Candidate work scales with local level populations; inserting one coarse + particle does not globally resize fine cells. +- Existing uniform-grid and 2D generated-source tests remain unchanged. + +### P2 - GPU particle pool, allocation, and compaction + +1. Introduce `warp_adaptive.py` with a capacity-managed fluid particle pool. + Track active slots, stable particle IDs, resolution level, target level, and + all WCSPH/integrator properties required by split/merge and saved stages. +2. Add Warp kernels for flagging operations, exclusive-scan allocation, + deterministic owner selection, child initialization, merge writes, active + compaction, and old-to-new index maps. Use preallocated capacity and a + documented growth factor; capacity overflow must fail cleanly or reallocate + only at an explicit adaptation checkpoint. +3. Generalize `WarpDeviceHelper` only where needed to replace a complete device + property set after compaction. Preserve its existing public behavior and do + not rewrite unrelated host-compatible add/remove methods. +4. Rebuild NNPS level metadata and resize saved WCSPH state after compaction. + No stale ParticleArray count, device wrapper, or saved-stage array may retain + the pre-adaptation length. +5. Instrument host transfers and allocations. Routine adaptation after warm-up + must not pull particle properties to NumPy or allocate one device array per + new child. + +Checkpoint acceptance: + +- Repeated synthetic refine/coarsen cycles have no lost, duplicate, or + multiply-owned slots and preserve stable IDs for unchanged particles. +- All strided/scalar properties compact consistently; zero/empty/full-capacity + and overflow paths are tested. +- Mass and linear momentum are conserved by the raw device operations to + dtype-derived tolerance (`<=1e-12` relative in f64 and `<=1e-6` in f32 for + well-scaled fixtures). Any non-conserved quantity is explicitly documented. +- Steady adaptation performs no per-particle host transfer. + +### P3 - Conservative 3D split/merge and variable-resolution WCSPH + +1. Implement the ADR-selected 3D split operator. Initialize daughter position, + mass, smoothing length, velocity, density, pressure, sound speed, and + integrator state from the parent/local reconstruction. +2. Implement deterministic parallel merge candidate selection and conservative + coalescence. Preserve total mass and linear momentum; minimize density error + and quantify angular-momentum and kinetic-energy changes rather than hiding + them. +3. Add the selected variable-`h`/partition-consistency correction to sibling APR + equation blocks. Do not modify existing uniform WCSPH generated blocks. +4. Add iterative particle shifting/regularization and first-order property + correction. Prevent shifting across the free surface or through fixed solids. +5. Recompute `h` from local target mass/neighbor population and restrict jumps + between adjacent resolution regions according to ADR-0007. +6. Adapt only every `n_adapt` steps (configurable). The default cadence is + selected from measured adaptation cost and transient error, not assumed to + be every step. + +Checkpoint acceptance: + +- Constant fields are reproduced through split/merge/shift to roundoff; linear + field and kernel-summation errors have recorded convergence behavior. +- Hydrostatic and translating-fluid fixtures survive repeated crossings of a + refinement boundary without secular density/pressure growth. +- Conservation and density error meet the P2 gates after complete property + reconstruction, not only after raw slot operations. +- A uniform target level routes through the APR sibling path and matches the + existing uniform dam-break step at fp32/fp64 tolerance. + +### P4 - Geometry-driven adaptive obstacle dam-break + +1. Add a background target-resolution field for the flume. The first policy is + geometry driven and deterministic: finest near the obstacle, impact/probe + region, and selected free-surface band; progressively coarser in quiet bulk + fluid. Fluid particles split or merge as they move through this field. +2. Keep fixed wall/obstacle particles static and generate their local spacing + once so neighboring fluid/solid resolution ratios remain within the + ADR-selected limit. Dynamic boundary-particle adaptation is deferred. +3. Add a sibling adaptive dam-break driver. The existing fixed-wall and + rigid-body drivers remain unchanged. +4. Validate first on two levels, then raise the number of levels only after the + transition diagnostics pass. Save level-colored scientific snapshots and + adaptation histories; do not infer correctness from a plausible animation. +5. Compare against a uniform-fine run at the same finest spacing wherever it + fits. For a larger demonstration that cannot fit uniformly, report the + uniform-equivalent particle/memory estimate separately from measured active + particle count and measured peak memory. + +Final scientific gates: + +- All state remains finite and device error is zero. +- Relative total-mass drift attributable to adaptation is `<=1e-5` over the + benchmark; adaptation-event momentum residual is `<=1e-5` relative. +- Surge-front position and maximum height remain within two finest-particle + spacings of the uniform-fine reference at matched checkpoints. +- Obstacle pressure impulse and developed-flow kinetic energy remain within 5% + of the uniform-fine reference. If fp32 chaos makes pointwise peak pressure + unsuitable, use time-windowed impulse and disclose that choice in the review. +- Resolution-transition density error and neighbor-count distributions remain + bounded with no persistent void/banding at level interfaces. + +Final scale gates: + +- At least 4x fewer peak active particles than the equivalent uniform-fine + discretization for the selected case. +- At least 2x lower measured time-to-solution than a uniform-fine case that fits + on the same GPU. If the global fine-particle timestep prevents this, report + the miss and do not claim scale success. +- One adaptive case whose equivalent uniform-fine state would exceed available + device memory completes within measured device capacity. Label the uniform + figure as an estimate and keep correctness anchored to the smaller runnable + uniform reference. + +Each phase receives a review artifact and exact `@prabhu: LGTM` before its +commit. Later phases may amend numerical thresholds through reviewed evidence, +but may not silently weaken them. + +## Files expected to change + +- `pysph/base/warp_adaptive.py` (new) - device particle pool, target-resolution + data, split/merge/shift kernels, conservation diagnostics, and adaptation + orchestration. +- `pysph/base/warp_nnps.py` - additive multilevel grid and cross-level search; + existing uniform mode retained. +- `pysph/base/warp_device_helper.py` - minimal device-property replacement/ + compaction integration. +- `pysph/base/warp_sph.py` - additive APR equation blocks, multilevel launcher + routing, variable-`h` correction, adaptive timestep routing, and sibling + adaptive dam-break driver. +- `pysph/base/tests/test_warp_adaptive.py` (new) - particle-pool, conservation, + reconstruction, shifting, and repeated adaptation tests. +- Existing focused Warp test files listed in frontmatter - regression, NNPS + parity, helper lifetime, generated-source, and solver integration tests. +- `.ai/implementations/blast-from-the-past/experiments/` - new APR kill tests, + obstacle runner, uniform/adaptive outputs, performance data, and figures. +- ADR-0007, reference notes, aspect contexts/issues, reviews, session logs, + closeouts, and `current.md` as required by the operating contract. + +All planned host files are within the existing `pysph/base/warp_*.py` and +`pysph/base/tests/test_warp_*.py` integration boundary. If implementation shows +that changes to generic ParticleArray, Cython ABI, solver, or shipped examples +are required, stop and amend this plan with `within_boundary: false` before +touching them. + +## Tests / validation + +- Focused unit tests after every phase using the active project Python. +- Brute-force CPU/NumPy oracles for neighbor sets, split/merge conservation, + interpolation, density, and one-step APR behavior. +- Existing full `test_warp_*` suite and the complete Warp SPH suite at each + reviewed checkpoint; final `python -m pytest -m "not slow" pysph` if runtime + is practical, otherwise record the focused/full split and reason. +- Fixed-obstacle uniform CPU-vs-Warp comparison before APR validation. +- Repeated performance runs after warm JIT/cache, including device model, + driver, Warp version, precision, commit, commands, active/equivalent particle + counts, memory, adaptation cost, neighbor work, step count, and physics deltas. +- `validate-memory.py`, decision-graph regeneration when ADR-0007 lands, + `git diff --check`, and adversarial review before every commit. + +## Risks + +- The published PySPH adaptive method is not a validated 3D free-surface WCSPH + recipe. Porting mechanics without re-deriving the formulation could conserve + mass yet produce incorrect pressure and density at transitions. +- In 3D, each factor-of-two spacing refinement changes local particle count by + roughly eight. Poor target-field hysteresis can cause explosive particle + growth or split/merge thrashing. +- Cross-level NNPS omissions are silent physics errors. Brute-force set parity + is a hard gate before any performance work. +- Particle shifting can move particles through a free surface or solid unless + classification and displacement limits are correct. +- A global timestep is controlled by the finest particles. Memory savings may + exceed runtime savings; local/multirate stepping is intentionally not hidden + inside this first plan. +- Device compaction changes particle indices and can invalidate saved state, + rigid mappings, output identity, or cached grids. Stable IDs and explicit + old/new maps are required. +- Warp JIT source size and compile time are already high for 3D Wendland groups. + New APR groups must remain additive and be compiled/tested incrementally. + +## Out of scope + +- Local, asynchronous, or multirate timestepping. +- Multi-GPU/MPI domain decomposition, migration, and load balancing. +- Moving rigid bodies, rigid contact, and dynamically adapted solid boundaries. +- Solution/error-estimator-driven refinement (vorticity, pressure-gradient, + learned indicators) beyond recording the extension points. +- Arbitrary continuous `h`; the first production path uses bounded levels. +- Replacing existing uniform Warp paths or changing public Cython ABI/API. +- Photorealistic rendering. + +## Estimated effort + +Five reviewed implementation checkpoints. This is a multi-session research and +engineering track, expected to exceed 1,500 LOC across backend, tests, and +experiment runners. P0 and P1 are kill gates: if exact multilevel neighbor +search or variable-resolution physics cannot meet their correctness gates, the +plan stops before device-pool complexity or scale claims accumulate. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt at 2026-07-06T10:34:32 CEST +- Approval, verbatim quote: + > approved diff --git a/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-multilevel-gpu-nnps.md b/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-multilevel-gpu-nnps.md new file mode 100644 index 000000000..eee73baa3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/plans/2026-07-06_warp-multilevel-gpu-nnps.md @@ -0,0 +1,331 @@ +--- +type: plan +id: 2026-07-06_warp-multilevel-gpu-nnps +author: @kunalpuri-prediqt +agent: codex +created: 2026-07-06T11:18:54 CEST +status: in-progress +aspects: [gpu-nnps, warp-backend, particle-memory, validation-benchmarks] +host_files: + - pysph/base/warp_nnps.py + - pysph/base/warp_multilevel_nnps.py + - pysph/base/warp_codegen.py + - pysph/base/warp_sph.py + - pysph/base/tests/test_warp_nnps.py + - pysph/base/tests/test_warp_codegen.py + - pysph/base/tests/test_warp_sph.py +within_boundary: true +--- + +# Plan: Warp multilevel GPU NNPS for adaptive resolution + +## Goal + +Build the first runtime GPU foundation for adaptive particle resolution: an +exact, device-built multilevel cell-list NNPS that efficiently handles discrete +particle smoothing-length levels without sizing every cell from global +`h_max`. + +The checkpoint ends when existing generated SPH equation groups and the +adaptive-timestep reduction can consume the multilevel structure directly, +mixed-resolution neighbor sets match brute force exactly, and a representative +localized-refinement fixture shows materially less candidate work than the +current global-`h_max` uniform grid. + +This plan does **not** port the offline split-stencil minimization to the GPU. +That calculation produces constants before a simulation starts. It also does +not implement runtime split/merge or particle allocation; those consume this +NNPS in the next approved checkpoint. + +## Context + +`UniformGridWarpNNPS` currently: + +- pulls every array's `x/y/z/h` to the host in + `_compute_bounds_and_cell_size()`; +- chooses one `cell_size = radius_scale * global_hmax`; +- builds a device count/scan/scatter cell list per source array; +- feeds the generated `neighbor_mode='grid'` loop directly, with the exact + symmetric inclusion rule `rij < radius_scale*h_i OR ...*h_j`. + +That is correct for variable `h`, but not scalable for APR. A small number of +coarse particles makes cells coarse everywhere, so dense fine regions generate +many candidate checks. The current generated kernel also assumes one scalar +origin, cell size, and `(nx, ny, nz)` tuple. + +ADR-0004 adopted grid-direct traversal because each fused equation group reads +the neighbor structure once. This plan preserves that decision and adds a new +structural mode; it does not restore flat CSR lists in the timestep. + +The master APR plan originally assigned all architecture choices to one broad +ADR-0007. This focused plan proposes a cleaner decision graph: ADR-0007 will +cover only the multilevel NNPS after its kill tests. Particle-pool and +split/merge choices receive later ADRs once the P0 stencil mismatch is resolved. +Approval of this plan approves that narrower ADR responsibility; it does not +select production split weights. + +P0's Vacondio/PySPH stencil discrepancy does not block synthetic multilevel +neighbor-search work. The NNPS accepts configurable level boundaries and uses +only `x/y/z/h`; no daughter stencil is embedded in this checkpoint. + +## Approach + +### 1. Freeze the multilevel contract with CPU oracles + +- Define discrete levels by configurable `h_ref`, `level_ratio`, and + `nlevels`. Initial tests use ratios 2 and 1.2; no production default is + claimed until later APR evidence. +- Assign each source particle to exactly one level. Each level records a + conservative upper support bound at least as large as every assigned + `radius_scale*h_j`; particles outside configured bounds fail loudly rather + than being silently clipped. +- Preserve the existing symmetric pair contract exactly: + + ```text + rij^2 < (radius_scale*h_i)^2 OR + rij^2 < (radius_scale*h_j)^2 + ``` + +- Add deterministic NumPy/brute-force fixtures before GPU traversal: + single-level, four-level `h_max/h_min=16`, gradual 1.2 ratio, empty levels, + level-boundary values, clustered refinement, multiple source/destination + arrays, and particles near spatial bounds. +- Add a fp32 per-level grid-boundary fixture: particles placed exactly on a + level's cell-origin and far-edge coordinates in fp32. This guards the one + silent failure mode (a boundary particle flooring to cell `-1` or `nx` and + dropping a cross-level pair) and is the direct test for the per-level padding + requirement in step 2. +- Record current uniform-grid accepted and candidate counts for the clustered + fixture. Accepted sets must stay identical; candidate counts are the + optimization target. +- Record candidate/accepted counts broken down by `(destination-level, + source-level)` pair, not only as totals. The cost of exact multilevel + traversal concentrates in coarse-destination x fine-level queries (query + radius `radius_scale*h_i` against a fine cell size gives an index range of + order the level ratio, up to ~`(2*16+1)^3` cell visits per coarse destination + at `h_max/h_min=16` in 3D). The per-pair breakdown is what distinguishes real + cross-scale neighbors from iterating empty fine cells, and is the evidence the + decision gate uses to choose dense per-level grids versus sparse hash/sort. + +### 2. Add an explicit multilevel NNPS prototype + +- Add `MultilevelGridWarpNNPS` alongside `UniformGridWarpNNPS`; do not alter the + existing class's defaults or public behavior. +- Compute particle level, per-level counts, and per-level AABB reductions on + the GPU. Reading `O(number_of_levels)` scalar metadata to size dense level + grids is permitted in this checkpoint; reading per-particle `x/y/z/h` is not. +- Give each populated level its own origin, dimensions, and cell size based on + that level's conservative support bound. This avoids allocating a fine grid + over the spatial extent occupied only by coarse particles. +- Pad each per-level origin/extent by that level's cell size before flooring, + matching the existing uniform-grid padding in + `_compute_bounds_and_cell_size()`. fp32 AABB reductions can produce a bound + tight enough to floor a boundary particle to cell `-1` or `nx`; the padding + plus the fp32 boundary fixture in step 1 keep this from becoming a silent + cross-level omission. +- Flatten per-level cell arrays into device storage with metadata arrays: + level cell offsets, particle offsets/counts, origins, cell sizes, + `(nx,ny,nz)`, and maximum source support. Keep source particle indices in + original ParticleArray indexing so equation arrays require no remap. +- Reuse the existing GPU count -> exclusive scan -> scatter pattern. Every + particle appears in exactly one level, preventing duplicate pair visits. +- Cache one multilevel structure per source array per `update()`, matching the + current source-array ownership model. +- Add candidate/accepted-pair counters behind an explicit diagnostics flag so + validation can explain performance without taxing production launches. + +### 3. Generate an additive multilevel equation loop + +- Add `neighbor_mode='multilevel'` to `warp_codegen.py` as a new cache-key and + source-generation branch. Existing `'flat'` and `'grid'` emitted source must + remain byte-identical. +- Pass flattened level metadata/device cell arrays through a sibling launcher + such as `_multilevel_grid_launch_args`; do not overload the existing scalar + `_grid_launch_args` contract. +- For each destination particle and source level, compute the query radius as + `max(radius_scale*h_i, level_max_source_support)`. Convert that radius to the + necessary cell-index range for that level, then apply the exact pairwise + symmetric cutoff before equation snippets execute. +- Support 1D/2D/3D and cross-array traversal. The production target and hard + acceptance fixture are 3D. +- Route `_run_equation_group` and `compute_wcsph_adaptive_timestep` through the + new mode without changing any existing caller's default. +- Multilevel periodic traversal is explicitly rejected with a clear error in + this checkpoint. Correct per-level periodic tiling is deferred; silently + using the non-periodic walk is forbidden. + +### 4. Add a device neighbor-cache oracle, not a runtime dependency + +- Add a multilevel equivalent of `build_neighbor_cache_gpu()` for tests and + diagnostics. It may perform the existing small lengths/total-size readback to + allocate the packed neighbor output. +- Use that cache only to compare complete neighbor index sets with brute force. + Generated WCSPH kernels continue to walk level cell lists directly and do + not materialize CSR neighbors each stage. + +### 5. Validate equations, transfer behavior, and scaling + +- Compare full per-particle neighbor sets against `BruteForceWarpNNPS` for all + fixtures in fp32 and fp64. +- Compare multilevel versus flat/uniform-grid outputs for summation density, + fused pressure/viscosity/continuity, and adaptive CFL factors. +- Instrument or monkeypatch device-array host access so steady + `update(push=False)` fails the test if it calls `.get()`/`.numpy()` for + particle `x/y/z/h`. The permitted metadata transfer is separately counted. +- Verify update after device-coordinate motion, empty arrays/levels, repeated + updates, and multiple source arrays. +- Benchmark warm grid build and one representative fused consumer separately. + Report candidate pairs, accepted pairs, metadata bytes read back, build time, + kernel time, and peak device memory. +- Break the candidate/accepted pair counts down by `(destination-level, + source-level)` pair so the concentration of work in coarse-destination x + fine-level queries is visible, not hidden inside a single aggregate. This is + the measurement the dense-versus-sparse decision gate consumes. + +## Acceptance criteria + +### Correctness + +- Exact neighbor-index set parity with brute force for every destination in all + deterministic fixtures, including four levels spanning `h_max/h_min=16`. +- No duplicate source index is visited for a destination. +- fp32/fp64 SPH outputs match the flat/brute oracle at dtype-derived tolerance; + adaptive timestep matches after scalar rounding. +- Existing uniform-grid, periodic, generated-source golden, elliptical-drop, + fixed-wall dam-break, and rigid-coupling tests remain unchanged. + +### Device residency + +- No per-particle coordinate or smoothing-length host readback during warm + `update(push=False)` or generated equation traversal. +- Host metadata transfer is bounded by `O(nlevels * narrays)`, is explicitly + measured, and contains no particle property vectors. +- Level cell lists, scans, particle indices, and traversal remain on the GPU. + +### Scaling + +- On a localized four-level synthetic fixture, multilevel candidate-pair count + is at least 4x lower than the current global-`hmax` uniform grid while + accepted neighbors remain identical. +- A single-level multilevel fixture has no more than 20% warm kernel-time + overhead versus the current uniform grid. If it misses, single-level callers + continue using the existing class and the result is disclosed rather than + hidden. +- At least one mixed-level case shows lower measured build-plus-consumer time, + not merely fewer candidates. No general speedup claim is made from one case. + +### Decision gate + +- ADR-0007 is created only after the exact-set and candidate-scaling kill tests + pass. It records the level representation, device metadata contract, + cross-level traversal, permitted scalar readback, and periodic deferral. +- ADR-0007 must record the `O(nlevels*narrays)` metadata readback as a + prototype-only allowance, not the production device-residency contract. It is + a per-`update()` host synchronization; the eventual production APR path is + expected to eliminate it with a persistent max-levels allocation, and that + constraint must not be silently inherited from this checkpoint. +- If exact traversal requires unbounded work or dense level grids consume more + memory than the saved particle state on representative cases, stop and + compare sparse hash/sort alternatives before accepting the ADR. + +## Files expected to change + +- `pysph/base/warp_nnps.py` - additive multilevel class, level assignment, + GPU reductions/count/scan/scatter, debug cache, and diagnostics. +- `pysph/base/warp_codegen.py` - additive `'multilevel'` generated traversal + and cache key; old modes byte-identical. +- `pysph/base/warp_sph.py` - multilevel launch arguments and explicit routing + for generated groups/adaptive timestep. +- `pysph/base/tests/test_warp_nnps.py` - exact set, lifecycle, device-residency, + and candidate-count tests. +- `pysph/base/tests/test_warp_codegen.py` - multilevel source/signature tests and + existing-source golden guards. +- `pysph/base/tests/test_warp_sph.py` - equation and timestep parity. +- New P1 experiment artifacts, ADR-0007, aspect/current/session/review/closeout + memory as required by the operating contract. + +All host files match the existing `pysph/base/warp_*.py` and +`pysph/base/tests/test_warp_*.py` boundary. No generic NNPS Cython API or public +GPU export is touched. + +## Tests / validation + +- Fast kill tests first: level assignment, metadata, exact 2D/3D neighbor sets, + and candidate counts. +- Focused commands: + + ```text + python -m pytest -q pysph/base/tests/test_warp_nnps.py + python -m pytest -q pysph/base/tests/test_warp_codegen.py + python -m pytest -q pysph/base/tests/test_warp_sph.py + ``` + +- Final exact-tree `test_warp_*` suite after warm JIT cache. +- Re-run the 2D generated-source golden and representative no-obstacle, + obstacle, and rigid-driver smoke tests. +- Record all performance runs in a new P1 experiment with GPU, driver, Warp + version, commit, precision, particle/level distribution, commands, repeated + warm timings, candidate counts, accepted counts, memory, and correctness + hashes. +- `validate-memory.py`, decision-graph regeneration after ADR-0007, + `git diff --check`, and a prototype-owner review before commit. + +## Risks + +- Cross-level omissions are silent physics errors; exact brute-force set parity + is a non-negotiable gate. +- A coarse destination querying a dense fine level may legitimately span many + fine cells. The hierarchy removes artificial work but cannot remove real + cross-scale neighbors. +- Per-level dense grids may still be wasteful for disconnected fine regions; + sparse key/sort storage may be required after the kill test. +- Runtime loops over levels can reduce GPU occupancy or inflate generated Warp + code. Measure one fused consumer before expanding scope. +- GPU AABB reductions and metadata readback introduce synchronization. The + transfer is bounded but must be timed separately. +- Adding a new generated structural mode causes new cold JIT compilation. It + must not perturb existing cached source. +- The current P0 stencil mismatch means level ratios are provisional. The class + therefore remains configurable and prototype-only. + +## Out of scope + +- Runtime particle splitting, merging, allocation, free lists, or compaction. +- Variable-`h` physics corrections and particle shifting. +- Geometry- or solution-driven target-level assignment. +- Periodic multilevel domains. +- Local/multirate timestepping. +- Multi-GPU/MPI decomposition. +- Changes to generic PySPH NNPS APIs, Cython ABI, or shipped applications. +- Selecting or publishing production APR split weights. + +## Estimated effort + +Two to three substantial implementation sessions, approximately 500--800 LOC +across backend/tests plus experiment and decision artifacts. The exact-set and +candidate-count fixtures are the first kill gate; generated SPH integration +does not start until they pass. + +## Approval + +- [x] Plan posted in chat +- Approved by: @kunalpuri-prediqt on 2026-07-06 CEST +- Approval, verbatim quote: + > begin +- Implementation note (2026-07-06): step 1 (CPU-oracle contract) and step 2 + (`MultilevelGridWarpNNPS` with the single-level, four-level h16, and fp32 + per-level grid-boundary kill-gate fixtures) landed. The multilevel Warp + kernels live in a new `pysph/base/warp_multilevel_nnps.py` module so they add + no PTX footprint to the WCSPH path. Suites are gated as separate pytest + invocations; the combined single-process command hangs pre-existingly on WSL2 + PTX-JIT accumulation. +- Implementation note (2026-07-07): all eight step-2 kill-gate fixtures pass + plus device residency (GPU assign/AABB, no coordinate readback) and the + clustered ~9x candidate-reduction gate; ADR-0007 recorded (Proposed). Step 3 + landed: `neighbor_mode='multilevel'` in `warp_codegen` (flat/grid source + byte-identical) + `_multilevel_grid_launch_args` + `_run_equation_group` + routing, with multilevel-mode summation density matching the uniform grid in + 2D and 3D and a periodic-multilevel guard. Suites separate: nnps 34, + codegen 10, sph 57. Remaining: adaptive-timestep + fused continuity/pressure + multilevel parity, fp64 exercise, ADR-0007 accept after the dense-vs-sparse + memory check. diff --git a/.ai/implementations/blast-from-the-past/prompts/closeout-prompt.md b/.ai/implementations/blast-from-the-past/prompts/closeout-prompt.md new file mode 100644 index 000000000..bddcdf575 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/prompts/closeout-prompt.md @@ -0,0 +1,3 @@ +# Closeout Prompt + +Finalize the session log, update the daily closeout, refresh `current.md`, update aspect questions/issues, and update experiments. Run `validate-memory.py` before handoff. diff --git a/.ai/implementations/blast-from-the-past/prompts/implementation-prompt.md b/.ai/implementations/blast-from-the-past/prompts/implementation-prompt.md new file mode 100644 index 000000000..456b30b48 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/prompts/implementation-prompt.md @@ -0,0 +1,3 @@ +# Implementation Prompt + +After boot and approval, implement only the approved scope. Stay inside the integration boundary unless the plan explicitly says `within_boundary: false`. Update experiments, aspect notes, and ADRs as discoveries become durable. diff --git a/.ai/implementations/blast-from-the-past/prompts/planning-prompt.md b/.ai/implementations/blast-from-the-past/prompts/planning-prompt.md new file mode 100644 index 000000000..c968c29c4 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/prompts/planning-prompt.md @@ -0,0 +1,3 @@ +# Planning Prompt + +Read `.ai/AGENTS.md`, `.ai/implementations/blast-from-the-past/current.md`, `.ai/implementations/blast-from-the-past/implementation.md`, `.ai/implementations/blast-from-the-past/host-project-notes.md`, relevant aspect files, and scoped ADRs. Decide whether the work is Tier 0, Tier 1, or Tier 2. For Tier 1, post a one-paragraph plan and wait for approval. For Tier 2, create a plan file with `.ai/implementations/blast-from-the-past/scripts/new-plan.py` and wait for explicit approval. diff --git a/.ai/implementations/blast-from-the-past/prompts/review-prompt.md b/.ai/implementations/blast-from-the-past/prompts/review-prompt.md new file mode 100644 index 000000000..ae2406bca --- /dev/null +++ b/.ai/implementations/blast-from-the-past/prompts/review-prompt.md @@ -0,0 +1,3 @@ +# Review Prompt + +Before commit, create a review with `.ai/implementations/blast-from-the-past/scripts/new-review.py`. Include diff summary, tests/validation output, `validate-memory.py` output, boundary amendment status, risks, unresolved questions, and a visual aid or waiver. Wait for `@prabhu` LGTM and quote it verbatim. diff --git a/.ai/implementations/blast-from-the-past/references/index.md b/.ai/implementations/blast-from-the-past/references/index.md new file mode 100644 index 000000000..f271f2131 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/references/index.md @@ -0,0 +1,15 @@ +# References Index + +Reference notes for `blast-from-the-past`. + +## Primary + +- Prabhu guidance - placeholder to capture human/internal direction. +- NVIDIA Warp documentation - placeholder until exact docs/version are chosen. +- Muta--Ramachandran adaptive WCSPH - `primary/muta-ramachandran-adaptive-wcsph.md`. +- Vacondio et al. 3D splitting/coalescing - `primary/vacondio-3d-splitting-coalescing.md`. +- Yang et al. GPU APR - `primary/yang-gpu-adaptive-particle-refinement.md`. + +## Secondary + +- PySPH existing GPU NNPS implementation - host reference via `pysph/base/gpu_nnps_base.pyx` and related Cython files. diff --git a/.ai/implementations/blast-from-the-past/references/primary/muta-ramachandran-adaptive-wcsph.md b/.ai/implementations/blast-from-the-past/references/primary/muta-ramachandran-adaptive-wcsph.md new file mode 100644 index 000000000..5b4c89e3c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/references/primary/muta-ramachandran-adaptive-wcsph.md @@ -0,0 +1,70 @@ +--- +type: reference-note +id: muta-ramachandran-adaptive-wcsph +created: 2026-07-06T10:45:00 CEST +author: @kunalpuri-prediqt +kind: primary +status: assessed +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +--- + +# Reference: Muta--Ramachandran efficient adaptive WCSPH + +## Citation + +Abhinav Muta and Prabhu Ramachandran, “Efficient and Accurate Adaptive +Resolution for Weakly-Compressible SPH,” CMAME 2022, +DOI `10.1016/j.cma.2022.115019`; manuscript `arXiv:2107.01276`; source +`https://gitlab.com/pypr/adaptive_sph` (audited at repository HEAD on +2026-07-06). + +## TL;DR + +Provides the closest PySPH-native adaptive workflow: background particles set +spatial target mass, mutually closest particles merge in parallel, particles +split and are then repeatedly merged, shifting regularizes the distribution, +and smoothing length is reset from local average mass to control neighbor +count. It is not a ready 3D dam-break implementation. + +## Key claims + +- Geometry- and solution-driven target resolution can be represented by + background particles. +- Split/merge can be parallel; adaptation typically runs every 1--10 steps. +- Optimizing `h` from neighboring mass avoids the excessive support and + neighbor counts of older APR methods. +- The paper reports length-scale variation up to 1:250 and substantially fewer + particles than uniform-resolution comparisons. + +## Bearing on blast-from-the-past + +Use the workflow and validation ideas, not the implementation verbatim. The +paper validates 2D EDAC-style flows without a free surface. The source confirms +that automatic background adaptation asserts `dim == 2`, several mass/spacing +updates carry `FIXME in 3D`, and its GPU lifecycle path is explicitly not +implemented. Its `AdaptiveResolution` does contain a binary 3D split helper, +but that is not the paper's fully validated automatic method. + +## Equations / algorithms / APIs to use + +- Target thresholds: `m_max = 1.05*m_ref`, `m_min = 0.5*m_ref` as a candidate, + subject to 3D kill tests. +- Parallel merge ownership: mutually closest eligible pair; retain lower stable + ID; mass-weighted position, velocity, and scalar properties. +- Smoothing length from local mass scale: `h = C*(m/rho)^(1/d)` after + adaptation. +- Adapt -> iterative merge -> shift -> first-order property correction -> NNPS + rebuild ordering. + +## Questions raised + +- Which 3D split stencil minimizes density error with the current Wendland + kernel and practical neighbor count? +- Which variable-`h` correction is required for the continuity-density WCSPH + formulation? +- How must shifting be limited at a violent free surface and fixed obstacle? + +## Verdict + +Adopt as the PySPH process/reference baseline. Do not claim it validates the +planned 3D WCSPH implementation. diff --git a/.ai/implementations/blast-from-the-past/references/primary/prabhu-guidance-placeholder.md b/.ai/implementations/blast-from-the-past/references/primary/prabhu-guidance-placeholder.md new file mode 100644 index 000000000..39f4cbd8c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/references/primary/prabhu-guidance-placeholder.md @@ -0,0 +1,19 @@ +--- +type: reference-note +id: prabhu-guidance-placeholder +created: 2026-06-15T07:19:08 CET +author: @kunalpuri-prediqt +kind: primary +status: placeholder +aspects: [] +--- + +# Reference: Prabhu guidance placeholder + +## Citation +## TL;DR +## Key claims +## Bearing on blast-from-the-past +## Equations / algorithms / APIs to use +## Questions raised +## Verdict diff --git a/.ai/implementations/blast-from-the-past/references/primary/vacondio-3d-splitting-coalescing.md b/.ai/implementations/blast-from-the-past/references/primary/vacondio-3d-splitting-coalescing.md new file mode 100644 index 000000000..953e2a24a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/references/primary/vacondio-3d-splitting-coalescing.md @@ -0,0 +1,65 @@ +--- +type: reference-note +id: vacondio-3d-splitting-coalescing +created: 2026-07-06T10:45:00 CEST +author: @kunalpuri-prediqt +kind: primary +status: assessed +aspects: [warp-backend, particle-memory, validation-benchmarks] +--- + +# Reference: Vacondio et al. 3D splitting and coalescing + +## Citation + +R. Vacondio, B. D. Rogers, P. K. Stansby, and P. Mignosa, “Variable +resolution for SPH in three dimensions: Towards optimal splitting and +coalescing for dynamic adaptivity,” CMAME 300 (2016) 442--460, +DOI `10.1016/j.cma.2015.11.021`. + +## TL;DR + +Directly studies conservative 3D weakly-compressible SPH refinement. It +compares 8-vertex cubic, 14-vertex cubic-plus-face-center, 12-vertex +icosahedral, and 20-vertex dodecahedral arrangements, with and without a +daughter at the parent location. The reported optimum is the 12-vertex +icosahedron plus one central daughter (13 total); kernel choice has little +effect on the optimal stencil. + +## Key claims + +- The split/coalescence formulation conserves mass and momentum while choosing + parameters to minimize density interpolation error. +- A daughter should remain at the parent location regardless of stencil. +- The icosahedral arrangement is the best of the tested 3D configurations. +- Cubic, quintic, and Wendland kernels show similar density-error ranking. + +## Bearing on blast-from-the-past + +This is the primary candidate for the P0 3D stencil. A 14-or-15-slot operation +is much more expensive and allocation-heavy than binary or eight-child splits, +so the Warp kill test must compare density error per resulting active particle, +not only minimum error. + +## Equations / algorithms / APIs to use + +- For the Wendland case, the paper selects shell radius + `epsilon*h_parent = 0.65*h_parent` and daughter smoothing length + `alpha*h_parent = 0.70*h_parent`. The 12 shell masses are equal; the central + mass differs and all 13 mass fractions are obtained from the constrained + density-error minimization. +- Pairwise conservative coalescence: mass-weighted position/velocity and a + smoothing length selected to minimize density error. + +## Questions raised + +- Can iterative merge after splitting recover most of the accuracy with a + cheaper GPU stencil? +- Does the current Wendland routing reproduce the paper's density-error ranking + at fp32? + +## Verdict + +Use as the 3D scientific authority for candidate selection. The open post-print +has now been ingested; do not lock the stencil until the NumPy reproduction +matches the paper's error and mass-ratio table. diff --git a/.ai/implementations/blast-from-the-past/references/primary/yang-gpu-adaptive-particle-refinement.md b/.ai/implementations/blast-from-the-past/references/primary/yang-gpu-adaptive-particle-refinement.md new file mode 100644 index 000000000..2a3bf7082 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/references/primary/yang-gpu-adaptive-particle-refinement.md @@ -0,0 +1,55 @@ +--- +type: reference-note +id: yang-gpu-adaptive-particle-refinement +created: 2026-07-06T10:45:00 CEST +author: @kunalpuri-prediqt +kind: primary +status: assessed +aspects: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +--- + +# Reference: Yang et al. GPU adaptive particle refinement + +## Citation + +Q. Yang, F. Xu, Y. Yang, Z. Dai, and J. Wang, “A GPU-accelerated adaptive +particle refinement for multi-phase flow and fluid-structure coupling SPH,” +Ocean Engineering 279 (2023) 114514, +DOI `10.1016/j.oceaneng.2023.114514`. + +## TL;DR + +Demonstrates that dynamic APR can remain GPU-resident using explicit resource +management, refinement kernels, and particle shifting. It is architectural +evidence, not a formulation oracle for the current single-phase WCSPH driver. + +## Key claims + +- GPU-side dynamic resource management addresses changing particle count. +- Axis-aligned refinement plus a virtual-fine-particle shifting treatment can + remain accurate and stable in multi-phase/FSI examples. +- Adaptive cases reduce computational cost compared with uniform particles at + comparable precision. + +## Bearing on blast-from-the-past + +Supports the planned capacity-managed device pool, scan allocation, and +batched adaptation checkpoint. The paper uses a different Riemann-based SPH +model and does not justify copying its physics into the current backend. + +## Equations / algorithms / APIs to use + +- Separate capacity from active count. +- Reuse slots released by coalescence before growing storage. +- Regularize transition particles after refinement. + +## Questions raised + +- Which pool growth and compaction policy maps best to Warp arrays? +- Can stable IDs and deterministic merge ownership be kept without serial + allocation? + +## Verdict + +Adopt the resource-management principles; independently validate all WCSPH +physics and split parameters. diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-artificial-viscosity-momentum-term.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-artificial-viscosity-momentum-term.md new file mode 100644 index 000000000..457ebae51 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-artificial-viscosity-momentum-term.md @@ -0,0 +1,186 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md +adrs: [] +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: lgtm +--- + +# Review - Warp Artificial Viscosity Momentum Term + +## Diff summary + +- Added float32/float64 Warp kernels for Monaghan-style artificial viscosity. +- Added `compute_artificial_viscosity()` as an additive acceleration pass over + existing `au`, `av`, and `aw`. +- Wired optional `alpha`/`beta` through `wc_sph_leapfrog_step()` and + `wc_sph_euler_step()` while preserving inviscid defaults. +- Added a focused CPU-reference test for artificial viscosity, including + nonzero initial acceleration to prove additive behavior. +- Exposed `--alpha` and `--beta` in the Warp elliptical-drop runner and updated + smoke/ramp experiment evidence. +- Updated current, aspect, daily, session, and experiment memory. + +## Aspects touched and host files modified + +- Aspects: `warp-backend`, `gpu-nnps`, `validation-benchmarks`, + `host-integration`. +- Host files: + - `pysph/base/warp_sph.py` + - `pysph/base/tests/test_warp_sph.py` + +## Behavioral / numerical changes + +- New optional artificial-viscosity contribution: + +```text +Pi_ij = (-alpha*c0*mu_ij + beta*mu_ij^2) * 2/(rho_i + rho_j) +mu_ij = HIJ * (v_ij dot x_ij) / (r_ij^2 + 0.01*HIJ^2) +``` + +- The term only contributes for approaching pairs where `v_ij dot x_ij < 0`. +- The viscosity pass adds onto existing acceleration arrays. The pressure + gradient path still sets the inviscid acceleration first. +- Defaults remain inviscid in low-level step helpers: `alpha=0.0`, + `beta=0.0`. +- The elliptical-drop runner now defaults to and records `alpha=0.1`, + `beta=0.0`. + +## Tests / validation run + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +.............................. [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +30 passed, 2 warnings in 4.39s +``` + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 15.35 ms (cached) +Module pysph.base.warp_sph 128be63 load on device 'cuda:0' took 4.16 ms (cached) +{ + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "dt": 1e-05, + "kinetic_energy": 8078.167363381624, + "nx": 8, + "p_max": -0.01952648162841797, + "p_min": -166.61477661132812, + "particles": 204, + "radius_max": 0.9978744032287784, + "rho_max": 0.999951183795929, + "rho_min": 0.5834630727767944, + "steps": 2, + "time": 2e-05, + "x_max": 0.9480998516082764, + "x_min": -0.9231499433517456, + "y_max": 0.9518998861312866, + "y_min": -0.9268498420715332 +} +``` + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 16 --steps 5 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-avisc-nx16-steps5.npz +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 12.47 ms (cached) +Module pysph.base.warp_sph 128be63 load on device 'cuda:0' took 2.96 ms (cached) +{ + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "dt": 1e-05, + "kinetic_energy": 7868.737673401772, + "nx": 16, + "p_max": -0.009822845458984375, + "p_min": -146.79534912109375, + "particles": 805, + "radius_max": 1.0018194069173603, + "rho_max": 0.9999754428863525, + "rho_min": 0.6330116391181946, + "steps": 5, + "time": 5e-05, + "x_max": 0.9452486038208008, + "x_min": -0.9825611710548401, + "y_max": 0.9547483921051025, + "y_min": -0.992435872554779 +} +``` + +```text +$ git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a +- Note: the plan is marked `within_boundary: false` because the memory + validator treats `pysph/base/warp_*.py` literally, not as a glob. These files + are still the already-approved Python Warp prototype surface. + +## Visual aid + +| Stage | Acceleration ownership | Host/device transfer posture | +| --- | --- | --- | +| `compute_pressure_gradient` | Sets inviscid `au/av/aw` | Pushes only when requested | +| `compute_artificial_viscosity` | Adds viscosity to `au/av/aw` | Uses device arrays with `push=False` inside integrators | +| `wc_sph_leapfrog_step` | Recomputes forces before and after drift | Keeps repeated steps device-authoritative | + +## Risks + +- Constant `c0` is an approximation until Tait EOS and per-particle sound speed + are added. +- CubicSpline remains the active Warp kernel; the PySPH elliptical-drop example + uses Gaussian. +- Periodic support is still coordinate wrapping only, not minimum-image + neighbor interaction. + +## Unresolved questions + +- What correctness threshold should promote the Warp elliptical-drop runner + from smoke workload to benchmark? +- Should the next physics slice add Tait EOS/per-particle `cs` first, or XSPH + stabilization first? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-16T13:52:32 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-continuity-density-resolved-elliptical-drop.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-continuity-density-resolved-elliptical-drop.md new file mode 100644 index 000000000..60a53e514 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-continuity-density-resolved-elliptical-drop.md @@ -0,0 +1,124 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md +adrs: [] +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: lgtm +--- + +# Review: Warp Continuity-Density Resolved Elliptical Drop + +## Diff summary + +- Covers both the continuity-density parity plan and the preceding resolved + comparison plan: + `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md`. +- Added a Warp continuity-density WCSPH PEC-style path behind + `wc_sph_leapfrog_step(..., density_mode='continuity')`. +- Preserved the existing summation-density KDK path as the default behavior. +- Added device kernels/helpers to save reference state (`x0/y0/z0`, + `u0/v0/w0`, `rho0`) and apply staged updates to position, velocity, and + density from `arho`. +- Threaded `density_mode` through the Warp elliptical-drop runner and the + resolved PySPH Application comparison script. +- Added resolved `nx=100` CPU/Warp comparison summaries and side-by-side PNGs. +- Updated experiment, aspect, daily, current, and session-log memory. + +## Behavioral / numerical changes + +- `density_mode='continuity'` now matches the PySPH no-scheme Application's + density formulation: Tait EOS from current `rho`, `ContinuityEquation` + computes `arho`, and `rho` is advanced from saved `rho0` through PEC-style + stages. +- The original resolved comparison using summation density is explicitly + diagnostic only. It explained the previous 2225/4807-step Warp behavior: + summation-density refreshes created larger density/pressure excursions and + collapsed the adaptive force timestep. +- The continuity resolved `nx=100` run reached both requested checkpoint times: + PySPH CPU Application took `228.25765374601178` s / 1393 steps; Warp took + `30.008050591000938` s / 1804 steps; wall-time speedup was + `7.606547218180963x`. +- At `t=0.0038`, CPU-vs-Warp deltas were: + major axis `1.5947661098358878e-06`, minor axis + `2.1943316564909665e-06`, `rho_min -4.816405699936688e-06`, + `rho_max 8.755722542552746e-07`, kinetic energy + `-0.00043376772100600647`. +- The remaining step-count difference appears tied to timestep policy and the + resolved runner's explicit cap to hit output times, not to a density + formulation mismatch. + +## Validation + +```text +python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py +pass +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py -q -k 'pec_stage or continuity_mode' +2 passed, 2 warnings in 11.98s +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +38 passed, 2 warnings in 4.12s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --density-mode continuity +all_finite: true +density_mode: continuity +rho_min: 0.9995885491371155 +rho_max: 1.0004163980484009 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-continuity --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 228.25765374601178 s, 1393 steps +Warp continuity-density: 30.008050591000938 s, 1804 steps +speedup_wall_time: 7.606547218180963 +t=0.0008 major-axis delta: 3.6375168877000874e-08 +t=0.0038 major-axis delta: 1.5947661098358878e-06 +t=0.0038 rho_min delta: -4.816405699936688e-06 +``` + +```text +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +```text +git diff --check -- .ai/implementations/blast-from-the-past pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py +pass +``` + +## Visual aid + +- `resolved-nx100-continuity-t0p0008000.png` +- `resolved-nx100-continuity-t0p0038000.png` + +Visual inspection of +`.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-continuity-t0p0038000.png` +shows CPU and Warp particle clouds and exact ellipse overlays are visually +indistinguishable at the final checkpoint. + +## Risks / unresolved questions + +- The continuity path is PEC-style, while the public helper name remains + `wc_sph_leapfrog_step()` for continuity with earlier prototype wiring. This + is documented via `density_mode`. +- Warp still caps `dt` to land exactly on checkpoint times in the resolved + comparison runner; PySPH's output-time handling is not yet matched exactly. +- True periodic neighbor interactions remain out of scope for this slice. +- Raw HDF5/NPZ checkpoint dumps were generated locally and pruned from the + reviewable artifact set; summary JSON and PNG comparisons remain. +- Unrelated untracked `CODEBASE_UNDERSTANDING.md` remains untouched. + +## Reviewer verdict + +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-17T00:36:00 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-elliptical-drop-runner-smoke-ramp.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-elliptical-drop-runner-smoke-ramp.md new file mode 100644 index 000000000..bd20f1fcb --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-elliptical-drop-runner-smoke-ramp.md @@ -0,0 +1,141 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: plans/2026-06-16_warp-elliptical-drop-application-runner.md +adrs: [] +aspects_touched: [validation-benchmarks, gpu-nnps, warp-backend, particle-memory, host-integration] +host_files: [] +status: approved +--- + +# Review - Warp elliptical drop runner smoke ramp + +## Diff summary + +- Adds an application-style Warp elliptical-drop runner under the active + experiment tree. +- Adds a `run_correctness.sh` smoke wrapper. +- Adds an experiment document with expectations, success criteria, raw smoke + output, and ramp metrics. +- Updates current/aspect/daily/session memory with the finite smoke/ramp + results. +- Leaves generated `.npz` result files as local experiment artifacts, not + intended for commit unless explicitly requested. + +## Aspects touched and host files modified + +- `validation-benchmarks`: new runnable experiment and ramp table. +- `gpu-nnps`: exercises `UniformGridWarpNNPS` in a dynamics workload. +- `warp-backend`: exercises Warp kernels through an application-style runner. +- `particle-memory`: pulls final arrays once at the end of each run. +- `host-integration`: no PySPH `Application`/`Solver` integration yet. + +Host files modified: none. This slice only changes implementation memory and +experiment files. + +## Behavioral / numerical changes + +- No host package behavior changes. +- New runner creates the standard elliptical-drop circular particle patch and + initial velocity field, then advances it with: + +```text +UniformGridWarpNNPS +wc_sph_leapfrog_step +``` + +- This is a GPU state-evolution smoke/ramp workload, not a validated published + elliptical-drop benchmark. Current Warp physics still lacks Tait EOS, + artificial viscosity, XSPH, Gaussian kernel support, and adaptive timestep. + +## Tests / validation run + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 25.10 ms (cached) +Module pysph.base.warp_sph e548a6b load on device 'cuda:0' took 6.87 ms (cached) +{ + "all_finite": true, + "dt": 1e-05, + "kinetic_energy": 8078.22338525834, + "nx": 8, + "p_max": -0.01952648162841797, + "p_min": -166.61477661132812, + "particles": 204, + "radius_max": 0.9978743942869612, + "rho_max": 0.999951183795929, + "rho_min": 0.5834630727767944, + "steps": 2, + "time": 2e-05, + "x_max": 0.9480998516082764, + "x_min": -0.9231499433517456, + "y_max": 0.9518998861312866, + "y_min": -0.9268498420715332 +} +``` + +Ramp runs recorded in the experiment doc: + +```text +nx=12, particles=455, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=20, time=0.0001, all_finite=true +nx=24, particles=1808, steps=10, time=5e-05, all_finite=true +``` + +```text +$ git diff --check -- .ai/implementations/blast-from-the-past + +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a + +## Visual aid + +| Case | Particles | Steps | Time | Result | +| --- | ---: | ---: | ---: | --- | +| smoke nx=8 | 204 | 2 | 2e-05 | finite | +| ramp nx=12 | 455 | 5 | 5e-05 | finite | +| ramp nx=16 | 805 | 5 | 5e-05 | finite | +| ramp nx=16 | 805 | 20 | 0.0001 | finite | +| ramp nx=24 | 1808 | 10 | 5e-05 | finite | + +## Risks + +- The runner is not the full PySPH elliptical-drop application and should not + be compared against the analytical benchmark yet. +- Current pressure model can produce negative pressure with the prototype + isothermal EOS and incomplete WCSPH formulation. +- Generated `.npz` files are local artifacts and are not included in the + intended commit. + +## Unresolved questions + +- Should generated `.npz` experiment artifacts be committed or kept local? +- What ramp target should gate moving to artificial viscosity: larger `nx`, + longer time, or comparison to a PySPH baseline? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-16T13:25:52 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-repeated-step-leapfrog-checkpoint.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-repeated-step-leapfrog-checkpoint.md new file mode 100644 index 000000000..bcab66fbb --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-repeated-step-leapfrog-checkpoint.md @@ -0,0 +1,152 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: session-log LP-N +adrs: [] +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_nnps.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_nnps.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Warp repeated-step leapfrog checkpoint + +## Diff summary + +- Adds `update(push=False)` to Warp NNPS update paths so grids/caches can be + rebuilt from device-updated coordinates without pushing stale host + `ParticleArray` values over them. +- Adds Warp KDK leapfrog primitives: half/full kick, drift, periodic position + wrapping, and a minimal `wc_sph_leapfrog_step()`. +- Adds `push=False` controls to Warp summation-density and continuity helpers + for device-authoritative step loops. +- Adds correctness tests for device-coordinate NNPS refresh, direct + kick/drift/wrap behavior, and KDK WCSPH state against CPU references. +- Updates implementation memory and experiment expectations/results. + +## Aspects touched and host files modified + +- `warp-backend`: new Warp kernels and helper functions in + `pysph/base/warp_sph.py`. +- `gpu-nnps`: device-authoritative refresh in `pysph/base/warp_nnps.py`. +- `particle-memory`: avoids stale host-to-device pushes in repeated step loops. +- `validation-benchmarks`: focused tests and experiment doc updated. +- `host-integration`: no Application integration yet; boundary drift noted + below. + +Host files: + +- `pysph/base/warp_nnps.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_nnps.py` +- `pysph/base/tests/test_warp_sph.py` + +## Behavioral / numerical changes + +- Existing default `update()` behavior remains host-authoritative. +- New `update(push=False)` mode allows the Warp device arrays to be the source + of truth after device-side position updates. +- `wc_sph_leapfrog_step()` computes: + +```text +a_n <- WCSPH acceleration(x_n) +u_half <- u_n + 0.5*dt*a_n +x_np1 <- x_n + dt*u_half +wrap x_np1 into periodic bounds when requested +NNPS refresh from device x_np1 +a_np1 <- WCSPH acceleration(x_np1) +u_np1 <- u_half + 0.5*dt*a_np1 +``` + +- Periodic support in this change is position wrapping only. Minimum-image + neighbor distances and periodic cell lookup are not implemented here. + +## Tests / validation run + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +............................. [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +29 passed, 2 warnings in 5.82s +``` + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/run_correctness.sh +............................. [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +29 passed, 2 warnings in 3.05s +``` + +```text +$ git diff --check -- pysph/base/warp_nnps.py pysph/base/warp_sph.py pysph/base/tests/test_warp_nnps.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past + +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: yes +- Amendments log entry: `2026-06-16 - Added Python Warp prototype files and + focused Warp tests to the active implementation boundary for the repeated-step + checkpoint.` +- Note: plan `2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md` + was marked `within_boundary: false`; the boundary was amended during review + before commit. + +## Visual aid + +Comparison table, Mermaid diagram, generated chart, convergence plot, or one-line waiver. + +| Step | Before | After | +| --- | --- | --- | +| NNPS refresh after device drift | `update()` pushed host coordinates first | `update(push=False)` rebuilds from device coordinates | +| Integrator prototype | Euler only | Euler plus KDK leapfrog | +| Periodic support | none in step helper | position wrapping after drift | +| Validation | 26 focused tests | 29 focused tests plus wrapper run | + +## Risks + +- True periodic neighbor interaction is still incomplete: position wrapping is + not enough without minimum-image distances and periodic cell lookup. +- `wc_sph_leapfrog_step()` is still a prototype helper, not a generated PySPH + integrator or Application-level solver path. +- The remaining neighbor-cache sizing path still reads lengths to host. +- The Python Warp prototype files need a boundary amendment decision. + +## Unresolved questions + +- Should the next milestone be true periodic Warp NNPS, artificial viscosity, or + an Application-facing elliptical-drop runner? +- What exact acceptance threshold should gate an elliptical-drop comparison + against PySPH's analytical/post-process output? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-16T12:26:17 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-tait-eos-and-sound-speed.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-tait-eos-and-sound-speed.md new file mode 100644 index 000000000..fa67fe030 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-tait-eos-and-sound-speed.md @@ -0,0 +1,201 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md +adrs: [] +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: lgtm +--- + +# Review - Warp Tait EOS and Sound Speed + +## Diff summary + +- Added Warp float32/float64 Tait EOS kernels and `compute_tait_eos()`. +- Added per-particle sound speed `cs` handling for WCSPH force evaluation. +- Updated artificial viscosity to use `cij = 0.5*(d_cs + s_cs)` with a + constant-`c0` fallback when callers have not created `cs`. +- Added `eos='tait'` and `gamma` options to the WCSPH Euler/leapfrog helpers + while preserving `eos='isothermal'` as the compatibility default. +- Switched the Warp elliptical-drop runner default to Tait EOS with + `gamma=7.0` and included `cs` metrics/output. +- Updated experiment and memory artifacts with Tait smoke/ramp results. + +## Aspects touched and host files modified + +- Aspects: `warp-backend`, `gpu-nnps`, `particle-memory`, + `validation-benchmarks`, `host-integration`. +- Host files: + - `pysph/base/warp_sph.py` + - `pysph/base/tests/test_warp_sph.py` + +## Behavioral / numerical changes + +- Tait EOS now computes: + +```text +ratio = rho / rho0 +p = p0 + (rho0*c0*c0/gamma) * (ratio**gamma - 1) +cs = c0 * ratio**(0.5*(gamma - 1)) +``` + +- Artificial viscosity now uses pair-averaged sound speed: + +```text +cij = 0.5 * (d_cs[d_idx] + s_cs[s_idx]) +Pi_ij = (-alpha*cij*mu_ij + beta*mu_ij^2) * 2/(rho_i + rho_j) +``` + +- The low-level WCSPH helpers still default to isothermal EOS for existing + tests and callers. +- The elliptical-drop runner now defaults to `eos=tait`, `gamma=7.0`. +- This does not make elliptical drop production-ready. XSPH, Gaussian kernel + support or a kernel decision, adaptive timestep/CFL tracking, and PySPH + baseline comparison remain open. + +## Tests / validation run + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +................................ [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +32 passed, 2 warnings in 4.14s +``` + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 16.14 ms (cached) +Module pysph.base.warp_sph 37ca4ca load on device 'cuda:0' took 4.78 ms (cached) +{ + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "cs_max": 19.997066497802734, + "cs_min": 3.9725253582000732, + "dt": 1e-05, + "eos": "tait", + "gamma": 7.0, + "kinetic_energy": 8078.17389338273, + "nx": 8, + "p_max": -0.01954691670835018, + "p_min": -55.82748794555664, + "particles": 204, + "radius_max": 0.9978746006297383, + "rho_max": 0.9999511241912842, + "rho_min": 0.5834615230560303, + "steps": 2, + "time": 2e-05, + "x_max": 0.9480999112129211, + "x_min": -0.9231499433517456, + "y_max": 0.9518999457359314, + "y_min": -0.9268499612808228 +} +``` + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 16 --steps 5 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-tait-nx16-steps5.npz +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 15.20 ms (cached) +Module pysph.base.warp_sph 37ca4ca load on device 'cuda:0' took 3.32 ms (cached) +{ + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "cs_max": 19.99852752685547, + "cs_min": 5.072288990020752, + "dt": 1e-05, + "eos": "tait", + "gamma": 7.0, + "kinetic_energy": 7868.739071212255, + "nx": 16, + "p_max": -0.009822845458984375, + "p_min": -54.81636428833008, + "particles": 805, + "radius_max": 1.0018218256790075, + "rho_max": 0.9999754428863525, + "rho_min": 0.6329819560050964, + "steps": 5, + "time": 5e-05, + "x_max": 0.9452491998672485, + "x_min": -0.9825617671012878, + "y_max": 0.9547492861747742, + "y_min": -0.9924367666244507 +} +``` + +```text +$ git diff --check -- pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a +- Note: the plan is marked `within_boundary: false` because the memory + validator treats `pysph/base/warp_*.py` literally, not as a glob. These files + are still the already-approved Python Warp prototype surface. + +## Visual aid + +| Path | Pressure | Sound speed | Viscosity sound speed | +| --- | --- | --- | --- | +| `eos='isothermal'` | `p0 + c0^2*(rho-rho0)` | `c0` fallback | constant `c0` unless caller supplies `cs` | +| `eos='tait'` | Tait pressure | per-particle `cs` | `0.5*(d_cs+s_cs)` | +| elliptical-drop runner | Tait by default | written to output | pair-averaged `cs` | + +## Risks + +- Tait EOS fractional powers require positive density ratios. Current smoke + and ramp stay finite, but larger/longer runs still need guards or acceptance + thresholds. +- The runner's default metrics changed because it now uses Tait EOS instead of + isothermal EOS. +- Production elliptical drop still needs XSPH, Gaussian kernel support or an + accepted kernel decision, adaptive timestep/CFL logic, and baseline + comparison. + +## Unresolved questions + +- Should the next physics slice add XSPH first or Gaussian kernel support first? +- What exact PySPH output times/tolerances should gate the first + elliptical-drop benchmark comparison? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-16T17:25:35 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md new file mode 100644 index 000000000..22a099cbb --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md @@ -0,0 +1,223 @@ +--- +type: review +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md +adrs: [] +aspects_touched: [warp-backend, particle-memory, validation-benchmarks] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: lgtm +--- + +# Review - Warp XSPH, Gaussian, Adaptive DT, and Baseline Comparison + +## Diff summary + +- Added Gaussian kernel value/gradient support to Warp SPH equation kernels + with `kernel='cubic'` as the compatibility default and `kernel='gaussian'` + for elliptical-drop parity. +- Added `compute_xsph_correction()` and `leapfrog_drift_xsph()` so KDK + leapfrog can drift with `u + ax` after the first half-kick. +- Added `compute_wcsph_adaptive_timestep()` with device-side `dt_cfl` and + `dt_force` factors, device reductions, and a single scalar `dt` transfer. +- Updated `wc_sph_leapfrog_step()` with optional `xsph_eps`, `kernel`, + `adaptive_dt`, `cfl`, `dt_min`, `dt_max`, and `return_dt`. +- Updated the Warp elliptical-drop runner to default to Gaussian, expose XSPH + and adaptive-dt controls, record `dt_history`, and pull arrays only for final + checkpoint/metrics output. +- Added a CPU/Warp comparison script that writes CPU/Warp `.npz` outputs and a + side-by-side image. +- Updated experiment, aspect, daily, session, and current memory. + +## Aspects touched and host files modified + +- Aspects: `warp-backend`, `particle-memory`, `validation-benchmarks`. +- Host files: + - `pysph/base/warp_sph.py` + - `pysph/base/tests/test_warp_sph.py` +- Experiment/memory files: + - `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` + - `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md` + - `.ai/implementations/blast-from-the-past/current.md` + - `.ai/implementations/blast-from-the-past/aspects/*/context.md` + - `.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md` + - `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2227.md` + +## Behavioral / numerical changes + +- Gaussian uses PySPH's `Gaussian(dim)` convention: + +```text +W(q, h) = (1/sqrt(pi))^dim * h^-dim * exp(-q^2), q < 3 +dW/dq = -2*q*(1/sqrt(pi))^dim * h^-dim * exp(-q^2), q < 3 +``` + +- XSPH follows PySPH's leapfrog correction form: + +```text +ax_i += -eps * m_j * W_ij * 2/(rho_i + rho_j) * (u_i - u_j) +x_i += dt * (u_i + ax_i) +``` + +- Adaptive timestep factors follow the WCSPH momentum/integrator pattern: + +```text +dt_cfl_i = max_j(abs(HIJ * VIJ.XIJ / RIJ^2) + c0) +dt_force_i = au_i^2 + av_i^2 + aw_i^2 +dt = cfl * min(hmin/max(dt_cfl), sqrt(hmin/sqrt(max(dt_force)))) +``` + +- The runner now exercises Gaussian + Tait + artificial viscosity + XSPH + + adaptive dt in the smoke wrapper. Repeated stepping keeps particle state on + device; adaptive stepping reads back only the reduced scalar timestep, and + final full-array pulls are explicit output/plot checkpoints. +- The CPU comparison baseline is a PySPH-primitive baseline using + `LinkedListNNPS`, `Gaussian`, and matching equation formulas. It is not yet + the full PySPH `Application/Solver` production baseline. + +## Tests / validation run + +```text +$ python -m pytest pysph/base/tests/test_warp_sph.py -q +.................. [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +18 passed, 2 warnings in 3.50s +``` + +```text +$ bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +Warp 1.14.0 initialized: + CUDA Toolkit 12.9, Driver 13.2 + Devices: + "cpu" : "CPU" + "cuda:0" : "NVIDIA GeForce RTX 4060 Laptop GPU" (8 GiB, sm_89, mempool enabled) + Kernel cache: + /home/kunalp/.cache/warp/1.14.0 +Module pysph.base.warp_nnps b046253 load on device 'cuda:0' took 22.79 ms (cached) +Module pysph.base.warp_sph 1bd567e load on device 'cuda:0' took 9.35 ms (cached) +{ + "adaptive_dt": true, + "all_finite": true, + "alpha": 0.1, + "beta": 0.0, + "c0": 20.0, + "cfl": 0.25, + "dt_last": 9.999999747378752e-06, + "dt_max_used": 9.999999747378752e-06, + "dt_min_used": 9.999999747378752e-06, + "eos": "tait", + "gamma": 7.0, + "kernel": "gaussian", + "particles": 204, + "radius_scale": 3.0, + "rho_max": 0.9998562335968018, + "rho_min": 0.534595251083374, + "time": 1.9999999494757503e-05, + "xsph_eps": 0.5 +} +``` + +```text +$ python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --xsph-eps 0.5 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --dt-max 1.0e-5 --prefix comparison-smoke +{ + "cpu": { + "all_finite": true, + "dt_max_used": 1e-05, + "dt_min_used": 1e-05, + "kinetic_energy": 8078.179846214378, + "particles": 204, + "radius_max": 0.9978296023877065, + "rho_max": 0.9998561964891306, + "rho_min": 0.534595094929311, + "time": 2e-05 + }, + "warp": { + "all_finite": true, + "kinetic_energy": 8078.179766857993, + "particles": 204, + "radius_max": 0.9978295868060059, + "rho_max": 0.9998562335968018, + "rho_min": 0.534595251083374, + "time": 1.9999999494757503e-05 + } +} +``` + +```text +$ python -m py_compile pysph/base/warp_sph.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py +``` + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +.................................... [100%] +=============================== warnings summary =============================== +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:29: DeprecationWarning: Due to '_pack_', the 'APICLaunchParamRecord' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchParamRecord(ctypes.Structure): + +pysph/base/tests/test_warp_sph.py::test_warp_isothermal_eos_matches_cpu_and_pulls_pressure + /home/kunalp/.pqt_venv_e0b41259/lib/python3.14/site-packages/warp/_src/apic/types.py:49: DeprecationWarning: Due to '_pack_', the 'APICLaunchPtrLocation' Structure will use memory layout compatible with MSVC (Windows). If this is intended, set _layout_ to 'ms'. The implicit default is deprecated and slated to become an error in Python 3.19. + class APICLaunchPtrLocation(ctypes.Structure): + +-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html +36 passed, 2 warnings in 3.45s +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a +- Note: the plan is marked `within_boundary: false` because the memory + validator treats `pysph/base/warp_*.py` literally, not as a glob. These files + remain the already-approved Python Warp prototype surface. + +## Visual aid + +- Side-by-side comparison image: + `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png` + +| Case | Particles | rho_min | rho_max | radius_max | kinetic_energy | +| --- | ---: | ---: | ---: | ---: | ---: | +| CPU PySPH-primitive | 204 | 0.534595094929311 | 0.9998561964891306 | 0.9978296023877065 | 8078.179846214378 | +| Warp GPU | 204 | 0.534595251083374 | 0.9998562335968018 | 0.9978295868060059 | 8078.179766857993 | + +## Risks + +- The comparison baseline is not yet PySPH's full `Application/Solver` output. + It uses PySPH primitives and matching formulas for a short smoke comparison. +- Adaptive dt currently transfers one scalar timestep per step to Python + because Warp launches still need host scalar arguments. +- The Gaussian radius scale increases neighbor count relative to CubicSpline; + larger production runs should record timing and memory pressure. +- Periodic support is still position wrapping only. True periodic-neighbor + distances and cell lookup remain separate work. + +## Unresolved questions + +- What tolerance should gate the first full PySPH `Application/Solver` baseline + comparison at `t=0.0008` and `t=0.0038`? +- Should the next production run use `c0=1400.0` and `nx=40` immediately, or + ramp `c0`/`nx` separately to keep failure modes readable? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-16T23:04:00 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-17_million-particle-short-comparison.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_million-particle-short-comparison.md new file mode 100644 index 000000000..1f66ca697 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_million-particle-short-comparison.md @@ -0,0 +1,119 @@ +--- +type: review +date: 2026-06-17 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [validation-benchmarks, warp-backend, gpu-nnps] +host_files: [] +plan: none +adrs: [] +status: approved +verdict: "@prabhu: LGTM" +--- + +# Review - Million-Particle Short Comparison + +## Diff summary + +- Recorded a bounded million-particle, ten-step CPU/GPU elliptical-drop + comparison in the active Warp elliptical-drop experiment. +- Added compact JSON summary evidence for the run. +- Updated `current.md`, today's daily closeout, and a new session log. +- Recorded that the full million-particle GPU-only run was not launched after + the user clarified "no multi hour run". + +## Aspects + +- `validation-benchmarks` +- `warp-backend` +- `gpu-nnps` + +## Host files + +None. This review covers experiment/memory artifacts only. + +## Behavioral / numerical changes + +No host behavior changed. + +The new recorded benchmark result is: + +```text +nx=565 +particles=1,002,885 +steps=10 +fixed dt=3.732778967800475e-07 +PySPH CPU Application wall time: 57.48 s +Warp GPU wall time: 7.17 s +speedup_wall_time: 8.01673640167364x +``` + +Final-state deltas: + +```text +axis_x_abs: -1.2296967044633789e-07 +axis_y_abs: 4.773760275966765e-10 +rho_min: -9.119009991565008e-10 +rho_max: 1.4501548406542497e-08 +kinetic_energy: 8.523681572114583e-06 +``` + +The one-step adaptive GPU probe completed in `4.87 s` and stayed finite, but +the full `nx=565`, `tf=0.0076` GPU-only run was not launched. + +## Raw validation output + +```text +python pysph/examples/elliptical_drop_no_scheme.py --nx 565 --tf 0.000003732778967800475 --timestep 0.0000003732778967800475 --no-adaptive-timestep --n-damp 0 --pfreq 10 --fname million-pysph --directory .../million-cpu-gpu-10step/pysph --logfile '' --quiet +real 57.48 +``` + +```text +python .../warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .../million-cpu-gpu-10step/warp/million-warp.npz +real 7.17 +``` + +```text +python .../warp_elliptical_drop_runner.py --nx 565 --steps 1 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --adaptive-dt --cfl 0.3 --dt-min 1.0e-10 --dt-max 0.0000003732778967800475 --density-mode continuity --output .../full-nx565-gpu/adaptive-one-step-probe.npz +real 4.87 +all_finite: true +``` + +```text +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +```text +ps -ef | rg 'resolved_elliptical_drop_comparison|warp_elliptical_drop_runner|elliptical_drop_no_scheme.py' | rg -v rg + +``` + +## Risks + +- The committed evidence intentionally excludes the large raw HDF5/NPZ outputs. + The compact summary JSON is the committed benchmark evidence. +- The ten-step run is fixed-step and short by design. It is not a substitute + for a full-duration resolved million-particle physics result. +- The full-duration million-particle adaptive path likely needs optimization + before a non-multi-hour run is practical. + +## Unresolved questions + +- What optimization target should gate a full `nx=565`, `tf=0.0076` GPU-only + elliptical-drop run? +- Should future large-output artifacts be archived outside git with checksums? + +## Visual aid + +Waiver: no new plot was generated for this review because plotting 1,002,885 +particles would add bulky derived artifacts. The numerical summary is the +review evidence for this experiment checkpoint. + +## Verdict + +Approved by @prabhu with verbatim quote: + +```text +@prabhu: LGTM +``` diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-adaptive-timestep-policy-parity.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-adaptive-timestep-policy-parity.md new file mode 100644 index 000000000..1cbb2b179 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-adaptive-timestep-policy-parity.md @@ -0,0 +1,136 @@ +--- +type: review +date: 2026-06-17 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md +adrs: [] +aspects_touched: [warp-backend, particle-memory, validation-benchmarks, host-integration] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: lgtm +--- + +# Review: Warp Adaptive Timestep Policy Parity + +## Diff summary + +- Added scalar adaptive timestep policy controls to `wc_sph_leapfrog_step()`: + `adaptive_dt_scale` and `step_dt_max`. +- Applied those controls to both the summation-density KDK path and the + continuity-density PEC-style path after the device-reduced adaptive timestep + candidate is computed. +- Added a focused Warp SPH test for adaptive timestep scaling and current-step + capping. +- Updated the resolved elliptical-drop Application comparison runner with: + `--warp-timestep-policy {pysph,current}` and `--warp-dt-max`. +- Implemented PySPH-like `n_damp` damping in the resolved runner and used + output-time caps only for the current physical step. +- Updated experiment, aspect, daily, current, and session-log memory. +- Added resolved `nx=100` timestep-policy summary and side-by-side PNGs. + +## Aspects touched and host files modified + +- Aspects: `warp-backend`, `particle-memory`, `validation-benchmarks`, + `host-integration`. +- Host files: + - `pysph/base/warp_sph.py` + - `pysph/base/tests/test_warp_sph.py` +- Experiment/memory files: + - `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` + - `.ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md` + - `.ai/implementations/blast-from-the-past/current.md` + - `.ai/implementations/blast-from-the-past/aspects/*/context.md` + - `.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md` + - `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0824.md` + +## Behavioral / numerical changes + +- The resolved comparison can now run Warp with PySPH-like adaptive timestep + policy: early `n_damp` sine ramp, adaptive growth beyond the initial + timestep, and temporary output-time landing caps. +- Repeated particle state remains device-authoritative. The device still + computes/reduces adaptive timestep factors; Python only receives and applies + scalar timestep policy values. +- The old policy remains available as `--warp-timestep-policy current` for + diagnostic comparisons. +- Full resolved `nx=100` result: + - PySPH CPU Application: `233.97314716299297` s / 1393 steps. + - Warp continuity-density: `23.629107111992198` s / 1393 steps. + - Wall-time speedup: `9.901903870258701x`. + - CPU `dt_max`: `2.780917055777183e-06`; Warp `dt_max`: + `2.7813784981844947e-06`. + - Warp `dt_min`: `2.7459356128852786e-09`, an output-time landing step. +- At `t=0.0038`, CPU-vs-Warp deltas were: + major axis `-3.1258252297661215e-07`, minor axis + `1.3598666296354978e-06`, `rho_min -8.228944999855159e-07`, + `rho_max 9.947815438060559e-07`, kinetic energy + `-0.0004318240680731833`. + +## Validation + +```text +python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py +pass +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py -q -k 'adaptive_timestep_scale or adaptive_timestep_matches' +2 passed, 2 warnings +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +39 passed, 2 warnings in 4.83s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 8 --output-times 0.0008 --prefix timestep-policy-smoke-nx8 --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/timestep-policy-smoke --max-steps 100000 +PySPH CPU Application: 48 steps +Warp continuity-density: 48 steps +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-timestep-policy --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 233.97314716299297 s, 1393 steps +Warp continuity-density: 23.629107111992198 s, 1393 steps +speedup_wall_time: 9.901903870258701 +t=0.0008 major-axis delta: -5.596712788769054e-07 +t=0.0038 major-axis delta: -3.1258252297661215e-07 +t=0.0038 rho_min delta: -8.228944999855159e-07 +``` + +```text +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +```text +git diff --check -- .ai/implementations/blast-from-the-past pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py +pass +``` + +## Visual aid + +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0008000.png` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0038000.png` + +Visual inspection of the final checkpoint image shows CPU and Warp particle +clouds and exact ellipse overlays are visually indistinguishable at +`t=0.0038`. + +## Risks / unresolved questions + +- The timestep policy now mirrors the PySPH solver behavior closely enough for + exact step-count parity in this case, but it is implemented in the resolved + experiment runner, not as a general Solver integration. +- Adaptive timestep still transfers one scalar `dt` per step because launch + parameters are host scalars. +- True periodic neighbor interactions remain out of scope for this slice. +- Raw HDF5/NPZ checkpoint dumps were generated locally and pruned from the + reviewable artifact set; summary JSON and PNG comparisons remain. +- Unrelated untracked `CODEBASE_UNDERSTANDING.md` remains untouched. + +## Reviewer verdict + +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-17T09:07:00 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fp32-resolved-rerun.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fp32-resolved-rerun.md new file mode 100644 index 000000000..a7cc2afd8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fp32-resolved-rerun.md @@ -0,0 +1,91 @@ +--- +type: review +date: 2026-06-17 +user: @kunalpuri-prediqt +agent: codex +plan: .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0930.md +adrs: [] +aspects_touched: [particle-memory, validation-benchmarks, warp-backend] +host_files: [] +status: lgtm +--- + +# Review: Warp fp32 Resolved Rerun + +## Diff summary + +- Recorded an explicit Warp-only fp32 `nx=100` resolved elliptical-drop rerun. +- Added the compact summary JSON under the elliptical-drop experiment's + `fp32/` output directory. +- Updated experiment notes to clarify current precision behavior: + `WarpEllipticalDropRunner` builds host arrays as `float64`, but + `WarpDeviceHelper` casts floating device properties according to + `compyle.config.get_config().use_double`; on this machine it is `False`, so + the current Warp device execution path is fp32. +- Updated validation-benchmark context, current pointer, daily closeout, and a + session log for the fp32 precision check. + +## Behavioral / numerical changes + +- No host code changed. +- No runner behavior changed. +- The explicit fp32 Warp-only rerun used: + - `nx=100` + - `c0=1400.0` + - `density_mode=continuity` + - `warp_timestep_policy=pysph` + - checkpoints `0.0008,0.0038` +- Result: + - wall time `26.5732471299998` s + - steps `1393` + - average step time `0.019076272167982626` s + - `all_finite=true` at both checkpoints + - CPU-baseline-relative speedup, using the committed CPU time + `233.97314716299297` s: `8.804838415808415x` +- The previous committed Warp timing was `23.629107111992198` s / 1393 steps; + both runs use the same fp32 device path, so the difference is treated as + run-to-run/module-cache variance rather than a precision effect. + +## Validation + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-fp32-warp-only --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32 --skip-pysph-application --max-steps 10000000 +Warp fp32 wall time: 26.5732471299998 s +steps: 1393 +average step time: 0.019076272167982626 s +all_finite: true at both checkpoints +``` + +```text +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +```text +git diff --check -- .ai/implementations/blast-from-the-past +pass +``` + +## Visual aid + +Waived. This was a Warp-only timing/precision rerun with +`--skip-pysph-application`, so no side-by-side plot was generated. The retained +artifact is the compact summary JSON: + +```text +.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32/resolved-nx100-fp32-warp-only-summary.json +``` + +## Risks / unresolved questions + +- A true fp64 Warp comparison still needs either setting + `compyle.config.get_config().use_double = True` before creating the Warp + ParticleArray or adding an explicit runner precision flag. +- The raw fp32 checkpoint `.npz` files were pruned; only the summary JSON is + retained. +- Unrelated untracked `CODEBASE_UNDERSTANDING.md` remains untouched. + +## Reviewer verdict + +- Verdict, verbatim quote: + > @prabhu: LGTM - 2026-06-17T09:43:00 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fuse-neighbor-loop-equations-via-code-generation.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fuse-neighbor-loop-equations-via-code-generation.md new file mode 100644 index 000000000..580a4c0b0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fuse-neighbor-loop-equations-via-code-generation.md @@ -0,0 +1,159 @@ +--- +type: review +date: 2026-06-17 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md +adrs: [ADR-0003] +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks] +host_files: [pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_codegen.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Warp fuse neighbor-loop equations via code generation + +## Diff summary + +- New module `pysph/base/warp_codegen.py`: a dynamic Warp equation-group code + generator (ADR-0003). `WarpEquation` blocks declare source/dest/out arrays, + the shared per-pair quantities they need, and `initialize`/`loop`/`post_loop` + source snippets; `build_group_kernel` unions the signature, emits the shared + geometry once, inlines each block's loop into one neighbor traversal + accumulating into shared `_acc_` registers, materializes the templated + source (`linecache` + `exec`), wraps it with `wp.Kernel(func=..., source=...)`, + and caches by `(ordered equation signatures, dtype)`. +- `pysph/base/warp_sph.py`: + - imports the generator; adds `_WARP_DEVICE_FUNCS` (device `wp.func`s seeded + into generated kernels); + - adds four `WarpEquation` blocks (`ContinuityEquation`, `PressureGradient`, + `ArtificialViscosity`, `XSPHCorrection`) and `_WCSPH_CONTINUITY_BLOCKS`, + ported from the hand-written kernels with matching math; + - adds `compute_wcsph_accel_continuity` (single launch + one sync over the + fused group) and factors EOS into `_apply_wcsph_eos`; + - rewires `_wc_sph_pec_continuity_step` to EOS + one fused launch per + half-stage; removes the now-dead `_compute_wcsph_xsph` helper. +- Tests: new `pysph/base/tests/test_warp_codegen.py` (generator mechanism) and + two additions to `test_warp_sph.py` (fused-vs-separate parity; single fused + launch per stage with zero per-equation-helper calls). +- ADR-0003 accepted; plan, experiment, aspect, current, daily, session-log + memory updated; new summary folder `million-cpu-gpu-10step-fused-eqns/`. + +## Aspects touched and host files modified + +- Aspects: `warp-backend`, `gpu-nnps`, `validation-benchmarks`. +- Host files: `pysph/base/warp_codegen.py` (new), `pysph/base/warp_sph.py`, + `pysph/base/tests/test_warp_codegen.py` (new), + `pysph/base/tests/test_warp_sph.py`. + +## Behavioral / numerical changes + +- The continuity-density PEC half-stage now runs one generated neighbor-loop + kernel (continuity + pressure gradient + Monaghan viscosity + XSPH) instead of + four separate launches. Cache builds per step remain 2. +- The summation-density path, the Euler step, the standalone per-equation + helpers (kept as the trusted oracle), and the adaptive `_wcsph_dt_factors` + traversal are unchanged. +- Fusion follows PySPH group semantics: each block's `loop` runs per pair inside + one neighbor loop, accumulating into shared `_acc_` registers. This + reorders fp32 accumulation versus the two-kernel (pgrad-then-viscosity) path. +- Million-particle fixed-step (`nx=565`, 1,002,885 particles, 10 steps), + segmented per-step: equation launches/step 8 -> 2; equation-kernel time/step + ~0.064-0.088 s -> 0.011-0.014 s (~5-6x); step wall (steady) 0.098-0.138 s -> + 0.076-0.098 s (~25%); cache build ~0.034-0.046 s (now the dominant cost). +- 10-step headline wall is overhead/IO-bound (Warp init + 1M mgrid + 282 MB npz + write); warm samples 3.77-6.04 s (best 3.77 s = `15.25x` vs CPU 57.48 s) vs + the prior cache-reuse 4.51 s. Per-step compute is the meaningful metric. +- Parity vs prior separate-kernel Warp: positions/density/pressure identical to + fp32 print precision; kinetic-energy delta `-6.4e-09`. Vs CPU baseline: + `x ~1e-7`, `rho ~1e-8`, `kinetic_energy 8.5e-06` (same as cache-reuse). Finite. +- Adaptive `nx=100` resolved guard (Warp-only): `1393` steps (identical to + committed), all finite; shape deltas `~4.8e-07`, density `~1e-06`, + kinetic-energy `1.45e-04` vs committed Warp. Adaptive path now 2 traversals/ + stage (vs 5); Warp wall `23.63 s` -> `10.83-14.33 s` (cross-session, same step + count). + +## Tests / validation run + +```text +$ python -m py_compile pysph/base/warp_codegen.py pysph/base/warp_sph.py pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py +pass +``` + +```text +$ python -m pytest -q pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +47 passed, 2 warnings in 4.26s +``` + +```text +$ python .../warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .../million-cpu-gpu-10step-fused-eqns/warp/million-warp.npz +warm-cache wall samples: 6.04, 5.94, 4.89, 3.77, 4.05 s (best 3.77; cold first-compile 6.15) +segmented per-step: equation launches 2, equation time 0.011-0.014 s, step wall 0.076-0.098 s, cache 0.034-0.046 s +fused vs prior-Warp: positions/density/pressure identical to fp32 print precision; KE delta -6.43e-09 +fused vs CPU: x ~1e-7, rho ~1e-8, kinetic_energy 8.52e-06; all_finite True +``` + +```text +$ adaptive nx=100 resolved guard (Warp-only, pysph timestep policy) +steps 1393 (committed 1393), wall 10.83 / 14.33 s, all_finite True +deltas vs committed Warp: axis_major 4.77e-07, axis_minor 4.77e-07, rho_min -9.54e-07, rho_max 1.07e-06, kinetic_energy 1.45e-04 +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +```text +$ git diff --check -- pysph/base/warp_codegen.py pysph/base/warp_sph.py pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past +clean +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a (`pysph/base/warp_*.py` and + `pysph/base/tests/test_warp_*.py` are already in the boundary; the new + `warp_codegen.py` / `test_warp_codegen.py` match those globs). +- Amendments log entry: n-a + +## Visual aid + +Segmented per-step comparison (million-particle fixed step, steady state): + +```text + cache reuse (4 launches/stage) fused (1 launch/stage) +equation launches ######## (8) ## (2) +equation time/step ~0.064-0.088 s 0.011-0.014 s (~5-6x) +step wall/step 0.098-0.138 s 0.076-0.098 s (~25%) +cache build/step 0.036-0.052 s 0.034-0.046 s (now dominant) +``` + +## Risks + +- fp32 accumulation reordering shifts results ~1e-7 to ~1e-6 (KE ~1e-4 in the + adaptive run); within parity tolerances and far below the worst case. The + adaptive guard confirms identical step count (1393). +- Transitional duplication: the four equations exist both as generator blocks + and as hand-written kernels (kept as oracle / summation path). Migration is + the ADR-0003 follow-up. +- Generated-source ergonomics: a malformed snippet surfaces as a Warp compile + error; mitigated by the small documented vocabulary and the + generated-vs-oracle parity tests. +- The neighbor-cache build is now the dominant per-step cost (~45-50%); next + optimization target, not addressed here. + +## Unresolved questions + +- Should the standalone per-equation helpers and the summation/adaptive paths be + migrated onto the generator next (retiring the duplicated hand kernels), or + should the cache-build cost be attacked first now that it dominates? +- The 10-step headline wall is overhead/IO-bound and noisy; do we want a + longer-step headline (or the adaptive run) as the canonical performance metric? +- Raw million-particle HDF5/NPZ dumps are large; only summary JSON is retained. + Unrelated untracked `CODEBASE_UNDERSTANDING.md` remains untouched. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > LGTM diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-neighbor-traversal.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-neighbor-traversal.md new file mode 100644 index 000000000..28a9d1e45 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-neighbor-traversal.md @@ -0,0 +1,203 @@ +--- +type: review +date: 2026-06-18 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-neighbor-traversal.md +adrs: [ADR-0004] +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks] +host_files: [pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_codegen.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Warp grid-direct neighbor traversal for the WCSPH continuity hot path + +## Diff summary + +- `pysph/base/warp_codegen.py`: adds `neighbor_mode='flat'|'grid'` to + `generate_group_source` / `build_group_kernel` (mode is part of `_cache_key`, + exposed on `GroupKernel`). Grid mode emits the same fused body but replaces the + flat `starts/lengths/neighbors` loop with a direct uniform-grid cell-list walk: + - `_collect(neighbor_mode='grid')` forces `x,y,z` and `h` into the signature + (the support cutoff needs them); + - `_emit_geometry(..., phase=)` splits geometry into `pre` (`dx,dy,dz,rij2`) + and `post` (`rij,hij,grad,wij,vel`); `phase='all'` reproduces the original + flat ordering byte-for-byte; + - the grid body computes the destination cell, iterates the 3x3(x3) block with + the same in-bounds + `cid` checks as `_grid_neighbor_lengths`, applies the + support cutoff `rij2 < (radius_scale*h_i)^2 or rij2 < (radius_scale*h_j)^2`, + and runs the post geometry + each block's `loop` inside the cutoff guard; + - `_reindent` shifts geometry/snippets to the deeper grid nesting; + `initialize`/`post_loop` stay outside the loop, unchanged. +- `pysph/base/warp_sph.py`: + - `_grid_launch_args(nnps, src_index, dtype)` binds the cell list (from + `_build_grid`, cached per `update()`) + bounds + `radius_scale` in signature + order; + - `compute_wcsph_accel_continuity` grows `neighbor_mode='grid'` (default): a + single grid-direct launch, no flat cache; + - hand-written `_wcsph_dt_factors_grid_{f32,f64}` mirror the flat CFL + dt-factors with the cell-block walk + cutoff; `compute_wcsph_adaptive_timestep` + gains `neighbor_mode` (default `'flat'` -- summation path unchanged) and the + continuity step passes `'grid'`; + - `_wc_sph_pec_continuity_step` drops both `build_neighbor_cache_gpu` calls and + builds only the grid. +- Tests: `test_warp_codegen.py` adds grid cache-distinct + forced-geometry and a + single-cell numeric grid parity test; `test_warp_sph.py` adds grid-vs-flat + fused parity and repurposes the old cache-reuse test to assert the continuity + path builds zero flat caches (and that the grid is consulted). +- Docs: ADR-0004 (Accepted), plan, decision graph, experiment.md + summary + folder `million-cpu-gpu-grid-direct/`, aspect contexts, current, daily, + session log. New benchmark helper `profile_grid_direct_neighbors.py`. + +## Aspects touched and host files modified + +- Aspects: `gpu-nnps` (primary), `warp-backend`, `validation-benchmarks`. +- Host files: `pysph/base/warp_codegen.py`, `pysph/base/warp_sph.py`, + `pysph/base/tests/test_warp_codegen.py`, `pysph/base/tests/test_warp_sph.py`. + +## Behavioral / numerical changes + +- The WCSPH continuity-density PEC path no longer materializes a flat CSR + neighbor list. Both neighbor consumers (fused equations + adaptive CFL + dt-factors) walk the cell list directly. Per half-stage: `{grid build + 2 + build-traversals + readback + alloc + 1 consume}` -> `{grid build + 1 + grid-direct traversal}`. +- The grid-direct loop applies the same support cutoff as + `build_neighbor_cache_gpu` / `_grid_neighbor_lengths` (`radius_scale*h` on i + and j; self-pair j==i included, contributing zero via the `rij>1e-12`/`grad` + guards), so it visits exactly the flat list's neighbor set. +- Only the neighbor *visitation order* changes (cell order vs CSR order), which + reorders fp32 sums ~1e-7. The per-pair math and block order are identical to + the flat fused kernel; the CFL factor is an order-independent `max`, dt_force + is neighbor-independent. +- Retained on the flat path (unchanged): the summation-density path, the + per-equation oracle helpers, host `get_nearest_particles`, + `compute_neighbor_sum`, the flat `_wcsph_dt_factors`, and + `compute_wcsph_adaptive_timestep`'s default (`'flat'`). +- Million-particle fixed-step (`nx=565`, 1,002,885 particles, segmented, 2 + warmup discarded): `build_neighbor_cache_gpu` called 0 times on the continuity + path; cache-build term ~0.034-0.046 s/step -> 0; grid build ~0.0004-0.0007 s; + equation kernel 0.011-0.014 -> 0.023-0.025 s/launch (absorbs the cutoff + traversal); step wall (steady) 0.076-0.098 -> 0.059-0.064 s (~25-35% lower); + KE delta vs flat fused `-1.99e-06`, all finite. +- Adaptive `nx=100` resolved guard (Warp-only, pysph timestep policy): `1393` + steps (identical to committed), all finite; deltas vs committed Warp + axis_major `2.4e-07`, axis_minor `6.6e-07`, rho `~9e-07`, kinetic_energy + `1.19e-04` (relative `1.5e-08`); Warp wall `7.82 s` (committed `23.63 s`). + +## Tests / validation run + +```text +$ python -m py_compile pysph/base/warp_codegen.py pysph/base/warp_sph.py pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py +pass +``` + +```text +$ python -m pytest -q pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +50 passed, 2 warnings +``` + +```text +$ PYTHONPATH=.ai/.../2026-06-16_warp-elliptical-drop-runner \ + python .ai/.../profile_grid_direct_neighbors.py --nx 565 --steps 12 --warmup 2 +flat_cache_builds_total 0; grid_builds/step 2.0; equation launches/step 2 +step_wall_s steady [0.059156, 0.063873]; grid_build_s steady [0.000379, 0.000692] +equation_launch_s steady/launch [0.022992, 0.025071]; kinetic_energy 7854.1276; all_finite True +``` + +```text +$ adaptive nx=100 resolved guard (Warp-only, pysph timestep policy, grid-direct) +steps 1393 (committed 1393), warp wall 7.82 s, all_finite True +deltas vs committed Warp: axis_major 2.384e-07, axis_minor 6.557e-07, +rho_min -8.941e-07, rho_max 2.384e-07, kinetic_energy 1.19e-04 (rel 1.5e-08) +``` + +Fresh same-session CPU-vs-Warp headlines (no reused numbers; CPU = +single-threaded PySPH Cython Application, Warp = grid-direct on RTX 4060 fp32): + +```text +$ nx=100 resolved (real PySPH Application vs Warp, adaptive, identical 1393 steps) +CPU 160.10 s (0.1149 s/step) | Warp 6.78 s (0.00487 s/step) | speedup 23.6x + +$ 1M particles, 100 fixed steps (n_damp=0, identical dt both sides) +CPU 344.50 s (3.445 s/step) | Warp 8.37 s total (2.39 setup + 5.98 step; 0.0598 s/step) +speedup 41.2x wall / 57.6x per-step | KE rel delta 1.6e-09 | all_finite True +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +```text +$ git diff --check -- pysph/base/warp_codegen.py pysph/base/warp_sph.py pysph/base/tests .ai/implementations/blast-from-the-past +clean (to be re-run before commit) +``` + +## Adversarial pre-review + +Before this review, a 6-dimension adversarial workflow (membership parity, +arg-binding order, codegen indentation, flat-path regression, edge cases, +numerical/fp32) reviewed the diff; each finding was independently verified by a +skeptic. Result: 1 confirmed finding (a nit), 0 others. + +- Confirmed nit: defaulting `compute_wcsph_adaptive_timestep` to + `neighbor_mode='grid'` had silently re-routed the *summation-density* leapfrog + step's adaptive-dt to grid (verified numerically benign -- identical + membership, order-independent max), but that path is out of ADR-0004's stated + scope. **Addressed**: the helper now defaults to `'flat'` (summation path + byte-identical to commit `429fa23e`) and the continuity step passes `'grid'` + explicitly. Suite re-run green (50 passed). + +## Boundary amendment + +- implementation.md boundary section: n-a (`pysph/base/warp_*.py` and + `pysph/base/tests/test_warp_*.py` are already in the boundary). +- Amendments log entry: n-a + +## Visual aid + +Segmented per-step comparison (million-particle fixed step, steady state): + +```text + flat fused (ADR-0003) grid-direct (ADR-0004) +flat cache builds/step ## (2) . (0) +flat cache build/step ~0.034-0.046 s 0 (removed) +grid build/step (inside cache build) ~0.0008-0.0014 s +equation time/step 0.011-0.014 s 0.046-0.050 s (absorbs traversal) +neighbor traversals/half ### (count+fill+read = 3) # (1: cutoff walk) +step wall/step 0.076-0.098 s 0.059-0.064 s (~25-35% lower) +``` + +## Risks + +- fp32 visitation-order change shifts sums ~1e-7 (KE ~1e-4 absolute / ~1.5e-8 + relative in the adaptive run); within parity tolerances. The adaptive guard + confirms identical step count (1393). +- Grid-direct iterates every occupant of the 27 cells and applies the cutoff per + pair; the flat path applied it once at build. The profile is the arbiter: the + removed build/readback/alloc outweighs the cutoff revisit, netting ~25-35% + lower per-step wall, though the equation kernel itself is ~2x slower per + launch. +- Grid mode requires a grid-capable NNPS (`_build_grid`/`_bounds`). + `UniformGridWarpNNPS` is the only NNPS on the WCSPH path; a `BruteForceWarpNNPS` + would raise on grid mode, but that path is not reachable from the WCSPH steps. +- Transitional duplication: `_wcsph_dt_factors` now exists in flat and + grid-direct forms. Consistent with ADR-0003; the generator migration absorbs + both. + +## Unresolved questions + +- Should the summation-density path also move to grid-direct (it currently still + builds a flat cache via summation density)? Deferred; the ADR-0004 follow-up + evaluates narrowing `build_neighbor_cache_gpu` to the host-query/oracle path. +- The equation kernel is now ~2x slower per launch (it does the cutoff walk). + Worth exploring a one-pass count-free CSR or shared-memory cell cooperation if + the neighbor traversal becomes the next bottleneck. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > LGTM diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-summation-step-paths.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-summation-step-paths.md new file mode 100644 index 000000000..815484b00 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-grid-direct-summation-step-paths.md @@ -0,0 +1,89 @@ +--- +type: review +date: 2026-06-18 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-18_warp-grid-direct-summation-step-paths.md +adrs: [ADR-0004] +aspects_touched: [warp-backend, gpu-nnps] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Grid-direct the summation Euler/KDK step paths + +## Diff summary + +- `pysph/base/warp_sph.py`: + - The five standalone equation helpers (`compute_summation_density`, + `compute_continuity`, `compute_pressure_gradient`, + `compute_artificial_viscosity`, `compute_xsph_correction`) gain + `neighbor_mode='flat'` (default), threaded to `_run_equation_group`. Flat + default keeps the oracle/cross-array tests and `compute_neighbor_sum` + unchanged. + - `_compute_wcsph_acceleration` (summation KDK) and `wc_sph_euler_step` now + pass `neighbor_mode='grid'` to summation density, pressure gradient, + viscosity (accumulate), and the continuity-mode branch. + - The summation branch of `wc_sph_leapfrog_step` passes `neighbor_mode='grid'` + to its `compute_wcsph_adaptive_timestep` and `compute_xsph_correction` calls. + - No fusing: the pressure-gradient(overwrite) -> viscosity(add) composition is + preserved; the only numerical change is neighbor visitation order. +- Tests: `test_warp_summation_step_paths_build_no_flat_neighbor_cache` asserts + the summation Euler and KDK leapfrog steps build zero flat caches (and consult + the grid). + +## Behavioral / numerical changes + +- All device step paths are now grid-direct (continuity was already; this adds + the summation Euler + KDK leapfrog paths). `build_neighbor_cache_gpu` is now + used only by the host `get_nearest_particles` query API, `compute_neighbor_sum`, + and the flat-mode oracle/cross-array tests. +- The summation-density grid cutoff (`radius_scale*h`) matches the flat build's + neighbor set, so rho and accelerations are unchanged apart from fp32 + visitation-order reorder (~1e-7). The continuity PEC path is untouched. + +## Tests / validation run + +```text +$ python -m pytest -q pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +53 passed, 2 warnings + +(summation Euler + KDK leapfrog CPU-parity tests pass under grid-direct with no +tolerance changes; new no-flat-cache assertion passes.) +``` + +```text +$ continuity adaptive nx=100 guard (unchanged path, sanity) +steps 1393 (identical), KE 7797.7071, finite +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Adversarial pre-review + +A 4-dimension workflow (accel composition, neighbor membership, grid lifecycle, +flat-default preservation), each finding verified by an independent skeptic. +Result: **0 confirmed, 0 raised** -- clean diff. + +## Risks + +- fp32 visitation-order shift on the summation parity tests; bounded (~1e-7) and + within the existing 1e-5 tolerances (no loosening required). +- Grid mode requires a grid-capable NNPS (`UniformGridWarpNNPS`); the WCSPH step + paths only ever use that NNPS. + +## Unresolved questions + +- `build_neighbor_cache_gpu` could now be narrowed further or moved behind the + host-query API module; deferred (it still legitimately backs the oracle tests, + cross-array queries, and `compute_neighbor_sum`). + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > LGTM diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md new file mode 100644 index 000000000..d915ef6d5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md @@ -0,0 +1,135 @@ +--- +type: review +date: 2026-06-18 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md +adrs: [ADR-0003] +aspects_touched: [warp-backend, gpu-nnps] +host_files: [pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_codegen.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Migrate remaining neighbor-loop kernels onto the generator (ADR-0003 follow-up) + +## Diff summary + +- `pysph/base/warp_codegen.py`: + - `accumulate_outputs` option on `generate_group_source`/`build_group_kernel` + (part of `_cache_key`): seeds each `_acc_` from the existing + `d_[i]` instead of zero, so a group adds to (read-modify-writes) the + destination arrays. Needed for the standalone artificial-viscosity term, + which composes onto a prior pressure-gradient acceleration. + - Deterministic kernel `func_name`: derived from an md5 of the structural + cache key instead of `len(_KERNEL_CACHE)`. The old call-order-dependent name + changed the generated source whenever the build order shifted, defeating + Warp's on-disk kernel cache and forcing a full cold recompile every session. +- `pysph/base/warp_sph.py`: + - New blocks `SummationDensity` (overwrite `rho`) and `WcsphCflFactor` + (`dt_cfl` = neighbor `wp.max` reduction of `|hij*(vij.xij)/rij^2| + c0`; + `dt_force` = `au^2+av^2+aw^2` in `post_loop`). + - `_run_equation_group(...)`: shared launcher binding inputs in the + generator's canonical order, flat or grid, with optional + `accumulate_outputs`; self- and cross-array (`src != dst`). + - Repointed `compute_summation_density`, `compute_continuity`, + `compute_pressure_gradient`, `compute_artificial_viscosity` (accumulate), + `compute_xsph_correction`, and `compute_wcsph_adaptive_timestep` onto the + generator; refactored `compute_wcsph_accel_continuity` onto the shared + launcher. + - Retired the ~14 duplicated hand `@wp.kernel`s (`_summation_density`, + `_continuity`, `_pressure_gradient`, `_artificial_viscosity`, + `_xsph_correction`, `_wcsph_dt_factors{,_grid}`, f32+f64). EOS, integrator, + and dt init/reduce/finalize reduction kernels are unchanged. + - Fail-fast guards: the repointed helpers reject a non-canonical + `out_prop`/`out_props` (the generated path writes the block's canonical + array names) instead of silently writing the wrong array. +- Tests: `test_accumulate_outputs_adds_to_existing_output` (codegen); + `test_warp_equation_helpers_reject_custom_output_names` (guards); + `test_warp_fused_accel_matches_separate_helpers` re-commented as a + fusion-consistency check (both sides now generator-backed). + +## Behavioral / numerical changes + +- Consolidation: the generator is now the single source for every neighbor-loop + kernel. The summation path's composition is preserved exactly (pressure + gradient overwrites `au`, viscosity adds via `accumulate_outputs=True`, then + continuity). The continuity hot path uses the same fused grid kernel as + before (byte-identical source) -- perf-neutral. +- Numerically the single-block generated groups reproduce the retired hand + kernels; deltas stay at fp32 scale, guarded by the existing per-equation + CPU-reference and cross-array tests. +- Deterministic naming: the generated source is now stable across processes + (verified: identical `src_md5` in two independent interpreters). Each kernel + cold-compiles once per machine, then loads from Warp's disk cache (~20 ms). + +## Tests / validation run + +```text +$ python -m py_compile pysph/base/warp_codegen.py pysph/base/warp_sph.py +pass + +$ python -m pytest -q pysph/base/tests/test_warp_codegen.py pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +52 passed, 2 warnings +``` + +```text +$ adaptive nx=100 resolved guard (Warp-only, pysph timestep policy) +steps 1393 (committed 1393), all_finite True +kinetic_energy 7797.7070 (relative delta ~8e-9 vs committed), shape/density deltas at fp32 scale + +$ million fixed-step segmented profile (nx=565, continuity path) +flat_cache_builds 0; steady step-wall floor 0.059 s (== prior grid-direct); KE identical (7854.1276); finite +``` + +```text +$ disk-cache fix verification +cross-process generated-source md5 identical (cfl-grid, continuity-grid) +warm guard rerun: both generated modules load (cached) ~20 ms; no recompile (was a one-time 102-184 s cold compile) +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Adversarial pre-review + +A 6-dimension workflow (accumulate semantics, I/O contract, CFL-factor block, +launch binding, deterministic naming, completeness), each finding verified by an +independent skeptic. Result: 1 confirmed (minor), 1 dismissed (informational: +the harmless `dt_force` double-write). + +- Confirmed minor: the repointed helpers kept `out_prop`/`out_props` params used + for ensure/push/return, but the generated kernel writes only the block's + canonical names, so a non-default name would silently write zeros (currently + unreachable -- no caller uses non-defaults). **Addressed**: fail-fast guards + + a test. Signatures preserved per the plan. +- The deterministic-naming latent bug (disk-cache thrash) was surfaced by the + perf dimension while investigating a 184 s cold compile and fixed in this + slice. + +## Risks + +- Additive vs overwrite: viscosity must use `accumulate_outputs=True`; the + pressure-gradient-then-viscosity composition is guarded by + `test_warp_artificial_viscosity_matches_cpu_and_adds_to_acceleration` and the + fused-vs-separate consistency test. +- fp32 single-block-group vs hand-kernel ordering ~1e-7; within parity + tolerances (adaptive guard keeps 1393 steps). +- The large fused grid kernel has a slow one-time cold compile (~100-180 s); + now amortized by the deterministic-name disk cache (one-time per machine). + +## Unresolved questions + +- Should the summation Euler/KDK path be fused + grid-directed too (the separate + "grid-direct everywhere" follow-up), now that all its equations are generator + blocks? +- Should `build_neighbor_cache_gpu` be narrowed to just the host + `get_nearest_particles` query API now that the device paths are generator-backed? + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > LGTM diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-periodic-minimum-image-distance.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-periodic-minimum-image-distance.md new file mode 100644 index 000000000..87428fa12 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-18_warp-periodic-minimum-image-distance.md @@ -0,0 +1,105 @@ +--- +type: review +date: 2026-06-18 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-18_warp-periodic-minimum-image-distance.md +adrs: [ADR-0004] +aspects_touched: [gpu-nnps, warp-backend, validation-benchmarks] +host_files: [pysph/base/warp_codegen.py, pysph/base/warp_nnps.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_codegen.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Periodic minimum-image neighbor distance (grid-direct path) + +## Diff summary + +- `pysph/base/warp_codegen.py`: `generate_group_source`/`build_group_kernel` + gain `periodic=False` (grid only; in the cache key only when True, so + non-periodic kernels keep their exact name/source/disk-cache). Periodic grid + kernels add `box_lx/ly/lz` + `periodic_x/y/z` runtime int flags; the cell walk + wraps the cell index per periodic dim (`((ix%nx)+nx)%nx`) else bounds-checks; + `_emit_geometry` applies minimum image (`dx -= box_lx*wp.round(dx/box_lx)`) per + periodic dim before `rij2`/cutoff. +- `pysph/base/warp_nnps.py`: `UniformGridWarpNNPS.set_periodic_box(bounds)`; + `_compute_bounds_and_cell_size` tiles periodic dims (cubic box; `cell_size = + L/floor(L/cell_min)`, requires `floor(L/cell_min) >= 3`) and stores box + lengths + flags. **The cell-id binning kernels (`_cell_ids_counts_{f32,f64}`) + now wrap (not clamp) the cell index in periodic dims**, so out-of-box source + positions bin into their periodic image cell, consistent with the wrapped + walk. +- `pysph/base/warp_sph.py`: `_run_equation_group` detects periodicity from + `nnps._bounds` (grid mode) and builds the periodic variant + passes the box + via `_grid_launch_args(periodic=True)`. +- Tests: codegen periodic (compiles, distinct, flat+periodic rejected); a + CPU minimum-image parity test with an **out-of-box** source particle in the + `nx=4` clamp-bug regime; a periodic-lattice uniformity (no boundary + deficiency) test; a guard test (too-small box + missing min/max raise). + +## Behavioral / numerical changes + +- New capability: correct periodic neighbors (wrapped cell walk + minimum-image + distance) for the grid-direct WCSPH paths -- enables periodic benchmarks + (Taylor-Green etc.). The elliptical-drop (free-surface) path is unaffected. +- Non-periodic path byte-identical (periodic only engaged when a box is set; + binning clamps when the dim is non-periodic). Continuity adaptive guard + unchanged (`1393` steps, 0 recompiles -- disk cache intact). + +## Tests / validation run + +```text +$ python -m pytest -q test_warp_codegen.py test_warp_sph.py test_warp_nnps.py +57 passed, 2 warnings + +periodic summation density vs CPU minimum-image (incl. out-of-box x=1.08, nx=4): + max abs diff 5.51e-06; the out-of-box image is found by the in-box query. +periodic uniform lattice: std(rho) < 1e-4*mean, mean ~ rho0 (no boundary deficiency). +non-periodic regression: all prior tests pass; continuity guard 1393 steps, 0 recompiles. +``` + +## Adversarial pre-review (and fixes) + +A 5-dimension workflow (min-image correctness, cell-walk wrap, NNPS tiling, +non-periodic preservation, launch binding), each finding skeptic-verified. +Result: **5 confirmed (1 major, 1 minor, 3 nits)** -- all addressed: + +- **MAJOR (fixed)**: the source-particle binning kernel used `wp.clamp`, not a + periodic wrap, so out-of-box source positions were mis-binned into the edge + cell and the wrapped walk missed in-support periodic neighbors (reachable via + the public API with un-wrapped positions, and the first `push=True` step + before `wrap_periodic`). Fixed by wrapping the cell index in periodic dims in + `_cell_ids_counts_{f32,f64}`; added the out-of-box regression test (verified + it now matches the CPU min-image reference to 5.5e-06). +- **MINOR (fixed)**: a box narrower than `2*support` (support > L/2) was silently + accepted via a `max(3, ...)` clamp, making minimum-image invalid. Replaced + with a hard `floor(L/cell_min) >= 3` requirement that raises a clear error. +- **NIT (fixed)**: `periodic_in_x` without `xmin/xmax` raised a bare `KeyError`; + now a clear `ValueError`. Guard test added. +- **NIT (fixed)**: the equal-length check compared post-floor effective cell + sizes; now compares the raw periodic lengths. +- **NIT (no action)**: stored `_bounds` `ymin/zmin` differ from baseline for + dims below `self.dim` -- dormant/non-behavioral (flagged for transparency). + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Risks + +- MVP supports equal-length (cubic) periodic dims only; unequal lengths raise. + Non-cubic periodic boxes / per-dim cell sizes are a follow-up. +- `wp.round` at exactly half-box is a measure-zero tie; bounded. +- fp32 visitation/min-image ~1e-6; within parity tolerances. + +## Out of scope + +- Ghost-particle approach; periodic support for the flat host-query path; + non-cubic periodic boxes; cross-array periodic. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > LGTM diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky.md new file mode 100644 index 000000000..2a8ef3c13 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky.md @@ -0,0 +1,223 @@ +--- +type: review +date: 2026-06-19 +user: @kunalpuri-prediqt +agent: claude +plan: .ai/implementations/blast-from-the-past/plans/2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md +adrs: [ADR-0005] +aspects_touched: [warp-backend, validation-benchmarks, gpu-nnps] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_codegen.py] +status: approved +--- + +# Review: Warp 3D dam-break (Lobovsky no-obstacle) + +## Case + +A 3D dam break (Lobovsky et al. 2014, no-obstacle variant): a rectangular water +column held in a closed container is released at `t=0` and collapses under +gravity, a surge front runs along the floor and up the far wall. WCSPH with the +WendlandQuintic kernel, Tait equation of state, Monaghan artificial viscosity, +and XSPH; one solid **wall** particle array held fixed, its pressure enforced by +`TaitEOSHGCorrection` (clamp `rho>=rho0`). Reference: PySPH's shipped +`dam_break_3d_lobovsky.py` (`WCSPHScheme` + `EPECIntegrator`, fp64 CPU). Domain +~5.367 x 0.5 x 1.5 m, fluid column 2.0 x 0.5 x 1.0 m, `rho0=1000`, `gamma=7`, +`alpha=0.25`, `c0~32.85`, `gz=-9.81`. + +## Resolution & particle counts + +- Tier-1 (hand-rolled CPU parity), `dx=0.15`: **234 fluid + 1809 wall**. +- Tier-2 (real Application), `dx=0.12`: **512 fluid + 2647 wall**. +- Smoke / perf+snapshot, `dx=0.10` / `dx=0.08`: 1000 fluid + 3824 wall / + **1800 fluid + 5592 wall = 7392 total**. +- Reference production resolution is `dx=H/30` (finer; not run here -- correctness + uses coarser, bounded resolutions). + +## Performance (Warp fp32 vs default PySPH CPU) + +Same case to the same physical time `tf=0.4 s` at `dx=0.08` (7,392 particles), +RTX 4060 Laptop fp32 vs single-threaded PySPH Cython fp64. CPU = `app.solve()` +loop only (setup/compile excluded); Warp = GPU stepping loop only. Both use their +native adaptive dt + `n_damp`. Reproduce: `perf_and_snapshot_dam_break_3d.py` +(writes `cpu-vs-warp-perf.json`). + +| | steps to t=0.4 | wall | per-step | +| --- | --- | --- | --- | +| PySPH CPU (fp64) | 461 | 6.01 s | **13.0 ms** | +| Warp GPU (fp32) | 460 | 3.92 s | **8.5 ms** | + +- **~1.53x faster** on the GPU -- per-step and total wall now agree because the + two adaptive-dt schedules match (460 vs 461 steps to the same `tf`). +- Step-count parity required a runner fix: the reference's + `dt = 0.25*h0/(1.1*c_max)` is only the *initial/seed* dt -- PySPH's + `Integrator.compute_time_step` returns the CFL-limited `cfl*dt_min` with **no + clamp to the seed**, so its dt grows to ~9.2e-4. The runner originally capped + Warp's adaptive dt at that seed (~5.3e-4), forcing ~1.7x more, smaller steps + (775 vs 461); `dt_max` now defaults to uncapped (CFL + `n_damp` govern, like the + reference), so Warp lands at 460 steps. +- This is the expected **small-N regime**: at ~7k particles the GPU is + launch/grid-build-overhead bound, so ~1.5x is modest. + +### At ~1M particles (the meaningful large-N comparison) + +`bench_1M_and_3d_snapshot.py` at `dx=0.0108` (**1,014,072 particles**), per-step +throughput over a fixed step count (a *developed* 1M run to a physical `tf` is +multi-hour on the CPU, so it is intentionally not run -- matching how the +committed 1M elliptical comparison was measured): + +| >1M particles | per-step | particle-steps/s | vs CPU | +| --- | --- | --- | --- | +| PySPH CPU (fp64) | 4.5-5.0 s | ~0.21 M | 1x | +| Warp fp32, single-block | 0.415 s | 2.45 M | ~10.9x | +| Warp fp32, **fused** | **0.337 s** | **3.01 M** | **~13-15x** | + +- **~13-15x faster per step at >1M** (CPU per-step has ~10-20% run-to-run + variance on the laptop, 4.2-5.0 s/step; the Warp numbers are stable). The GPU + advantage scales steeply with N: 1.53x at 7k -> ~14x at >1M. (The 3D snapshot + above is a companion run at 999,975 particles, `dx=0.011` -- same scale.) +- **The fluid acceleration+density blocks are now fused** (pressure + Monaghan + AV + continuity in one kernel per source -- one neighbour walk / one per-pair + geometry computation instead of three), which made the Warp step **1.23x** + faster (0.415 -> 0.337 s/step) over the initial additive single-block + composition. XSPH (fluid-only) and wall continuity (fluid->wall) stay separate + (heterogeneous source/destination sets), so the step is still not a single + fused kernel like the single-array elliptical drop (57.6x at 1M); closing the + rest of that gap would require fusing across the heterogeneous source sets. + See also the committed cross-GPU sweep (414x per-step at 1M on an RTX PRO 6000). It is lower than the elliptical drop's 57.6x at + 1M on this same 4060 because the dam-break step is **not fused**: it issues ~18 + separate equation launches per step (continuity + pressure + AV over 2 sources, + XSPH, wall continuity, 2 EOS, gravity, x2 predictor/corrector), vs the + elliptical's single fused continuity kernel. Fusing the dam-break step is a + natural future optimisation (out of scope for this correctness-focused ADR). + See also the committed cross-GPU sweep (414x per-step at 1M on an RTX PRO 6000). + +## Representative final snapshot + +Side-by-side x-z view at `t=0.4 s` (fluid coloured by speed, walls grey). Both +panels show the collapsed column with the surge front advanced to x ~ 2.5-3 m +along the floor and peak speed ~3.5 m/s at the front; the PySPH CPU (fp64) and +Warp GPU (fp32) states are visually indistinguishable (consistent with the +fp32-vs-fp64 deltas quantified below). + +![CPU fp64 vs Warp fp32 dam-break snapshot at t=0.4 s](2026-06-19_warp-3d-dam-break-lobovsky_assets/cpu-vs-warp-snapshot.png) + +(CPU<->Warp parity at 7,392 particles; x-z projection. Review images live in +`2026-06-19_warp-3d-dam-break-lobovsky_assets/`; generated by +`experiments/2026-06-18_warp-dam-break-3d-runner/perf_and_snapshot_dam_break_3d.py`.) + +### 3D-explicit view at ~1M particles + +To show the case is genuinely 3D (not a 2D run plotted in 3D), the developed Warp +state at **999,975 particles** (`t=0.2 s`) in four views -- x-z side (all y +projected), x-y top-down (fluid spread across the full channel width in y), y-z +end view (cross-channel particle layers), and a subsampled 3D scatter: + +![Warp fp32 3D dam break, ~1M particles, four views at t=0.2 s](2026-06-19_warp-3d-dam-break-lobovsky_assets/bench-1M-3d-snapshot.png) + +(Generated by `bench_1M_and_3d_snapshot.py`. The x-y and y-z panels make the +multi-layer y-structure explicit; the simulation runs `dim=3` NNPS + 3D physics +throughout.) + +### Medium-resolution developed showcase (2026-06-20) + +Fresh `dx=0.025` run: **59,280 fluid + 66,407 wall = 125,687 particles**. +Warp measured `0.03140 s/step` versus `0.42733 s/step` for single-threaded +PySPH CPU (`13.61x`). The adaptive run reached `t=0.8016 s` in 2,837 steps, +remained finite, and kept density in `989.74..1014.13 kg/m^3`. + +The collapse-phase hero at `t=0.3996 s` uses the actual Warp particles coloured +by speed (not a generative rendering): + +![Warp GPU 3D dam-break collapse, 125,687 particles coloured by speed](2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png) + +The developed `t=0.8016 s` state is shown in four verification views: + +![Warp GPU developed 3D dam-break, 125,687 particles, four views](2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png) + +Splashsurf reconstruction was exercised successfully. Blender/ffmpeg are not +installed on this host, so the retained hero is explicitly a polished particle +visualization rather than a photorealistic animation. + +## Diff summary + +Implements plan step 5 (the experiment packet) on top of the already-present +additive backend physics (steps 1-4: WendlandQuintic id 2, `apply_body_force`, +`compute_tait_eos_hg_correction`, `wc_sph_dam_break_step`, dim=3 codegen/parity +tests). + +- New experiment packet `experiments/2026-06-18_warp-dam-break-3d-runner/`: + - `dam_break_3d_runner.py` -- Application-style 3D runner; IC built by the + *same* `DamBreak3DGeometry(no obstacle)` the reference uses -> warp fluid + + wall; `UniformGridWarpNNPS(dim=3)`; `wc_sph_dam_break_step`; adaptive dt with + PySPH `n_damp` *timestep* damping; 3D metrics (surge-front x, max height, + z-extent, 3D KE, wall p range). + - `run_correctness.sh` -- coarse smoke wrapper (venv python; no stale activate). + - `compare_warp_pysph_dam_break_3d.py` -- tier-1 hand-rolled CPU EPEC parity + (`LinkedListNNPS(dim=3)` + `WendlandQuintic`), mirrors the step block-for-block; + fixed dt / no damping / full gravity for a clean fp32-vs-fp64 diff. + - `resolved_dam_break_3d_comparison.py` -- tier-2 vs the real PySPH + `dam_break_3d_lobovsky.py` Application (subprocess `run(argv=...)`, shared t~0 + IC, step Warp to matched checkpoints), persists a summary JSON. + - `perf_and_snapshot_dam_break_3d.py` -- runs the real PySPH Application and + the Warp runner to the same `tf`, reports particle counts + wall/per-step + performance + speedup, and renders the side-by-side x-z snapshot. + - `bench_1M_and_3d_snapshot.py` -- ~1M-particle per-step throughput comparison + (CPU vs Warp, fixed step count) + a 3D-explicit four-view developed snapshot. + - `experiment.md`, `results-smoke.npz`, tier-1/tier-2 summary JSONs, + `cpu-vs-warp-snapshot.png`, `cpu-vs-warp-perf.json`. +- `pysph/base/warp_sph.py`: added the four Wendland device leaves to + `_WARP_DEVICE_FUNCS` for parity with cubic/gaussian (not part of any cache key + or generated source; kernel_id==2 already resolved transitively). Also **fused + the dam-break fluid step**: new `_WCSPH_DAM_BREAK_FLUID_BLOCKS` + (PressureGradient + ArtificialViscosity + ContinuityEquation) run as one + generated kernel per source (`accumulate_outputs=True`) instead of three + separate single-block launches -- a new cache entry, the 2D path / generated + source unchanged (byte-identity guard still passes). 1.23x faster Warp step. +- `pysph/base/tests/test_warp_codegen.py`: cache-stability regression guard + `test_2d_path_generated_source_is_byte_identical_to_golden` (md5-pins the + cubic/gaussian 2D-path generated source, flat + grid; asserts no `wendland`). +- ADR-0005 -> Accepted, with corrected cache wording + a Validation section; + decisions graph/index updated. + +## Validation evidence + +- **Backend targeted tests:** `5 passed` (Wendland 3D summation density, ramped + gravity + 2D guard, Tait-HG, two-array 3D dam-break step). Full focused suite + (`test_warp_codegen` + `test_warp_sph` + `test_warp_nnps`) rerun -- see + current.md for the count. +- **Tier-1:** kinematics + density match to ~1e-8 relative; pressure abs 0.17 Pa + < fp32 Tait floor 2.06 Pa -> `passed: true`. +- **Tier-2 (real Application):** KE / surge-front / max-height / density agree to + fp32 across checkpoints; p_max 1.11% relative at the developed checkpoint; + per-particle x/z ~1e-7 near rest. + +## Adversarial review (workflow, 5 dims x verify) + +24 confirmed findings; acted on the real ones (byte-identity guard, tier-2 +summary JSON + `--prefix`, dead wall-velocity pull, ADR wording, dict consistency). +The synthesis-elevated "blocker" (Wendland leaves missing from +`_WARP_DEVICE_FUNCS`) was **refuted by reality** -- every Wendland run already +passed because the routers resolve their leaves via module `__globals__`; the +leaves were added anyway for hygiene. + +## Known deltas / caveats (documented in experiment.md + ADR Validation) + +- Near-rest pressure is at the fp32 Tait-EOS cancellation floor (relative error + large only because p ~ 1 Pa); recovers to ~1% relative when developed. +- EPEC matches the reference `EPECIntegrator` (no PEC substitution). +- `n_damp` damps the timestep (not gravity); `c0 = 10*sqrt(2*9.81*0.55) ~ 32.85`. +- Cache: emitted 2D source byte-identical + logic-preserving router extension + (one-time recompile possible, results unchanged). + +## Out of scope (ADR-0005 follow-ups) + +SPHERIC/Kleefsman obstacle case; full `tf=2.5` run + probe-pressure vs +`db_exp_data.get_lobovsky_data()`; cubic/gaussian dam-break cross-check; +performance/cross-GPU characterisation. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM +- Timestamp: 2026-06-21T02:06:46 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/bench-1M-3d-snapshot.png b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/bench-1M-3d-snapshot.png new file mode 100644 index 000000000..32282f311 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/bench-1M-3d-snapshot.png differ diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/cpu-vs-warp-snapshot.png b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/cpu-vs-warp-snapshot.png new file mode 100644 index 000000000..eefce80bd Binary files /dev/null and b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/cpu-vs-warp-snapshot.png differ diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png new file mode 100644 index 000000000..502d8dd23 Binary files /dev/null and b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png differ diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png new file mode 100644 index 000000000..f660bc78f Binary files /dev/null and b/.ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png differ diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md new file mode 100644 index 000000000..164f80022 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md @@ -0,0 +1,178 @@ +--- +type: review +date: 2026-06-20 +user: @kunalpuri-prediqt +agent: codex +plan: plans/2026-06-20_warp-device-resident-rigid-body-p2.md +adrs: [ADR-0006] +aspects_touched: [warp-backend, particle-memory, validation-benchmarks] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Device-resident rigid-body 6-DOF integration (P2) + +## Diff summary + +- Added persistent `WarpRigidBodyState` device buffers for one or more bodies: + f64 moment reduction, mass/COM/inertia/force/torque/acceleration, linear and + angular state, and RK2 saved state; static body IDs stay on-device. +- Added a one-thread-per-body Warp finalize kernel with an explicit symmetric + 3x3 solve for angular acceleration and device error flags. +- Added device kernels/API for start-state save, RK2 midpoint/full compact-state + updates, rigid velocity `vc + omega x r`, and stage position updates. +- Refactored the P1 reduction into a reusable no-sync launcher. The old + `compute_rigid_body_moments()` host-result API is preserved; production P2 + uses `compute_rigid_body_moments_device()` and performs no host transfer. +- Added setup-time empty/zero-mass/singular-inertia rejection. +- Added fp32/fp64 device-finalize and RK2 parity tests, a no-host-barrier guard, + pure-translation geometry/multi-body checks, and singular-geometry coverage. +- Amended ADR-0006 and the floating-body experiment to record the explicitly + approved GPU-resident direction. + +## Aspects touched and host files modified + +- `warp-backend`: standalone Warp kernels and orchestration API. +- `particle-memory`: compact rigid state remains device-authoritative; host + copies only at explicit output/validation boundaries. +- `validation-benchmarks`: faithful NumPy/PySPH oracle parity and architectural + no-host-barrier gate. +- Host files: `pysph/base/warp_sph.py`, + `pysph/base/tests/test_warp_sph.py` — both within the approved boundary. + +## Behavioral / numerical changes + +- New behavior is additive; no existing WCSPH/fixed-wall step is modified. +- Rigid P2 now executes reduction -> moment finalize/3x3 solve -> rigid motion -> + RK2 compact-state update in Warp stream order, without `.numpy()`, + ParticleArray pull, or explicit synchronization. +- Compact rigid state/reduction are f64 even when particle fields are fp32; + particle positions/velocities remain in the configured particle dtype. +- Both precisions match the NumPy implementation of PySPH's + `RigidBodyMoments`, `RigidBodyMotion`, and `RK2StepRigidBody` at the stated + tolerances. Existing P1 host-query behavior remains available. + +## Tests / validation run + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py -k rigid \ + pysph/base/tests/test_warp_codegen.py::test_2d_path_generated_source_is_byte_identical_to_golden +.......... [100%] +10 passed, 40 deselected, 2 warnings in 6.70s +``` + +```text +$ python -m pytest -q pysph/base/tests/test_warp_sph.py +................................................ [100%] +48 passed, 2 warnings in 1825.06s (0:30:25) +``` + +The full run was collected immediately before the final singular-geometry test +was added; that host-only guard is included in the subsequent 10-test focused +rerun above. A final warm-cache run of the exact final tree then passed all 49: + +```text +$ python -m pytest -q --disable-warnings pysph/base/tests/test_warp_sph.py +................................................. [100%] +49 passed, 2 warnings in 12.84s +``` + +The 30-minute cold wall time is the known NVRTC compile of the +large fused Wendland 3D dam-break kernel; the process stayed healthy at full CPU +utilization and the remaining tests completed immediately afterward. + +P2 repeated-step runtime checkpoint (RTX 4060, 315-particle asymmetric 3D box, +prescribed force + torque, 2,000 RK2 steps, `dt=1e-4`, one final pull): + +```text +wall_s=0.5445955659997708; steps_per_s=3672.4500250537144 +device_error=0; all_finite=true; vc_max_abs_error=1.552180384223334e-09 +relative_pair_distance_drift=9.386771416218177e-07 +``` + +This is a P2 dynamics/runtime checkpoint, not fluid-coupled validation. + +```text +$ python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py + + +$ git diff --check + +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/update-decision-graph.py +Generated decisions/index.json and decisions/graph.md + +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- `implementation.md` boundary section updated: n/a — both host files match + existing `pysph/base/warp_*.py` and `pysph/base/tests/test_warp_*.py` entries. +- Amendments log entry: n/a. + +## Visual aid + +| P2 stage | Previous P1/P2 proposal | Implemented production path | +| --- | --- | --- | +| Particle force/torque reduction | GPU f64 atomic sum | GPU f64 atomic sum | +| COM/inertia/torque finalize | Host NumPy | GPU f64 kernel | +| 3x3 angular solve | Host NumPy | GPU f64 symmetric solve | +| RK2 body-state update | Host | GPU f64 kernel | +| Particle rigid motion | GPU | GPU | +| Per-stage host boundary | reduction copy + solve | **none** | + +The substantial fluid-only showcase images belong to—and are embedded in—the +[3D dam-break review](2026-06-19_warp-3d-dam-break-lobovsky.md#medium-resolution-developed-showcase-2026-06-20). +They validate the existing multi-array SPH path, not the still-pending P3 +fluid/rigid coupling; keeping that distinction in the review prevents a pretty +image from becoming an accidental physics claim. + +## Adversarial review + +Reviewed five dimensions: 3x3/cofactor and torque algebra; RK2 stage ordering; +device residency/synchronization; dtype/device/lifecycle handling; and additive +compatibility/cache stability. Findings fixed before the final run: + +- zero all compact outputs on a device-detected zero-mass reduction instead of + leaving stale values; +- reject a state/ParticleArray device mismatch at setup; +- reject empty, zero-mass, and singular-inertia bodies once at setup while + retaining the device error flag for runtime corruption; +- correct stale P1 comments that still described host finalize as production. + +No unresolved correctness finding remains for the P2 boundary. Long-horizon +rotation drift and physical fluid coupling remain explicit P4/P3 risks below. + +## Risks + +- The explicit 3x3 solve requires non-singular body geometry. Setup validates + every body once; the device kernel also emits an error flag for runtime + corruption. +- RK2 position integration intentionally matches PySPH's particle-based rigid + step rather than introducing a quaternion/orientation-matrix scheme. P4 must + measure long-horizon rigidity drift in the assembled case. +- Four ordered GPU operations remain per stage (zero/reduce, finalize, particle + motion, compact-state update). They have no host barrier and are compatible + with later CUDA-graph capture; launch fusion is not part of P2. +- P3 must still compute physically correct Liu equal-and-opposite forces before + this integrator is meaningful in the dam-break case. + +## Unresolved questions + +- Whether P4 needs quaternion/orientation-matrix integration depends on measured + long-run geometry drift; do not change away from PySPH parity preemptively. +- Device error flags are intentionally read only at explicit validation/output + boundaries. P3/P4 should define checkpoint failure reporting. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > take this as @prabhu: LGTM for P2. approved for P3 +- Timestamp: 2026-06-20T19:13:48 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md new file mode 100644 index 000000000..58ce0637f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md @@ -0,0 +1,204 @@ +--- +type: review +date: 2026-06-20 +user: @kunalpuri-prediqt +agent: codex +plan: plans/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md +adrs: [ADR-0006] +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +host_files: [pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +status: approved +--- + +# Review - Liu fluid/rigid coupling and coupled dam-break driver (P3) + +## Diff summary + +- Added `RigidNumberDensity`, `LiuFluidAcceleration`, and `LiuBodyReaction` + equation blocks. Liu coupling uses two destination-register passes, avoiding + nondeterministic fp32 source atomics while preserving equal-and-opposite + pair momentum. +- Added device kernels/helpers for rigid density save/midpoint/full staging and + per-particle body-force initialization. +- Added sibling `wc_sph_dam_break_rigid_step`; the existing fixed-wall driver + is unchanged. The new driver performs two EPEC force evaluations, advances + fluid/fixed walls through PEC, advances body density separately, and invokes + P2's device-resident rigid RK2 stages. +- Added fp32/fp64 Liu parity/reaction tests, Wendland number-density parity, + rigid staging checks, and a genuine 3D coupled-step smoke test. +- Added `warp_floating_box_runner.py` and a scientific hero render from a + 7,458-particle, 241-step coupled transient. + +Host diff for this slice: `warp_sph.py` +354 lines; +`test_warp_sph.py` +181/-4 lines. Experiment/review memory is additive. + +## Aspects touched and host files modified + +- `warp-backend`: additive Liu blocks, rigid staging kernels, coupled driver. +- `gpu-nnps`: repeated grid-direct cross-array traversal; no flat CSR cache. +- `particle-memory`: fluid, rigid particle, and compact 6-DOF state remain + device-authoritative; adaptive `dt` is the existing scalar handoff. +- `validation-benchmarks`: exact primitive parity plus an end-to-end transient. +- Host files: `pysph/base/warp_sph.py` and + `pysph/base/tests/test_warp_sph.py`, both inside the approved boundary. + +## Behavioral / numerical changes + +- Fluid pressure acceleration from body particles follows PySPH's + `LiuFluidForce`; the second pass applies the reversed, mass-weighted reaction + to body particle forces. No source-side atomic accumulation is used. +- Body self-neighbor `V = sum(W)` is initialized once. It is a static parity / + volume diagnostic; Liu itself does not consume `V`. +- Body contribution to fluid `arho` is evaluated exactly once outside the + pre-existing fluid+wall fused block. Body `arho` comes from fluid once; the + body is never integrated as independent fluid particles. +- Body gravity is initialized as `m*g` before each Liu force evaluation. The + existing f64 rigid reduction then produces force/torque and advances the + body entirely on the GPU. +- The existing `wc_sph_dam_break_step`, generated 2D source, and fixed-wall + behavior were not edited. + +## Tests / validation run + +Primitive fp32/fp64 Liu parity on the final tree: + +```text +$ python -m pytest -q --disable-warnings pysph/base/tests/test_warp_sph.py \ + -k warp_liu_coupling_matches_reference_and_reacts_equally +.. [100%] +2 passed, 52 deselected, 2 warnings in 242.67s (0:04:02) +``` + +Focused P3/cache guard collected before the final fp64 parameter was added: + +```text +$ python -m pytest -q \ + pysph/base/tests/test_warp_sph.py::test_warp_liu_coupling_matches_reference_and_reacts_equally \ + pysph/base/tests/test_warp_sph.py::test_warp_rigid_number_density_matches_cpu_wendland \ + pysph/base/tests/test_warp_sph.py::test_warp_rigid_density_and_body_force_stages \ + pysph/base/tests/test_warp_sph.py::test_warp_dam_break_rigid_step_is_finite_and_moves_body \ + pysph/base/tests/test_warp_codegen.py::test_2d_path_generated_source_is_byte_identical_to_golden +..... [100%] +5 passed, 49 deselected in 350.63s +``` + +The unchanged fixed-wall behavior test +`test_warp_dam_break_step_two_array_3d_is_finite_and_walls_fixed` also passes +in the final 54-test suite. + +Final exact-tree full Warp SPH suite: + +```text +$ python -m pytest -q --disable-warnings pysph/base/tests/test_warp_sph.py +...................................................... [100%] +54 passed, 2 warnings in 1886.59s (0:31:26) +``` + +The long wall time is the known CPU-side NVRTC compilation of large generated +Wendland 3D kernels. The process stayed active at ~100% CPU; all 54 tests +completed with no failure. + +Substantial coupled transient (RTX 4060, `dx=0.08`, collision-free horizon): + +```text +particles: 1,800 fluid + 5,592 wall + 66 body = 7,458 +steps / simulated time: 241 / 0.200603 s +all_finite / device_error: true / 0 +body COM displacement: [0.02396, -0.01009, -0.08081] m +body vc: [0.24182, -0.06702, -0.65823] m/s +body omega: [-0.14531, 0.70659, 0.20398] rad/s +final body force: [29.23, -5.27, 14.18] N +fluid rho: 991.68 .. 1015.78 kg/m^3 +body rho: 912.75 .. 1015.08 kg/m^3 +relative geometry drift: 1.90e-6 +``` + +The final positive vertical force despite body weight is computed fluid +reaction, not the prescribed-force P2 setup. The horizon deliberately ends +before rigid-wall contact, which remains P4 scope. + +```text +$ python -m py_compile pysph/base/warp_sph.py \ + pysph/base/tests/test_warp_sph.py \ + .ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/warp_floating_box_runner.py + + +$ git diff --check + +``` + +## validate-memory.py + +```text +$ python .ai/implementations/blast-from-the-past/scripts/update-decision-graph.py +Generated decisions/index.json and decisions/graph.md + +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Boundary amendment + +- `implementation.md` boundary update: n/a. Both host files match the existing + `pysph/base/warp_*.py` and `pysph/base/tests/test_warp_*.py` entries. +- Amendments log entry: n/a. + +## Visual aid + +![Warp GPU coupled dam-break: first floating-box transient](2026-06-20_warp-liu-fluid-rigid-coupling-p3_assets/coupled-column-dx080-t020-hero.png) + +Blue/cyan points are the actual fluid checkpoint, orange points are the rigid +box, and the faint grey geometry is the fixed flume. This is a scientific +particle render from the coupled run, not the P4 photorealistic animation. + +## Adversarial review + +Reviewed six dimensions: Liu sign/mass algebra; equal-and-opposite momentum; +`arho` accounting; EPEC stage ordering; device residency/determinism; and +fixed-driver/cache isolation. + +- Liu fp32 and fp64 match a direct NumPy/Wendland reference, including total + pair reaction at dtype tolerance. +- The body reaction is accumulated as a destination output, so there are no + fp32 source atomics. P2's force/torque reduction remains f64. +- The rigid body is excluded from `_WCSPH_DAM_BREAK_FLUID_BLOCKS`; fluid/body + continuity is launched once in each direction, avoiding `arho` duplication. +- Fixed walls remain fixed and the existing driver/golden source guards pass. +- Production stages contain no particle-state pull or host rigid solve. The + adaptive timestep retains the already-established scalar handoff. + +No blocker was found in the implemented P3 boundary. + +## Risks + +- Two-pass Liu coupling traverses fluid/body neighbors twice. It favors + deterministic correctness over launch/traversal minimization; profile before + considering fusion or mixed-dtype atomics. +- Body density reaches 912.75 kg/m^3 in the transient. Tait-HG prevents tensile + body pressure, but this excursion merits monitoring in P4's longer/contact + case. +- There is no rigid-wall contact yet. The reviewed transient is intentionally + collision-free and must not be extrapolated past first wall impact. +- Particle-based RK2 preserves geometry to `1.90e-6` relative over this run; + P4 must measure drift over the longer cinematic case before deciding whether + orientation-matrix/quaternion state is needed. + +## Unresolved questions / accepted validation deviation + +- The approved plan requested a complete hand-staged NumPy oracle for every + output of one coupled EPEC step. P3 instead has exact fp32/fp64 primitive Liu + parity, equal reaction, exact number-density/staging tests, the established + P2 RK2 oracle, and an end-to-end coupled smoke/transient. A monolithic EPEC + oracle was not added because it would mostly duplicate already-pinned + primitives and the current Python 3.14 environment cannot run the shipped + Compyle rigid Application. This is disclosed for reviewer acceptance rather + than silently claimed complete. +- P4 decides contact handling and whether the cinematic run needs a rigid/contact + timestep criterion in addition to the current fluid CFL scalar. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > alright. @prabhu: LGTM and closeout +- Timestamp: 2026-06-21T02:02:00 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3_assets/coupled-column-dx080-t020-hero.png b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3_assets/coupled-column-dx080-t020-hero.png new file mode 100644 index 000000000..6824f105b Binary files /dev/null and b/.ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3_assets/coupled-column-dx080-t020-hero.png differ diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-06-21_closeout-and-showcase-artifacts.md b/.ai/implementations/blast-from-the-past/reviews/2026-06-21_closeout-and-showcase-artifacts.md new file mode 100644 index 000000000..4fb34fc62 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-06-21_closeout-and-showcase-artifacts.md @@ -0,0 +1,95 @@ +--- +type: review +date: 2026-06-21 +user: @kunalpuri-prediqt +agent: codex +plan: n/a +adrs: [ADR-0005, ADR-0006] +aspects_touched: [validation-benchmarks, host-integration] +host_files: [CODEBASE_UNDERSTANDING.md] +status: approved +--- + +# Review - Remaining closeout and showcase artifacts + +## Diff summary + +This package versions every file intentionally left after the scoped P3 commit: + +- marks eight completed foundational/elliptical experiments `complete` after + checking their recorded outputs; +- records the developed `dx=0.025` 3D dam-break metrics in its experiment and + review, with the JSON timing record and two actual-run images; +- versions the earlier 2026-06-20 daily/session closeouts; +- retains the coupled-run hero in its experiment directory as the source copy + (the byte-identical review copy landed with P3); +- versions the owner-provided `CODEBASE_UNDERSTANDING.md` architecture report; +- updates current/closeout memory and the implementation boundary. + +No Python/Cython/Warp source or test file changes in this package. + +## Boundary amendment + +`CODEBASE_UNDERSTANDING.md` is outside the previous host-file boundary. The +owner explicitly requested that all remaining files be committed, so +`implementation.md` now lists this root architecture snapshot as a retained +host-level reference. No executable behavior or public API is affected. + +## Validation evidence + +The experiment status audit only changes `status` and `last_checked` after +checking existing successful outputs. The dam-break additions record this +already-completed run: + +```text +resolution / particles: dx=0.025 / 125,687 +Warp / CPU step: 0.03140 s / 0.42733 s +speedup: 13.61x +steps / final time: 2,837 / 0.8016 s +all finite: true +rho range: 989.7354 .. 1014.1267 kg/m^3 +``` + +The coupled source image and committed P3 review image have identical SHA-256: + +```text +f7cafa944a83b2bd459087357a707244462c4266be2d38588e24384418b055c9 +``` + +```text +$ git diff --check + + +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +No test rerun is required: this package contains no executable changes. The +immediately preceding P3 exact-tree run was `54 passed, 2 warnings`. + +## Visual aid + +Developed dam-break hero, actual Warp particles colored by speed: + +![Warp GPU 3D dam-break collapse](2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t040-hero.png) + +Four-view developed-state verification: + +![Warp GPU developed 3D dam-break](2026-06-19_warp-3d-dam-break-lobovsky_assets/showcase-dx025-t080-3d-snapshot.png) + +## Risks / unresolved questions + +- `CODEBASE_UNDERSTANDING.md` is a generated point-in-time report and may age; + the curated `.ai/` spec remains the implementation's active memory. +- The coupled hero is intentionally stored twice (experiment source and review + asset). The hashes prove identity; this costs about 1 MiB but preserves both + provenance and review rendering. +- The 3D dam-break review is locally approved; upstream PR #435 review remains + a separate publication workflow. + +## Sign-off + +- Reviewer: @prabhu +- Verdict, verbatim quote: + > @prabhu: LGTM +- Timestamp: 2026-06-21T02:06:46 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-apr-p0-obstacle-checkpoint.md b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-apr-p0-obstacle-checkpoint.md new file mode 100644 index 000000000..609308331 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-apr-p0-obstacle-checkpoint.md @@ -0,0 +1,126 @@ +--- +type: review +date: 2026-07-06 +user: @kunalpuri-prediqt +agent: codex +plan: 2026-07-06_warp-dynamic-adaptive-particle-resolution +adrs: [ADR-0005] +aspects_touched: [warp-backend, validation-benchmarks, host-integration] +host_files: [] +review_mode: prototype-owner +status: prototype-approved +--- + +# Review - APR P0 fixed-obstacle checkpoint + +## Diff summary + +- Extend the existing experiment runner with opt-in `--with-obstacle` support. + It consumes the third array already produced by `DamBreak3DGeometry` and + passes all solids to the existing `wc_sph_dam_break_step`; no backend changes. +- Generalize runner metrics/output for an optional obstacle while preserving + no-obstacle behavior. +- Add three assessed primary APR reference notes and index entries. +- Open the P0 experiment and record one-step, startup, and first-impact NPZ + outputs plus exact metrics. +- Add a NumPy-only 3D split-stencil density minimization and its 101-grid JSON + output; record the converged paper/PySPH discrepancy as a kill-test failure. +- Update current/session/daily memory with the active P0 checkpoint. + +## Aspects touched and host files modified + +Experiment/memory only. No host PySPH file is modified. + +## Behavioral / numerical changes + +The runner has a new opt-in fixed obstacle. Default `with_obstacle=False` +preserves the original two-array case. With the option enabled, the runner uses +fluid index 0 and every remaining array as a fixed solid source. + +The 250-step coarse run develops fluid/wall/obstacle pressure and reaches the +obstacle while keeping its device coordinates unchanged. This is prototype +evidence at `dx=0.10`, not a converged Kleefsman pressure claim. + +## Tests / validation run + +```text +$ python -m py_compile .../dam_break_3d_runner.py + + +$ python .../dam_break_3d_runner.py --dx 0.1 --steps 1 +fluid/wall = 1000/3824; obstacle=0; all_finite=true; exit 0 + +$ python .../dam_break_3d_runner.py --dx 0.1 --steps 20 --with-obstacle +fluid/wall/obstacle = 1000/3824/4 +t=0.003095018; all_finite=true; elapsed=3.16 s + +$ python .../dam_break_3d_runner.py --dx 0.1 --steps 250 --with-obstacle +fluid/wall/obstacle = 1000/3824/4 +t=0.258454926; all_finite=true +rho=983.44897..1022.19061 kg/m^3 +obstacle_p=23.696..150.147 kPa +surge_front_x=2.4901464; elapsed=5.76 s; max RSS=350756 KiB + +$ compare obstacle x/y/z at step 1 and step 250 +obstacle_device_position_drift=0 + +$ python split_stencil_density_kill.py --ngrid 101 +cubic+center: E=1.2586503e-3, mass sum=1 +icosa+center: E=3.5803204e-4, mass sum=1, min/max=0.6565656 +paper Table 1: E=8.326e-5, min/max=0.33 + +$ python split_stencil_density_kill.py --ngrid 81/121 +icosa E=3.5803275e-4 / 3.5803200e-4 (quadrature converged) + +$ git diff --check + +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a + +Only `.ai/` experiment and implementation-memory files changed. + +## Visual aid + +Waiver: this checkpoint proves array wiring and numerical finiteness; no visual +scientific claim is made. The committed NPZ contains fluid, wall, and obstacle +coordinates/fields for later checkpoint rendering. + +## Risks + +- Four obstacle particles are sufficient for wiring/impact smoke but far too + coarse for validation against experimental pressure traces. +- The first cold execution paid multi-minute generated Wendland cache loads; + warm timings exclude that one-time compilation cost. +- Pointwise pressure is not converged and may be fp32/noise sensitive. P0 still + needs probe impulse and uniform coarse/fine comparisons. +- The Muta--Ramachandran implementation is 2D-first; treating it as a 3D GPU + implementation would be incorrect. +- The first faithful-looking Vacondio reproduction does not match its numeric + table under PySPH's kernel convention. No production split weights may be + chosen from this result until the discrepancy is explained. + +## Unresolved questions + +- Vacondio's `epsilon=0.65`, `alpha=0.70`, 12-shell-plus-center stencil is now + ingested, but its Table 1 mass ratio/error is not reproduced by the + constrained PySPH-Wendland calculation. +- The tradeoff between a 14+center split and cheaper binary/eight-child GPU + refinement remains undecided; ADR-0007 is intentionally deferred. + +## Sign-off + +- Review mode: prototype-owner +- Prototype owner: @kunalpuri-prediqt +- Prototype authorization, verbatim quote: + > commit this prototype checkpoint. list out next steps +- Timestamp: 2026-07-06T11:00:43 CEST diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-dynamic-adaptive-particle-resolution-plan.md b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-dynamic-adaptive-particle-resolution-plan.md new file mode 100644 index 000000000..d56186d95 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-dynamic-adaptive-particle-resolution-plan.md @@ -0,0 +1,128 @@ +--- +type: review +date: 2026-07-06 +user: @kunalpuri-prediqt +agent: codex +plan: 2026-07-06_warp-dynamic-adaptive-particle-resolution +adrs: [] +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +host_files: [] +review_mode: prototype-owner +status: prototype-approved +--- + +# Review - Dynamic adaptive particle resolution plan package + +## Diff summary + +This prototype-governance and planning package adds: + +- an approved Tier-2 plan for single-GPU, device-resident dynamic particle + refinement/coarsening in the Warp WCSPH path; +- five separately reviewed checkpoints: baseline/design kill tests, + multilevel NNPS, GPU particle lifecycle, conservative variable-resolution + physics, and the adaptive fixed-obstacle dam-break; +- explicit correctness, conservation, performance, and scale gates; +- a session log recording consulted memory, scope, exact owner approval, and + the next review gate; +- this pre-commit review artifact. +- a narrow owner-authorized prototype review mode in the implementation + contract, with promotion/upstream work still gated by `@prabhu: LGTM`; +- matching review-template fields and validator checks for + `status: prototype-approved`. + +No PySPH runtime Python, Cython, Warp kernel, test, experiment output, or +host-project file is modified by this package. The memory validator is updated +only to enforce the new prototype-review metadata. + +## Aspects touched and host files modified + +| Aspect | Planning effect | Host files modified now | +|---|---|---| +| warp-backend | Defines additive APR sibling path and variable-resolution physics work | none | +| gpu-nnps | Defines exact multilevel/cross-level neighbor-search checkpoint | none | +| particle-memory | Defines device pool, scan allocation, and compaction checkpoint | none | +| validation-benchmarks | Defines obstacle baselines and scientific/scale gates | none | +| host-integration | Pins boundary and staged review/commit workflow | none | + +The plan's future host-file list stays within the existing +`pysph/base/warp_*.py` and `pysph/base/tests/test_warp_*.py` boundary. The plan +requires an amendment before touching generic ParticleArray, Cython ABI, +solver, or shipped-example files. + +## Behavioral / numerical changes + +No solver or numerical behavior changes and no experiment starts. Governance +behavior changes: prototype commits meeting every Rule 4 restriction may use +quoted owner authorization instead of external `LGTM`; promotion and upstream +publication still require `@prabhu: LGTM`. + +Future numerical thresholds are visible in the plan rather than implied: +mass/momentum conservation gates, uniform-fine observable comparisons, at +least 4x fewer active particles, and a target of at least 2x lower measured +time-to-solution. Failure to meet a performance target must be reported rather +than relabeled as success. + +## Tests / validation run + +No executable tests are required for this documentation-only package. + +```text +$ git diff --check + + +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n/a +- Amendments log entry: n/a + +The proposed host files already match the approved Warp wildcard boundary. + +## Visual aid + +Waiver: this package contains no numerical or behavioral result; the phase and +acceptance-gate structure is clearer in the plan's ordered text than in a +duplicated diagram. + +## Risks + +- This approval covers a multi-session research track, but each checkpoint + still needs its own review and exact `@prabhu: LGTM` before commit. +- The primary adaptive PySPH reference is 2D and EDAC-oriented; P0 explicitly + prevents treating it as an already-validated 3D free-surface WCSPH method. +- The global finest-particle timestep may limit runtime gains even when memory + and neighbor-work gains are substantial; the scale gates expose this risk. +- The plan is intentionally single-GPU. It does not claim that APR replaces + later multi-GPU domain decomposition for arbitrarily large problems. +- A prototype exception could be abused to avoid review. The contract limits it + to in-boundary Warp experimentation and makes the later promotion review + cumulative and mandatory. + +## Unresolved questions + +- ADR-0007 must select the 3D split pattern, merge ownership, target-resolution + representation, boundary policy, and canonical particle-pool layout after P0 + kill tests. +- Concrete uniform-equivalent scale depends on measured local GPU capacity and + will be fixed in the P0 experiment rather than guessed in this review. + +## Sign-off + +- Review mode: prototype-owner +- Prototype owner: @kunalpuri-prediqt +- Prototype authorization, verbatim quote: + > approved +- Timestamp: 2026-07-06T10:38:44 CEST + +This authorization approves the proposed prototype exception, which explicitly +applies to the current APR plan package. It is not promotion approval. diff --git a/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-multilevel-gpu-nnps-plan.md b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-multilevel-gpu-nnps-plan.md new file mode 100644 index 000000000..095455b2f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-multilevel-gpu-nnps-plan.md @@ -0,0 +1,94 @@ +--- +type: review +date: 2026-07-06 +user: @kunalpuri-prediqt +agent: codex +plan: 2026-07-06_warp-multilevel-gpu-nnps +adrs: [ADR-0003, ADR-0004] +aspects_touched: [gpu-nnps, warp-backend, particle-memory, validation-benchmarks] +host_files: [] +review_mode: prototype-owner +status: prototype-approved +--- + +# Review - multilevel GPU NNPS plan checkpoint + +## Diff summary + +- Add a focused Tier-2 draft plan for an exact, device-built multilevel Warp + NNPS as the first runtime GPU APR milestone. +- Bound the proposed implementation away from split/merge allocation, APR + physics, offline stencil optimization, periodic traversal, and multi-GPU. +- Define correctness, device-residency, candidate-count, runtime, and memory + acceptance gates before ADR-0007 may be accepted. +- Refresh active-experiment inspection timestamps and current/daily/session + memory after checking that recorded artifacts remain present. + +## Aspects touched and host files modified + +Planning touches `gpu-nnps`, `warp-backend`, `particle-memory`, and +`validation-benchmarks`. No host PySPH file is modified in this checkpoint. + +## Behavioral / numerical changes + +None. This checkpoint records a draft plan and memory only. It does not change +the NNPS, generated Warp kernels, simulation behavior, or numerical results. + +## Tests / validation run + +```text +$ python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS + +$ git diff --check + +``` + +No numerical test was run because no executable or host code changed. + +## validate-memory.py + +```text +validate-memory: PASS +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a + +Only `.ai/` plan and implementation-memory files changed. + +## Visual aid + +Waiver: this is a plan-only checkpoint with no runtime or scientific result to +visualize. + +## Risks + +- The plan is still `status: draft`: committing it records the proposed work; + it does not authorize implementation under Rule 2. +- Level ratios and eventual split weights remain provisional because APR P0 + has not resolved the Vacondio/PySPH stencil-convention mismatch. +- Dense per-level grids may fail the memory or timing gates and require a + sparse key/sort alternative. + +## Unresolved questions + +- Whether dense per-level storage beats sparse storage on representative + localized-refinement distributions. +- Whether the proposed single-level overhead and mixed-level timing gates are + achievable with a generated loop over levels. +- Which production level ratio and split/merge policy will be selected after + P0; this checkpoint deliberately does not decide them. + +## Sign-off + +- Review mode: prototype-owner +- Prototype owner: @kunalpuri-prediqt +- Prototype authorization, verbatim quote: + > commit locally please. +- Timestamp: 2026-07-06T11:23:29+02:00 +- Scope authorized: local commit of the plan/memory checkpoint only. +- Tier-2 implementation approval: still pending one of the exact Rule 2 + verdicts. diff --git a/.ai/implementations/blast-from-the-past/scripts/compact.py b/.ai/implementations/blast-from-the-past/scripts/compact.py new file mode 100755 index 000000000..778951828 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/compact.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +"""Archive daily closeouts and session logs older than the configured window.""" + +from __future__ import annotations + +import argparse +import shutil +from datetime import datetime, timedelta +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +ARCHIVE_WEEKS = 12 +NOW = datetime(2026, 6, 15) + + +def parse_date_from_name(path: Path): + for part in path.stem.split("_"): + try: + return datetime.strptime(part[:10], "%Y-%m-%d") + except ValueError: + pass + try: + return datetime.strptime(path.stem[:10], "%Y-%m-%d") + except ValueError: + return None + + +def collect(): + cutoff = NOW - timedelta(weeks=ARCHIVE_WEEKS) + pairs = [ + (ROOT / "updates" / "daily", ROOT / "updates" / "archive" / "daily"), + (ROOT / "updates" / "session-logs", ROOT / "updates" / "archive" / "session-logs"), + ] + moves = [] + for src_dir, dst_dir in pairs: + for path in sorted(src_dir.glob("*.md")): + d = parse_date_from_name(path) + if d and d < cutoff: + moves.append((path, dst_dir / path.name)) + return moves + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true") + args = parser.parse_args() + moves = collect() + if not moves: + print("compact: nothing to archive") + return 0 + for src, dst in moves: + print(f"{'would move' if args.dry_run else 'move'} {src} -> {dst}") + if not args.dry_run: + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.move(str(src), str(dst)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-aspect.py b/.ai/implementations/blast-from-the-past/scripts/new-aspect.py new file mode 100755 index 000000000..35f56164a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-aspect.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse, re +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +def slugify(s): return re.sub(r"[^a-zA-Z0-9]+", "-", s.lower()).strip("-") or "aspect" +def main(): + p = argparse.ArgumentParser(); p.add_argument("name") + a = p.parse_args(); name = slugify(a.name); d = ROOT / "aspects" / name + if d.exists(): raise SystemExit(f"Refusing to overwrite {d}") + (d / "notes").mkdir(parents=True) + text = (ROOT / "templates" / "aspect-context-template.md").read_text().replace("{{name}}", name).replace("{{ISO_TIMESTAMP}}", "2026-06-15T07:19:08 CET") + (d / "context.md").write_text(text) + (d / "open-questions.md").write_text(f"# Open Questions - {name}\n\n- (none yet)\n") + (d / "known-issues.md").write_text(f"# Known Issues - {name}\n\n- (none yet)\n") + print(d); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-daily-closeout.py b/.ai/implementations/blast-from-the-past/scripts/new-daily-closeout.py new file mode 100755 index 000000000..46b031894 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-daily-closeout.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +def main(): + p = argparse.ArgumentParser(); p.add_argument("--date", default="2026-06-15") + a = p.parse_args(); path = ROOT / "updates" / "daily" / f"{a.date}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "daily-closeout-template.md").read_text().replace("{{YYYY-MM-DD}}", a.date).replace("{{AGENT_ID}}", "codex").replace("{{N}}", "0") + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-decision.py b/.ai/implementations/blast-from-the-past/scripts/new-decision.py new file mode 100755 index 000000000..93036d1f4 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-decision.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +"""Create a new ADR without overwriting existing files.""" + +from __future__ import annotations + +import argparse +import re +from datetime import datetime +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +DECISIONS = ROOT / "decisions" + + +def slugify(text: str) -> str: + slug = re.sub(r"[^a-zA-Z0-9]+", "-", text.strip().lower()).strip("-") + return slug or "decision" + + +def next_id() -> tuple[str, str]: + max_id = 0 + for path in DECISIONS.glob("*adr-*.md"): + m = re.search(r"adr-(\d{4})", path.name) + if m: + max_id = max(max_id, int(m.group(1))) + n = max_id + 1 + return f"ADR-{n:04d}", f"{n:04d}" + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("title") + parser.add_argument("--scope", default="global") + parser.add_argument("--status", default="Proposed") + parser.add_argument("--date", default="2026-06-15") + parser.add_argument("--author", default="@kunalpuri-prediqt") + args = parser.parse_args() + + DECISIONS.mkdir(parents=True, exist_ok=True) + adr_id, nnnn = next_id() + slug = slugify(args.title) + path = DECISIONS / f"{args.date}_adr-{nnnn}_{slug}.md" + if path.exists(): + raise SystemExit(f"Refusing to overwrite {path}") + text = f"""--- +type: decision +id: {adr_id} +date: {args.date} +author: {args.author} +scope: {args.scope} +status: {args.status} +supersedes: [] +relates_to: [] +depends_on: [] +conflicts_with: [] +--- + +# {adr_id}: {args.title} + +## Context + +Confirm with team. + +## Decision + +Confirm with team. + +## Rationale + +Confirm with team. + +## Alternatives considered + +Confirm with team. + +## Consequences + +Confirm with team. + +## Follow-ups + +- Confirm with team. +""" + path.write_text(text) + print(path) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-experiment.py b/.ai/implementations/blast-from-the-past/scripts/new-experiment.py new file mode 100755 index 000000000..d87429cb3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-experiment.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse, re +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +def slugify(s): return re.sub(r"[^a-zA-Z0-9]+", "-", s.lower()).strip("-") or "experiment" +def main(): + p = argparse.ArgumentParser(); p.add_argument("title"); p.add_argument("--aspect", default="validation-benchmarks"); p.add_argument("--date", default="2026-06-15") + a = p.parse_args(); slug = slugify(a.title); d = ROOT / "experiments" / f"{a.date}_{slug}" + if d.exists(): raise SystemExit(f"Refusing to overwrite {d}") + (d / "plots").mkdir(parents=True) + text = (ROOT / "templates" / "experiment-template.md").read_text().replace("{{YYYY-MM-DD}}_{{slug}}", f"{a.date}_{slug}").replace("{{ISO_TIMESTAMP}}", f"{a.date}T07:19:08 CET").replace("{{aspect-name}}", a.aspect).replace("{{Title}}", a.title) + (d / "experiment.md").write_text(text); print(d); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-plan.py b/.ai/implementations/blast-from-the-past/scripts/new-plan.py new file mode 100755 index 000000000..cfa1ef5f0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-plan.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse, re +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +def slugify(s): return re.sub(r"[^a-zA-Z0-9]+", "-", s.lower()).strip("-") or "plan" + +def main(): + p = argparse.ArgumentParser(); p.add_argument("title"); p.add_argument("--date", default="2026-06-15") + a = p.parse_args(); slug = slugify(a.title) + path = ROOT / "plans" / f"{a.date}_{slug}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "plan-template.md").read_text().replace("{{YYYY-MM-DD}}_{{slug}}", f"{a.date}_{slug}").replace("{{Title}}", a.title).replace("{{ISO_TIMESTAMP}}", f"{a.date}T00:00:00 CET").replace("{{AGENT_ID}}", "codex") + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-reference.py b/.ai/implementations/blast-from-the-past/scripts/new-reference.py new file mode 100755 index 000000000..5ec1ce824 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-reference.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse, re +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +def slugify(s): return re.sub(r"[^a-zA-Z0-9]+", "-", s.lower()).strip("-") or "reference" +def main(): + p = argparse.ArgumentParser(); p.add_argument("title"); p.add_argument("--kind", default="primary") + a = p.parse_args(); slug = slugify(a.title); d = ROOT / "references" / ("primary" if a.kind == "primary" else "secondary") + path = d / f"{slug}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "reference-note-template.md").read_text().replace("{{slug}}", slug).replace("{{ISO_TIMESTAMP}}", "2026-06-15T07:19:08 CET").replace("{{Title}}", a.title).replace("kind: primary", f"kind: {a.kind}") + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-review.py b/.ai/implementations/blast-from-the-past/scripts/new-review.py new file mode 100755 index 000000000..fcf1f700a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-review.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse, re +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +def slugify(s): return re.sub(r"[^a-zA-Z0-9]+", "-", s.lower()).strip("-") or "review" +def main(): + p = argparse.ArgumentParser(); p.add_argument("title"); p.add_argument("--date", default="2026-06-15") + a = p.parse_args(); path = ROOT / "reviews" / f"{a.date}_{slugify(a.title)}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "review-template.md").read_text().replace("{{Title}}", a.title).replace("{{YYYY-MM-DD}}", a.date).replace("{{AGENT_ID}}", "codex") + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-session-log.py b/.ai/implementations/blast-from-the-past/scripts/new-session-log.py new file mode 100755 index 000000000..be9286b51 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-session-log.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +def main(): + p = argparse.ArgumentParser(); p.add_argument("--date", default="2026-06-15"); p.add_argument("--start", default="07:19") + a = p.parse_args(); path = ROOT / "updates" / "session-logs" / f"{a.date}_{a.start.replace(':','')}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "session-log-template.md").read_text().replace("{{YYYY-MM-DD}}", a.date).replace("{{HH:MM}}", a.start, 1).replace("{{HH:MM}}", a.start).replace("{{AGENT_ID}}", "codex") + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/new-weekly-closeout.py b/.ai/implementations/blast-from-the-past/scripts/new-weekly-closeout.py new file mode 100755 index 000000000..eff167d8d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/new-weekly-closeout.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +def main(): + p = argparse.ArgumentParser(); p.add_argument("--week", default="2026-W25"); p.add_argument("--range", default="2026-06-15 to 2026-06-21") + a = p.parse_args(); path = ROOT / "updates" / "weekly" / f"{a.week}.md" + if path.exists(): raise SystemExit(f"Refusing to overwrite {path}") + text = (ROOT / "templates" / "weekly-closeout-template.md").read_text().replace("{{YYYY-Www}}", a.week).replace("{{YYYY-MM-DD}} to {{YYYY-MM-DD}}", a.range) + path.write_text(text); print(path); return 0 +if __name__ == "__main__": raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/update-decision-graph.py b/.ai/implementations/blast-from-the-past/scripts/update-decision-graph.py new file mode 100755 index 000000000..be6946da9 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/update-decision-graph.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +"""Regenerate and validate the ADR decision index and graph.""" + +from __future__ import annotations + +import argparse +import json +import re +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +DECISIONS = ROOT / "decisions" +INDEX = DECISIONS / "index.json" +GRAPH = DECISIONS / "graph.md" +ADR_RE = re.compile(r"^ADR-\d{4}$") + + +def parse_scalar(value: str): + value = value.strip() + if value in ("[]", ""): + return [] + if value.startswith("[") and value.endswith("]"): + inner = value[1:-1].strip() + if not inner: + return [] + return [x.strip().strip("'\"") for x in inner.split(",")] + return value.strip("'\"") + + +def frontmatter(path: Path) -> dict: + text = path.read_text() + if not text.startswith("---\n"): + raise ValueError(f"{path}: missing frontmatter") + end = text.find("\n---", 4) + if end == -1: + raise ValueError(f"{path}: unterminated frontmatter") + data = {} + for line in text[4:end].splitlines(): + if not line.strip() or line.lstrip().startswith("#"): + continue + if ":" not in line: + raise ValueError(f"{path}: invalid frontmatter line: {line}") + key, value = line.split(":", 1) + data[key.strip()] = parse_scalar(value) + return data + + +def scan(): + nodes = [] + for path in sorted(DECISIONS.glob("*.md")): + if path.name in ("graph.md", "index.md"): + continue + fm = frontmatter(path) + fm["file"] = str(path.relative_to(ROOT)) + nodes.append(fm) + return nodes + + +def validate(nodes: list[dict]) -> list[str]: + errors = [] + ids = [n.get("id") for n in nodes] + seen = set() + aspect_scopes = {p.name for p in (ROOT / "aspects").iterdir() if p.is_dir()} + + for node in nodes: + node_id = node.get("id") + if not isinstance(node_id, str) or not ADR_RE.match(node_id): + errors.append(f"{node.get('file')}: invalid ADR id {node_id!r}") + if node_id in seen: + errors.append(f"duplicate ADR id {node_id}") + seen.add(node_id) + scope = node.get("scope") + if scope != "global" and scope not in aspect_scopes: + errors.append(f"{node_id}: invalid scope {scope!r}") + if node.get("status") not in ("Proposed", "Accepted", "Superseded", "Rejected"): + errors.append(f"{node_id}: invalid status {node.get('status')!r}") + + id_set = set(ids) + by_id = {n.get("id"): n for n in nodes} + edge_fields = ("supersedes", "relates_to", "depends_on", "conflicts_with") + for node in nodes: + node_id = node.get("id") + for field in edge_fields: + values = node.get(field, []) + if isinstance(values, str): + values = [values] + if not isinstance(values, list): + errors.append(f"{node_id}: {field} must be a list") + continue + for target in values: + if target not in id_set: + errors.append(f"{node_id}: {field} target {target} does not exist") + if field == "depends_on" and target in by_id: + if by_id[target].get("status") != "Accepted": + errors.append( + f"{node_id}: depends_on target {target} is " + f"{by_id[target].get('status')}, not Accepted" + ) + + # Supersedes cycle check. + supersedes = {} + for node in nodes: + vals = node.get("supersedes", []) + if isinstance(vals, str): + vals = [vals] + supersedes[node.get("id")] = vals + + def visit(start, node_id, stack): + for nxt in supersedes.get(node_id, []): + if nxt == start or nxt in stack: + errors.append(f"supersedes cycle involving {start}") + return + visit(start, nxt, stack | {nxt}) + + for node_id in list(supersedes): + visit(node_id, node_id, {node_id}) + + return sorted(set(errors)) + + +def render_index(nodes: list[dict]) -> str: + out = {"nodes": [], "edges": []} + for node in sorted(nodes, key=lambda n: n.get("id", "")): + out["nodes"].append({ + "id": node.get("id"), + "file": node.get("file"), + "scope": node.get("scope"), + "status": node.get("status"), + "date": node.get("date"), + }) + for field in ("supersedes", "relates_to", "depends_on", "conflicts_with"): + vals = node.get(field, []) + if isinstance(vals, str): + vals = [vals] + for target in vals: + out["edges"].append({"from": node.get("id"), "to": target, "type": field}) + return json.dumps(out, indent=2, sort_keys=True) + "\n" + + +def render_graph(nodes: list[dict]) -> str: + lines = [ + "# Decision Graph", + "", + "Generated from ADR frontmatter. Do not hand-edit.", + "", + "```mermaid", + "flowchart TD", + ] + scopes = sorted({n.get("scope") for n in nodes}) + for scope in scopes: + safe = re.sub(r"[^A-Za-z0-9_]", "_", str(scope)) + lines.append(f" subgraph {safe}[{scope}]") + for node in sorted([n for n in nodes if n.get("scope") == scope], key=lambda n: n.get("id")): + node_id = node.get("id") + status = node.get("status") + label = f"{node_id}
{status}" + lines.append(f" {node_id.replace('-', '_')}[\"{label}\"]") + lines.append(" end") + for node in sorted(nodes, key=lambda n: n.get("id", "")): + src = node.get("id", "").replace("-", "_") + edge_defs = { + "depends_on": "-- depends_on -->", + "relates_to": "-. relates_to .->", + "supersedes": "-. supersedes .->", + "conflicts_with": "-. conflicts_with .->", + } + for field, arrow in edge_defs.items(): + vals = node.get(field, []) + if isinstance(vals, str): + vals = [vals] + for target in vals: + lines.append(f" {src} {arrow} {target.replace('-', '_')}") + lines.extend([ + " classDef Accepted fill:#d5f5d5,stroke:#2c7a2c;", + " classDef Proposed fill:#fff3bf,stroke:#9a7500;", + " classDef Superseded fill:#e5e7eb,stroke:#6b7280;", + " classDef Rejected fill:#ffd6d6,stroke:#b91c1c;", + ]) + for node in nodes: + lines.append(f" class {node.get('id').replace('-', '_')} {node.get('status')};") + lines.append("```") + return "\n".join(lines) + "\n" + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--check", action="store_true") + args = parser.parse_args() + + DECISIONS.mkdir(parents=True, exist_ok=True) + try: + nodes = scan() + errors = validate(nodes) + except Exception as exc: + print(f"ERROR: {exc}", file=sys.stderr) + return 1 + if errors: + for error in errors: + print(f"ERROR: {error}", file=sys.stderr) + return 1 + + index_text = render_index(nodes) + graph_text = render_graph(nodes) + if args.check: + ok = True + if not INDEX.exists() or INDEX.read_text() != index_text: + print("ERROR: decisions/index.json is stale", file=sys.stderr) + ok = False + if not GRAPH.exists() or GRAPH.read_text() != graph_text: + print("ERROR: decisions/graph.md is stale", file=sys.stderr) + ok = False + return 0 if ok else 1 + INDEX.write_text(index_text) + GRAPH.write_text(graph_text) + print(f"Generated {INDEX.relative_to(ROOT)} and {GRAPH.relative_to(ROOT)}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/scripts/validate-memory.py b/.ai/implementations/blast-from-the-past/scripts/validate-memory.py new file mode 100755 index 000000000..ba86d429e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/scripts/validate-memory.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +"""Validate the .ai memory system.""" + +from __future__ import annotations + +import re +import subprocess +import sys +from datetime import datetime, timedelta +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +ALLOWED_USERS = {"@kunalpuri-prediqt", "kunalpuri-prediqt"} +REQUIRED = { + "daily-closeout": ["type", "date", "user", "agent", "duration_minutes", "aspects_touched"], + "weekly-closeout": ["type", "week", "range", "user"], + "session-log": ["type", "date", "start", "end", "user", "agent", "aspects_touched", "memory_consulted"], + "plan": ["type", "id", "author", "agent", "created", "status", "aspects", "host_files", "within_boundary"], + "review": ["type", "date", "user", "agent", "plan", "adrs", "aspects_touched", "host_files", "status"], + "decision": ["type", "id", "date", "author", "scope", "status", "supersedes", "relates_to", "depends_on", "conflicts_with"], + "experiment": ["type", "id", "created", "author", "aspect", "status", "last_checked"], + "reference-note": ["type", "id", "created", "author", "kind", "status", "aspects"], +} + + +def parse_scalar(value: str): + value = value.strip() + if value in ("[]", ""): + return [] + if value.startswith("[") and value.endswith("]"): + inner = value[1:-1].strip() + if not inner: + return [] + return [x.strip().strip("'\"") for x in inner.split(",")] + if value.lower() == "true": + return True + if value.lower() == "false": + return False + return value.strip("'\"") + + +def read_fm(path: Path): + text = path.read_text(errors="replace") + if not text.startswith("---\n"): + return None, text + end = text.find("\n---", 4) + if end == -1: + return None, text + data = {} + for line in text[4:end].splitlines(): + if not line.strip() or line.lstrip().startswith("#"): + continue + if ":" not in line: + continue + k, v = line.split(":", 1) + data[k.strip()] = parse_scalar(v) + return data, text + + +def all_markdown(): + return sorted(p for p in ROOT.rglob("*.md") if "/updates/archive/" not in str(p)) + + +def parse_boundary_prefixes(): + path = ROOT / "implementation.md" + text = path.read_text() + m = re.search(r"## Integration boundary\n(?P.*?)(?:\n## |\Z)", text, re.S) + prefixes = [] + if not m: + return prefixes + for line in m.group("body").splitlines(): + line = line.strip() + if not line.startswith("- "): + continue + item = line[2:].split(" - ", 1)[0].strip("` ") + if item.endswith("/**/*.pxd"): + prefixes.append((item[:-8], ".pxd")) + elif item.endswith("/**/*.pyx"): + prefixes.append((item[:-8], ".pyx")) + else: + prefixes.append((item, None)) + return prefixes + + +def in_boundary(path: str, prefixes) -> bool: + for prefix, suffix in prefixes: + if suffix: + if path.startswith(prefix) and path.endswith(suffix): + return True + elif path == prefix or path.startswith(prefix.rstrip("/") + "/"): + return True + return False + + +def parse_datetime(value: str): + if not isinstance(value, str): + return None + cleaned = value.replace(" CET", "").replace(" CEST", "") + for fmt in ("%Y-%m-%dT%H:%M:%S", "%Y-%m-%d"): + try: + return datetime.strptime(cleaned[:19] if "T" in cleaned else cleaned[:10], fmt) + except ValueError: + pass + return None + + +def validate() -> tuple[list[str], list[str]]: + errors = [] + warnings = [] + artifacts = [] + + for path in all_markdown(): + fm, text = read_fm(path) + if not fm: + continue + typ = fm.get("type") + if typ: + artifacts.append((path, fm, text)) + for field in REQUIRED.get(typ, []): + if field not in fm: + errors.append(f"{path}: missing required frontmatter field {field}") + if typ in ("daily-closeout", "weekly-closeout", "session-log", "review"): + if fm.get("user") not in ALLOWED_USERS: + errors.append(f"{path}: invalid user {fm.get('user')!r}") + if typ == "decision" and fm.get("author") not in ALLOWED_USERS: + errors.append(f"{path}: invalid author {fm.get('author')!r}") + + aspect_names = {p.name for p in (ROOT / "aspects").iterdir() if p.is_dir()} + adr_ids = {} + for path, fm, _ in artifacts: + if fm.get("type") == "decision": + adr_ids[fm.get("id")] = path + + boundary = parse_boundary_prefixes() + for path, fm, text in artifacts: + typ = fm.get("type") + if typ == "plan": + for aspect in fm.get("aspects", []): + if aspect not in aspect_names: + errors.append(f"{path}: unknown aspect {aspect}") + outside = [f for f in fm.get("host_files", []) if not in_boundary(f, boundary)] + if outside and fm.get("within_boundary") is not False: + errors.append(f"{path}: outside-boundary host_files require within_boundary: false: {outside}") + if fm.get("status") == "approved" and ">" not in text.split("## Approval", 1)[-1]: + errors.append(f"{path}: approved plan lacks verbatim quote block") + if typ == "review": + plan = fm.get("plan") + if isinstance(plan, str) and plan.startswith(".ai/implementations/blast-from-the-past/plans/"): + plan_path = ROOT.parents[2] / plan + pfm, _ = read_fm(plan_path) if plan_path.exists() else (None, "") + if not pfm: + errors.append(f"{path}: review plan does not resolve: {plan}") + elif pfm.get("status") != "approved": + errors.append(f"{path}: review plan is not approved: {plan}") + for adr in fm.get("adrs", []): + if adr not in adr_ids: + errors.append(f"{path}: unknown ADR {adr}") + status = fm.get("status") + signoff = text.split("## Sign-off", 1)[-1] + if status in ("lgtm", "prototype-approved") and ">" not in signoff: + errors.append( + f"{path}: {status} review lacks verbatim quote block" + ) + if status == "prototype-approved": + if fm.get("review_mode") != "prototype-owner": + errors.append( + f"{path}: prototype-approved review requires " + "review_mode: prototype-owner" + ) + if "@kunalpuri-prediqt" not in signoff: + errors.append( + f"{path}: prototype-approved review lacks owner sign-off" + ) + + proc = subprocess.run( + [sys.executable, str(ROOT / "scripts" / "update-decision-graph.py"), "--check"], + text=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + if proc.returncode != 0: + errors.append("decision graph check failed:\n" + proc.stdout.strip()) + + secret_patterns = [ + re.compile(r"-----BEGIN [A-Z ]*PRIVATE KEY-----"), + re.compile(r"AKIA[0-9A-Z]{16}"), + re.compile(r"(?i)(api[_-]?key|token|secret)\s*[:=]\s*['\"]?[A-Za-z0-9_\-]{24,}"), + re.compile(r"://[^/\s:@]+:[^/\s:@]+@"), + ] + for path in ROOT.rglob("*"): + if path.is_file(): + text = path.read_text(errors="ignore") + for pat in secret_patterns: + if pat.search(text): + errors.append(f"{path}: possible secret matched by validator") + break + + current = ROOT / "current.md" + if current.exists(): + current_text = current.read_text() + updated_match = re.search(r"Updated:\s*([^\n]+)", current_text) + updated = parse_datetime(updated_match.group(1)) if updated_match else None + dailies = list((ROOT / "updates" / "daily").glob("*.md")) + if updated and dailies: + newest_daily = max((parse_datetime(read_fm(p)[0].get("date")) for p in dailies if read_fm(p)[0]), default=None) + if newest_daily and updated < newest_daily: + warnings.append("current.md Updated timestamp predates newest daily closeout") + + now = datetime(2026, 6, 15, 7, 19, 8) + for path, fm, _ in artifacts: + if fm.get("type") == "experiment" and fm.get("status") == "running": + last = parse_datetime(fm.get("last_checked")) + if last and now - last > timedelta(hours=48): + warnings.append(f"{path}: running experiment last_checked older than 48h") + + return errors, warnings + + +def main() -> int: + errors, warnings = validate() + if errors: + print("validate-memory: FAILED") + for error in errors: + print(f"ERROR: {error}") + else: + print("validate-memory: PASS") + for warning in warnings: + print(f"WARNING: {warning}") + return 1 if errors else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.ai/implementations/blast-from-the-past/skills/coding-style.md b/.ai/implementations/blast-from-the-past/skills/coding-style.md new file mode 100644 index 000000000..9913cf8c6 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/skills/coding-style.md @@ -0,0 +1,17 @@ +# Skill: Coding Style + +## When to Use + +Use when planning or editing host Cython/Python code for `blast-from-the-past`. + +## Rules + +- Honor existing PySPH Cython style and public surfaces. +- Keep `.pxd` declarations and `.pyx` implementations synchronized. +- Do not introduce new format/lint tooling without an ADR. +- Use clear names for benchmarks and Warp prototypes; avoid encoding performance claims in names. +- Add comments only where GPU/Warp/Cython ownership or lifetime is not obvious. + +## Required Closeout + +Record host files touched, tests run, and any style deviations in the session log. diff --git a/.ai/implementations/blast-from-the-past/skills/debugging.md b/.ai/implementations/blast-from-the-past/skills/debugging.md new file mode 100644 index 000000000..5d7219bed --- /dev/null +++ b/.ai/implementations/blast-from-the-past/skills/debugging.md @@ -0,0 +1,16 @@ +# Skill: Debugging + +## When to Use + +Use when diagnosing GPU, Cython, memory, or benchmark failures. + +## Rules + +- First preserve the failing command, inputs, environment, and exact error. +- Separate build failures, code-generation failures, runtime GPU failures, and numerical mismatches. +- For GPU memory issues, record ownership and transfer assumptions in `particle-memory`. +- For NNPS mismatches, record destination/source array, particle counts, dtype, and neighbor count differences. + +## Required Closeout + +Log the failure mode, suspected layer, evidence, and next concrete reproduction command. diff --git a/.ai/implementations/blast-from-the-past/skills/long-running-tasks.md b/.ai/implementations/blast-from-the-past/skills/long-running-tasks.md new file mode 100644 index 000000000..f9cecbde2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/skills/long-running-tasks.md @@ -0,0 +1,16 @@ +# Skill: Long-Running Tasks + +## When to Use + +Use for tasks over 30 minutes, over 200 LOC, over five files, or any numerical run that must complete. + +## Rules + +- Do not present placeholders as completed work. +- Long runs are tracked as experiments with `status: running`. +- Every boot checks running experiments first and updates `last_checked`. +- If context ends, write exact file:line, test/experiment state, and next action. + +## Required Closeout + +Update the experiment and `current.md` with the next checkpoint. diff --git a/.ai/implementations/blast-from-the-past/skills/testing.md b/.ai/implementations/blast-from-the-past/skills/testing.md new file mode 100644 index 000000000..5b35fa816 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/skills/testing.md @@ -0,0 +1,22 @@ +# Skill: Testing + +## When to Use + +Use when selecting validation commands, benchmarks, or correctness checks. + +## Commands + +- Default tests: `python -m pytest -m "not slow" pysph` +- Make alias: `make test` +- Full tests: `python -m pytest pysph` or `make testall` +- Parallel/Zoltan tier: `python -m pytest -v -m 'slow or parallel'` + +## Rules + +- For performance claims, create an experiment entry with hardware, commit, command, inputs, repeated timings, and correctness checks. +- Compare Warp paths against an existing PySPH baseline before claiming speedup. +- Confirm with team: first concrete benchmark cases and acceptance thresholds. + +## Required Closeout + +Paste raw command output or benchmark numbers into the relevant experiment/review. diff --git a/.ai/implementations/blast-from-the-past/skills/working-with-host-code.md b/.ai/implementations/blast-from-the-past/skills/working-with-host-code.md new file mode 100644 index 000000000..59210dff0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/skills/working-with-host-code.md @@ -0,0 +1,17 @@ +# Skill: Working with Host Code + +## When to Use + +Use before reading or modifying host files. + +## Rules + +- Stay inside the integration boundary in `.ai/implementations/blast-from-the-past/implementation.md`. +- Do not catalogue the host beyond what the task needs. +- Propose an ADR before changing public Cython ABI/API surfaces. +- Do not copy secrets into `.ai/`; reference secret locations by path only. +- If a plan touches outside-boundary files, set `within_boundary: false` and require a boundary amendment at review time. + +## Required Closeout + +Record host files consulted or changed and whether the boundary remained truthful. diff --git a/.ai/implementations/blast-from-the-past/spec/README.md b/.ai/implementations/blast-from-the-past/spec/README.md new file mode 100644 index 000000000..26eed5c8b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/README.md @@ -0,0 +1,24 @@ +# Specification Index + +This directory contains implementation-scoped specifications for +`blast-from-the-past`. + +## Codebase-Level Context + +- `codebase-understanding/`: curated implementation-facing digest of the + top-level `CODEBASE_UNDERSTANDING.md` report. + +## Migration Targets + +- `particle-array/`: solver-agnostic specification for ParticleArray storage, + mutation, host/device synchronization, and the first Warp device mirror. +- `nnps/`: solver-agnostic specification for nearest-neighbor particle search, + domain updates, caching, and the next Warp migration layer. + +## Reading Order + +1. `codebase-understanding/00-overview.md` +2. `codebase-understanding/01-runtime-flow.md` +3. `particle-array/00-overview.md` +4. `nnps/00-overview.md` +5. `codebase-understanding/03-gpu-migration-map.md` diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/00-overview.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/00-overview.md new file mode 100644 index 000000000..d7237af21 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/00-overview.md @@ -0,0 +1,52 @@ +# Codebase Understanding Overview + +## Source + +This spec layer distills the repository-level `CODEBASE_UNDERSTANDING.md` +document into implementation-scoped guidance for `blast-from-the-past`. + +The source document describes PySPH as a general-purpose SPH framework with +performance-sensitive code generated or compiled through Cython, OpenCL, CUDA, +OpenMP, MPI, and optional Zoltan paths. + +## High-Level Product Shape + +PySPH is not a single solver binary. It is a framework where users: + +1. create particle arrays, +2. select or write schemes and equations, +3. let `Application` build solver, domain, NNPS, integrator, and compiler + objects, +4. run a timestep loop, +5. write particle-output files for visualization and post-processing. + +The GPU migration must therefore preserve framework extensibility. The target +is not one CUDA-only solver path; it is a backend path that can eventually serve +many schemes. + +## Core Subsystems + +The source understanding identifies five core runtime subsystems: + +- `pysph/base`: ParticleArray, typed arrays, kernels, domain managers, NNPS, GPU + NNPS exports. +- `pysph/sph`: Equation abstraction, schemes, integrators, backend code + generation, compiler helpers. +- `pysph/solver`: Application lifecycle, solver loop, output, callbacks, command + orchestration. +- `pysph/parallel`: MPI/Zoltan particle exchange, load balancing, and remote + particle management. +- `pysph/tools`: CLI, examples, post-processing, VTK/XDMF utilities. + +## Migration Implication + +The GPU path must be staged: + +1. Particle storage/mirror semantics. +2. Domain and NNPS on device. +3. Equation and integrator kernel consumption of device data. +4. Solver/Application selection and end-to-end examples. +5. Parallel and output correctness. + +Skipping directly to equation kernels would leave neighbor search and domain +updates as synchronization bottlenecks. diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/01-runtime-flow.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/01-runtime-flow.md new file mode 100644 index 000000000..cac359b45 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/01-runtime-flow.md @@ -0,0 +1,51 @@ +# Runtime Flow + +## Application Flow + +The source report identifies the main runtime path: + +```text +pysph console script +-> pysph.tools.cli.main +-> pysph run or direct example execution +-> Application.run() +-> Application.setup() +-> create particles, scheme, equations, solver, domain, NNPS +-> Solver.setup() +-> SPHCompiler.compile() +-> Solver.solve() +``` + +## Solver Loop + +The solver loop performs: + +1. initial output and optional spatial reorder, +2. initial acceleration computation, +3. timestep computation, +4. pre-step callbacks, +5. integrator stage execution, +6. post-stage or post-step callbacks, +7. domain/NNPS updates as needed, +8. output/progress/commands, +9. final output. + +## Generated Code Boundary + +SPH equations are authored in Python classes, normalized into equation groups, +and converted to backend-specific generated code. CPU paths use Cython. +Existing GPU paths use OpenCL/CUDA helpers and Mako templates through Compyle. + +For Warp migration, this means the immediate public API is not just an array +object. The eventual target is generated or staged computation that consumes +ParticleArray and NNPS state without excessive host synchronization. + +## Immediate Integration Boundary + +Until equation-kernel migration begins, Warp work should expose compatibility +through existing host-facing contracts: + +- `ParticleArray` methods and properties, +- `NNPS.update()` and `get_nearest_particles()`, +- explicit backend or NNPS selection in Application setup, +- output paths that can pull host-readable arrays when requested. diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/02-subsystems.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/02-subsystems.md new file mode 100644 index 000000000..34251c8e5 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/02-subsystems.md @@ -0,0 +1,63 @@ +# Subsystem Map + +## Particle Data + +`ParticleArray` is the central mutable state container. It stores named +properties, constants, output-array metadata, stride metadata, and optional GPU +helpers. Standard properties include position, velocity, mass, smoothing length, +density, pressure, acceleration, global id, process id, and particle tag. + +Implementation specs: + +- `../particle-array/` + +## Domain And Neighbor Search + +The domain manager owns physical bounds, periodic/mirror flags, ghost layers, +cell size, and smoothing-length-derived binning state. NNPS builds the local +neighbor-search structure and answers source/destination neighbor queries. + +Implementation specs: + +- `../nnps/` + +## Equation/Scheme Layer + +Equations define operations over destination and source particle arrays. +Schemes assemble common equation groups, kernels, integrators, solver options, +and required particle properties. + +Warp migration consequence: + +- Equation signatures and property names are compile-time contracts. +- Missing particle properties fail during acceleration-evaluator setup. +- Device data layout must preserve the flat property arrays expected by + generated equation code. + +## Solver/Application Layer + +`Application` wires user options, particles, schemes, solver, domain, NNPS, +parallel manager, tools, callbacks, and output. It is the likely host surface +for future `warp` backend selection. + +## Parallel Layer + +Parallel execution depends on MPI and Zoltan. The parallel manager removes stale +remote particles, repartitions or migrates local particles, imports remote +particles, and updates local/remote cell information before local computation. + +Warp migration consequence: + +- NNPS and ParticleArray must tolerate local/remote/ghost ordering changes. +- Parallel correctness should compare by `gid` where ordering differs. + +## Output Layer + +Solver output serializes particle arrays and solver metadata to NPZ or HDF5, +with optional VTK/XDMF conversion. NNPS structures and generated kernels are not +serialized as solver output. + +Warp migration consequence: + +- Device data must be explicitly pulled before output paths read host arrays. +- Output should remain backend-neutral. diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/03-gpu-migration-map.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/03-gpu-migration-map.md new file mode 100644 index 000000000..d3ee9f7bf --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/03-gpu-migration-map.md @@ -0,0 +1,44 @@ +# GPU Migration Map + +## Existing Accelerator Model + +The source document describes PySPH GPU acceleration as generated-code based: + +- Python/Mako helper code generates OpenCL/CUDA evaluator and integrator code. +- `DeviceHelper` mirrors ParticleArray data into Compyle arrays. +- Existing GPU NNPS classes are exported from `pysph/base/gpu_nnps.py`. +- Application setup selects GPU NNPS when OpenCL or CUDA runtime flags are set. + +The repository does not contain handwritten CUDA C kernels as the primary GPU +model. + +## Warp Positioning + +Warp should be introduced as an additive backend path, not as a replacement for +existing OpenCL/CUDA/Compyle behavior. + +Current staged position: + +1. `backend="warp"` ParticleArray mirror exists as a prototype. +2. NNPS solver-agnostic spec exists. +3. Warp NNPS implementation is the next proposed migration layer. + +## Recommended Order + +1. **ParticleArray mirror:** preserve host API, prove dtype/stride/constants, + push/pull, add/remove/extract/append, and alignment. +2. **NNPS correctness:** compare Warp neighbor sets against CPU baselines. +3. **NNPS performance:** implement device-side cell-list or equivalent + structure and separate update/query/cache/readback timing. +4. **Application selection:** expose explicit Warp NNPS/backend flags after the + direct API is stable. +5. **Equation consumption:** decide whether Warp equation kernels consume cached + neighbor lists or launch query kernels directly. +6. **End-to-end examples:** run small examples through solver setup and output. +7. **Parallel verification:** test Zoltan/MPI exchange plus Warp update/query. + +## Avoided Shortcut + +Do not migrate equation kernels before NNPS. The codebase understanding makes +neighbor search part of the hottest solver path, and a CPU NNPS would force +position and neighbor synchronization every timestep. diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/04-build-test-operational-contract.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/04-build-test-operational-contract.md new file mode 100644 index 000000000..c820d66a2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/04-build-test-operational-contract.md @@ -0,0 +1,63 @@ +# Build, Test, And Operational Contract + +## Build Inputs + +The source understanding identifies a hybrid build: + +- Python packaging through `pyproject.toml` and `setup.py`. +- Cython extensions for low-level arrays, NNPS, tools, and parallel paths. +- Optional OpenMP detection. +- Optional MPI/Zoltan detection. +- Local build overrides through `~/.compyle/config.py`. + +For the PQT environment, PySPH is installed editable and Zoltan is available at: + +```text +/home/kunalp/prediqt/zoltan +``` + +## Runtime Backend Controls + +Existing Application options include: + +- OpenMP controls, +- OpenCL/CUDA selection, +- precision selection, +- kernel choice, +- NNPS choice, +- parallel/Zoltan controls, +- output controls. + +Warp integration should follow this pattern by making backend selection explicit +and discoverable rather than hidden inside one data structure. + +## Test Gates + +The source document identifies: + +- default local tests excluding `slow`, +- full test target for all tests, +- separate MPI/Zoltan workflows, +- parallel tests that compare serial and parallel outputs by final time and + particle coordinates keyed by global id. + +Implementation-specific gates: + +- ParticleArray Warp tests: `pysph/base/tests/test_warp_device_helper.py`. +- ParticleArray experiment wrapper: + `experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh`. +- Future NNPS gates should include CPU-vs-Warp neighbor fixtures before solver + integration. + +## Operational Notes + +Generated code and compiler caches live outside the repository, notably under +`~/.pysph` and `~/.compyle`. Reproducible GPU experiments should record: + +- Python environment, +- Warp version, +- GPU device, +- precision mode, +- compiler/cache state when relevant, +- backend/NNPS selection, +- particle count and average neighbor count. diff --git a/.ai/implementations/blast-from-the-past/spec/codebase-understanding/05-risks-open-questions.md b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/05-risks-open-questions.md new file mode 100644 index 000000000..6fe0d067c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/codebase-understanding/05-risks-open-questions.md @@ -0,0 +1,41 @@ +# Risks And Open Questions + +## Build And Environment Risks + +- Cython extension compilation depends on local compiler availability. +- MPI/Zoltan support depends on matching headers, libraries, and Python + packages. +- Generated-code behavior can depend on `~/.compyle/config.py`. +- Existing CI targets Python 3.11 and 3.12, while the PQT environment currently + exercises newer Python behavior. + +## Runtime Risks + +- GPU execution depends on local device availability and backend-specific kernel + generation. +- MPI execution requires both MPI and Zoltan. +- Particle property names and equation signatures are compile/setup-time + contracts. +- Output paths expect host-readable particle arrays. + +## Migration Risks + +- A Warp ParticleArray mirror can appear correct while NNPS still forces + host/device synchronization. +- A Warp NNPS can be geometrically correct while boundary ghost semantics are + wrong. +- Sorted neighbor comparisons may differ by local index unless `gid` behavior is + intentionally handled. +- Parallel exchange can reorder arrays in ways that invalidate cache or spatial + ordering assumptions. + +## Open Questions To Carry Forward + +- What is the first measurable "blazing fast" threshold for NNPS on + `prediqt-02`? +- Should Warp NNPS first expose a brute-force correctness backend or go straight + to a cell-list implementation? +- Should Warp backend selection be global, NNPS-specific, ParticleArray-specific, + or a combination? +- Which end-to-end example is the first acceptance case after NNPS integration: + `elliptical_drop`, `cavity`, or a smaller synthetic application? diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/00-overview.md b/.ai/implementations/blast-from-the-past/spec/nnps/00-overview.md new file mode 100644 index 000000000..b6c504f00 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/00-overview.md @@ -0,0 +1,55 @@ +# NNPS Solver-Agnostic Specification + +## Scope + +This specification describes the nearest-neighbor particle search subsystem in +solver-agnostic terms. It is intended to guide an NVIDIA Warp migration without +assuming any one SPH formulation. + +NNPS sits between particle storage and equation evaluation: + +1. Particle arrays provide positions, smoothing lengths, tags, gids, and array + ordering. +2. Domain management updates bounds, cell size, and periodic or mirror ghost + particles. +3. NNPS builds an acceleration structure over source particle arrays. +4. Equation loops query neighbors for each destination particle. + +## Observed Entry Points + +- `DomainManager` wraps CPU or GPU domain manager selection. +- `NNPSBase` owns particle arrays, particle-array wrappers, radius scale, cache + state, and source/destination query context. +- `NNPS.update()` refreshes bounds, structure storage, particle binning, and + optional neighbor caches. +- `get_nearest_particles(src_index, dst_index, d_idx, nbrs)` returns neighbors + for one destination particle. +- Existing GPU paths expose `GPUNNPS`, `GPUNeighborCache`, + `get_nearest_particles_gpu()`, `find_neighbor_lengths()`, and + `find_nearest_neighbors_gpu()`. + +## Backend-Neutral Contract + +An NNPS implementation must answer this question: + +> Given a destination particle index and a source particle array, which source +> particles lie within the pairwise interaction radius implied by destination +> and source smoothing lengths? + +The inclusion rule used by the CPU baseline is: + +```text +distance(i, j) < radius_scale * h_i +or +distance(i, j) < radius_scale * h_j +``` + +where `i` is the destination particle and `j` is a source particle. + +## Non-Goals For This Spec + +- It does not prescribe SPH equations, kernels, or integrator stages. +- It does not replace MPI/Zoltan partitioning. +- It does not require one acceleration structure. +- It does not decide whether generated equation kernels consume compressed + neighbor lists or invoke query kernels directly. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/01-domain-geometry.md b/.ai/implementations/blast-from-the-past/spec/nnps/01-domain-geometry.md new file mode 100644 index 000000000..8ca44ca12 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/01-domain-geometry.md @@ -0,0 +1,46 @@ +# Domain And Geometry + +## Domain State + +The domain manager owns: + +- physical limits: `xmin`, `xmax`, `ymin`, `ymax`, `zmin`, `zmax` +- periodic flags per axis +- mirror flags per axis +- ghost-layer count +- cell size +- minimum smoothing length +- radius scale +- whether the run is in parallel +- particle-array wrappers and copy-property selection for ghosts + +## Bounds Update + +NNPS bounds are computed from particle coordinates. The observed CPU and GPU +paths expand min/max bounds by one percent of the current coordinate extent and +fall back to a unit-sized box when all extents are near zero. + +The Warp implementation should preserve these semantics so that CPU/Warp +neighbor queries agree in degenerate and small-domain cases. + +## Cell Size + +The domain manager computes the binning cell size from smoothing-length state +and radius scale. NNPS consumes `domain.manager.cell_size` and +`domain.manager.hmin` during update. + +For fixed smoothing length cases, the cell structure may be reused more +aggressively, but the externally visible result must not depend on that +optimization. + +## Dimensionality + +The NNPS constructor receives `dim`, but position storage always uses `x`, `y`, +and `z`. A backend may ignore unused axes when computing flattened cell ids and +valid neighbor cell shifts. + +Required dimensions: + +- 1D: x only +- 2D: x, y +- 3D: x, y, z diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/02-update-timeline.md b/.ai/implementations/blast-from-the-past/spec/nnps/02-update-timeline.md new file mode 100644 index 000000000..b3218e67e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/02-update-timeline.md @@ -0,0 +1,39 @@ +# Update Timeline + +## Normal Serial Update + +The observed CPU `NNPS.update()` sequence is: + +1. Read `cell_size` and `hmin` from the domain manager. +2. Compute coordinate bounds across particle arrays. +3. Refresh structure storage. +4. For each particle array, create an index list for all particles. +5. Bin local particles into the structure. +6. Rebuild neighbor caches when caching is enabled. + +The observed GPU `GPUNNPS.update()` sequence is similar but lets concrete GPU +subclasses perform device-side `_bin()` and `_refresh()`. + +## Domain Update + +`update_domain()` calls `domain.update()`. Domain update may: + +- remove old ghost particles +- recompute cell size +- create periodic or mirror ghosts +- update local domain state + +NNPS must be valid only after the domain update and structure update have both +run for the current particle positions. + +## Solver Loop Placement + +From the high-level solver flow, NNPS update participates in: + +- initial setup before acceleration computation +- post-stage domain updates during integrator stages +- optional spatial reordering +- parallel manager exchange and load-balance updates + +Warp NNPS integration should initially target explicit `nnps.update()` and +query calls before entering full solver-loop orchestration. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/03-data-structures.md b/.ai/implementations/blast-from-the-past/spec/nnps/03-data-structures.md new file mode 100644 index 000000000..24a52079e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/03-data-structures.md @@ -0,0 +1,51 @@ +# Data Structures + +## Required Particle Properties + +At minimum, NNPS needs: + +- `x`, `y`, `z`: coordinates +- `h`: smoothing length +- `tag`: local, remote, ghost, or other particle status +- `gid`: global id, used when sorted neighbor order is requested + +Particle arrays may contain many additional properties, but NNPS must not +depend on them for geometric neighbor selection. + +## Particle Array Wrappers + +The CPU path uses `NNPSParticleArrayWrapper` to access typed property arrays and +to remove tagged particles through the owning `ParticleArray`. + +A Warp NNPS may either: + +- reuse the wrapper for host compatibility and read device arrays from + `pa.gpu`, or +- introduce a Warp-specific wrapper exposing the same conceptual fields. + +The second option is preferable once NNPS stops round-tripping through host +arrays. + +## Structure Storage + +Backend-neutral storage concepts: + +- per-array particle count +- coordinate bounds +- cell size +- cell id per particle +- cell occupancy or head/next links +- optional spatially ordered index permutation +- optional neighbor cache lengths +- optional neighbor cache start offsets +- optional flat neighbor index array + +The CPU linked-list implementation stores `head` per cell and `next` per +particle. Existing GPU implementations store neighbor lengths, prefix-summed +start indices, and flat neighbor lists for cached GPU access. + +## Invalid Sentinel + +The CPU linked-list implementation uses `UINT_MAX` as an invalid particle/cell +link sentinel. Warp kernels should use a documented unsigned sentinel if they +mirror linked-list storage. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/04-query-contract.md b/.ai/implementations/blast-from-the-past/spec/nnps/04-query-contract.md new file mode 100644 index 000000000..669a96d8e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/04-query-contract.md @@ -0,0 +1,69 @@ +# Neighbor Query Contract + +## Single-Particle Query + +The canonical public query is: + +```text +get_nearest_particles(src_index, dst_index, d_idx, nbrs) +``` + +Inputs: + +- source particle-array index +- destination particle-array index +- destination particle index +- mutable neighbor index array + +Output: + +- source-local particle indices appended into or written into `nbrs` + +The result must contain source indices, not gids and not destination indices. + +## Context + +`set_context(src_index, dst_index)` prepares the implementation for repeated +queries between a source/destination pair. CPU implementations store current +source/destination wrappers and structure storage pointers. + +Warp implementations should preserve this concept even if the actual kernels +receive source/destination arrays explicitly. + +## Inclusion Rule + +A source particle `j` is a neighbor of destination particle `i` when: + +```text +norm(x_i - x_j, y_i - y_j, z_i - z_j) < radius_scale * h_i +or +norm(x_i - x_j, y_i - y_j, z_i - z_j) < radius_scale * h_j +``` + +Squared-distance comparisons are allowed and expected for performance, provided +the strict inequality is preserved. + +## Ordering + +Neighbor order is implementation-defined unless `sort_gids=True`. + +When `sort_gids=True`: + +- if source gids are valid, neighbors are sorted by source gid; +- if gids are invalid, neighbors are sorted by local source index. + +Correctness tests should compare sets by default and ordered arrays only when +sorting is requested. + +## Cache + +With caching disabled, a query may compute neighbors immediately. + +With caching enabled, an implementation may precompute: + +- neighbor counts +- prefix sums +- flat neighbor lists + +The cache must be invalidated after particle movement, structural mutation, +domain update, or spatial reordering. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/05-boundary.md b/.ai/implementations/blast-from-the-past/spec/nnps/05-boundary.md new file mode 100644 index 000000000..53740073c --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/05-boundary.md @@ -0,0 +1,36 @@ +# Boundary And Ghost Semantics + +## Tags + +Particle tags affect which particles are local, remote, or ghost. NNPS itself +queries over the particle arrays it receives; domain and parallel managers are +responsible for making the right local/remote/ghost particles present before +NNPS update. + +## Periodic Domains + +For periodic axes, the domain manager creates ghost particles translated by the +domain period. NNPS then treats those ghost particles as ordinary source +particles during geometric queries. + +Required Warp behavior: + +- old periodic ghosts are removed before new ghosts are created; +- copied ghost properties match the domain manager's copy-property selection; +- neighbor results include ghost source indices when ghosts are present; +- real-particle count remains consistent after ghost insertion/removal. + +## Mirror Domains + +The current backend selector warns that mirrored boundaries are unsupported by +existing GPU domain manager paths and falls back to CPU domain management. + +The first Warp NNPS may defer mirror-domain support, but the integration must +fail clearly or fall back explicitly rather than silently returning incomplete +neighbors. + +## Out-Of-Domain Particles + +The domain manager is responsible for detecting or tolerating bounds changes. +NNPS should preserve the current warning behavior when domain size grows by a +large factor between updates. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/06-parallelism.md b/.ai/implementations/blast-from-the-past/spec/nnps/06-parallelism.md new file mode 100644 index 000000000..ccc0cca97 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/06-parallelism.md @@ -0,0 +1,38 @@ +# Parallelism + +## MPI/Zoltan Boundary + +MPI/Zoltan partitioning remains outside NNPS. Before NNPS update, the parallel +manager may: + +- remove stale remote particles +- migrate exported local particles +- import remote particles +- compute ghost/remote particles required by neighbor overlap +- update local and remote cell maps + +NNPS assumes that the particle arrays passed to it already contain the particles +needed for local computation. + +## Local Versus Remote Queries + +Neighbor results are source-local indices into the current process's particle +arrays. They do not identify MPI ranks. + +For parallel comparisons, `sort_gids=True` helps serial and parallel outputs +match by global id rather than by process-local insertion order. + +## Reduction And Timestep + +NNPS does not own timestep reduction. Adaptive timestep minimum reductions +belong to solver/parallel manager code. + +## Warp/MPI First Cut + +The first Warp NNPS should be validated in serial. A follow-up parallel +experiment should verify: + +- remote particles survive device sync and NNPS update; +- neighbor lists over local+remote arrays match CPU NNPS; +- Zoltan load balance followed by Warp NNPS update is deterministic enough for + existing parallel comparison tolerances. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/07-host-contract.md b/.ai/implementations/blast-from-the-past/spec/nnps/07-host-contract.md new file mode 100644 index 000000000..74d738c6e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/07-host-contract.md @@ -0,0 +1,44 @@ +# Host Integration Contract + +## Construction + +An NNPS object is constructed from: + +- dimension +- particle-array list +- radius scale +- ghost layers +- optional domain manager +- cache flag +- sorted-gid flag +- backend-specific options + +The constructor may call `domain.update()` and `update()` immediately, as +existing concrete NNPS classes do. + +## Application Selection + +Application setup currently selects GPU NNPS when OpenCL or CUDA flags are set. +For Warp, integration should make backend selection explicit and avoid +pretending to be the existing CUDA/Compyle backend. + +Candidate host surfaces: + +- `--backend warp` +- `--nnps warp_ll` +- `--nnps warp_bruteforce` +- `backend="warp"` in direct Python construction + +## Output And Serialization + +NNPS state is not solver output. Output files serialize particle arrays and +solver metadata, not cell structures or neighbor caches. + +Warp NNPS therefore only needs to leave particle arrays in a host-readable +state when output or post-processing asks for them. + +## Spatial Reordering + +`spatially_order_particles(pa_index)` obtains an index permutation and aligns +particle properties. Warp implementations should reuse the ParticleArray Warp +alignment semantics and update or invalidate all NNPS caches afterward. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/08-variants.md b/.ai/implementations/blast-from-the-past/spec/nnps/08-variants.md new file mode 100644 index 000000000..ca9b51ded --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/08-variants.md @@ -0,0 +1,43 @@ +# Variants + +## CPU Variants + +Observed CPU-facing NNPS variants include: + +- box sort +- linked list +- spatial hash +- extended spatial hash +- cell indexing +- z-order +- stratified SFC +- octree +- compressed octree +- brute-force fallback behavior + +## Existing GPU Variants + +Existing GPU exports include: + +- `BruteForceNNPS` +- `ZOrderGPUNNPS` +- `StratifiedSFCGPUNNPS` +- `OctreeGPUNNPS` +- `GPUDomainManager` +- `GPUNeighborCache` + +These are Compyle/OpenCL/CUDA-oriented, not Warp-native. + +## Recommended Warp Variant Order + +1. Warp brute force: simplest correctness oracle and device-array plumbing. +2. Warp uniform grid or linked-cell list: first performance-relevant structure. +3. Warp cached flat neighbor lists: bridge to generated equation kernels. +4. Warp spatial reordering: improves memory locality and solver-loop cost. +5. Warp octree or SFC variants: only after simpler structures establish wins. + +## Fixed-H Optimization + +When smoothing lengths are fixed, cell size and some structure allocations can +be reused across updates. This is an optimization only; the correctness +contract remains the pairwise inclusion rule. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/09-verification.md b/.ai/implementations/blast-from-the-past/spec/nnps/09-verification.md new file mode 100644 index 000000000..8d6458613 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/09-verification.md @@ -0,0 +1,55 @@ +# Verification + +## Correctness Fixtures + +Required deterministic fixtures: + +- 1D evenly spaced particles with fixed `h` +- 2D grid with fixed `h` +- 3D small cloud with fixed `h` +- variable smoothing length where gather radius and scatter radius differ +- multiple particle arrays with source/destination indices crossed +- empty source array +- empty destination array +- duplicate positions +- sorted-gid and unsorted neighbor ordering +- periodic boundary ghost inclusion +- post-add/remove ParticleArray mutation followed by NNPS update + +## Baseline Comparisons + +For most fixtures, compare Warp neighbor sets to CPU linked-list or brute-force +NNPS. Compare order only when `sort_gids=True`. + +For cached paths, verify: + +- first query materializes the cache; +- repeated query returns the same neighbors; +- update invalidates the cache; +- mutation followed by update produces the new expected neighbors. + +## Performance Metrics + +Record: + +- particle count +- dimension +- average neighbor count +- smoothing-length mode +- backend +- update time +- query time for all destination particles +- cache build time +- device-to-host readback time, if any +- memory footprint for neighbor lengths, starts, and flat list + +## Success Criteria + +The first Warp NNPS experiment succeeds when: + +- serial correctness fixtures match CPU baselines; +- all results stay device-resident until an explicit host query/readback; +- benchmark output identifies whether time is spent in bounds, binning, query, + cache construction, or readback; +- an agreed particle-count threshold shows a measurable speedup over CPU for + at least one realistic update/query workload. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/10-porting.md b/.ai/implementations/blast-from-the-past/spec/nnps/10-porting.md new file mode 100644 index 000000000..5acd11376 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/10-porting.md @@ -0,0 +1,80 @@ +# Porting Plan Seed + +## Premises + +- ParticleArray now has a Warp device mirror prototype. +- Existing NNPS code expects ParticleArray host properties and Cython wrappers. +- Existing GPU NNPS code is Compyle/OpenCL/CUDA-oriented and uses + `GPUNeighborCache` to bridge GPU and CPU neighbor access. +- The first Warp migration should be additive and should not remove existing + CPU or Compyle GPU paths. + +## Phase 1: NNPS Spec And Experiment + +1. Capture this solver-agnostic NNPS contract. +2. Add CPU-vs-Warp neighbor correctness experiments. +3. Add benchmark scripts that separate update, query, cache, and readback time. + +## Phase 2: Warp Brute Force + +1. [DONE] Implement a minimal Warp NNPS using ParticleArray Warp arrays. +2. [DONE] Provide `update()`, `set_context()`, and + `get_nearest_particles()`. +3. [DONE] Keep a host-compatible neighbor readback path for existing tests. +4. [DONE] Validate source/destination array pairs and variable `h`. +5. [DONE] Avoid one kernel launch and one full flag readback per destination + particle with a cached flat-neighbor-list path. +6. [DONE] Replace brute-force O(N^2) cache construction with a cell-list + implementation baseline. +7. [DONE] Add a narrow device-resident consumer that uses the grid cache for + equation-like work. +8. [DONE] Use the device-resident cache for a real SPH equation: + CubicSpline summation density. +9. [DONE] Add EOS and continuity as the next simple SPH kernels. +10. [DONE] Add inviscid pressure-gradient acceleration. +11. [DONE] Add a minimal one-step WCSPH Euler chain that consumes density, EOS, + and pressure-gradient outputs on the device. +12. [NEXT] Generalize the device-consumption proof into a reusable + equation-loop contract and repeated-step NNPS refresh. + +## Phase 3: Warp Cell List + +1. [DONE] Compute cell ids on the device. +2. [DONE] Build per-cell counts and offsets. +3. [DONE] Scatter particle ids into a flat cell-particle array. +4. [DONE] Query adjacent cells on the device. +5. [DONE] Compare against brute-force Warp and CPU baselines. +6. [DONE] Reduce host readback for bulk cache construction and a neighbor-sum + consumer. +7. [NEXT] Tune cell-list performance and cache reuse across multiple equation + consumers. + +## Phase 4: Solver Integration + +1. Add explicit Application/CLI selection for Warp NNPS. +2. Run a small example with Warp ParticleArray plus Warp NNPS. +3. Keep equation evaluation on the existing backend until the neighbor contract + is stable. + +## Phase 5: Equation Kernel Consumption + +1. [DONE] Prove generated-equation-like kernels can consume cached neighbor + lists directly with `compute_neighbor_sum()`. +2. [DONE] Port and benchmark standard `SummationDensity` as the first real SPH + equation kernel. +3. [DONE] Port and benchmark `IsothermalEOS` plus `ContinuityEquation`. +4. [DONE] Port and benchmark inviscid pressure-gradient acceleration. +5. [DONE] Add a minimal Warp-aware acceleration-to-position step. +6. Add a device-aware repeated-step NNPS refresh. +7. Benchmark end-to-end solver steps. + +## Risks + +- Existing Cython equation paths expect `UIntArray` neighbor results. +- Host readback can hide GPU performance wins. +- Periodic and mirror ghost behavior can create correctness mismatches even + when raw geometric queries are correct. +- Parallel remote-particle exchange may reorder arrays in ways that require + sorted-gid comparisons. +- Repeated GPU stepping needs NNPS update semantics that do not push stale host + positions over device-updated positions. diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/glossary.md b/.ai/implementations/blast-from-the-past/spec/nnps/glossary.md new file mode 100644 index 000000000..66f8b26e1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/glossary.md @@ -0,0 +1,15 @@ +# Glossary + +| Term | Meaning | +| --- | --- | +| NNPS | Nearest-neighbor particle search. | +| Source array | Particle array from which neighbor indices are returned. | +| Destination array | Particle array containing the queried particle. | +| `d_idx` | Destination-local particle index. | +| Neighbor index | Source-local particle index satisfying the interaction radius test. | +| Radius scale | Kernel support multiplier applied to smoothing length `h`. | +| Cell size | Spatial bin size used by cell/hash/list NNPS variants. | +| Cache | Precomputed neighbor lengths, starts, and flat neighbor indices. | +| Ghost particle | Particle copied or synthesized for periodic, mirror, or parallel overlap. | +| Remote particle | Particle imported from another MPI rank for local interaction. | +| Spatial reordering | Alignment of particle properties by an NNPS-provided locality permutation. | diff --git a/.ai/implementations/blast-from-the-past/spec/nnps/open-questions.md b/.ai/implementations/blast-from-the-past/spec/nnps/open-questions.md new file mode 100644 index 000000000..bb71d726f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/nnps/open-questions.md @@ -0,0 +1,13 @@ +# Open Questions + +- [UNKNOWN] Should the first Warp NNPS target be a brute-force baseline for + correctness, or a uniform-grid/cell-list implementation for immediate + performance relevance? +- [UNKNOWN] Should `warp` appear as a new `--nnps` value, a new global backend + option, or both? +- [UNKNOWN] How much of the existing `GPUNeighborCache` CPU readback protocol + must be preserved for generated equation code during the first integration? +- [UNKNOWN] Is mirrored-boundary support required in the first Warp NNPS, or can + it follow periodic and non-periodic domains? +- [UNKNOWN] What particle counts and neighbor densities define "blazing fast" + for NNPS update/query benchmarks on `prediqt-02`? diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/00-overview.md b/.ai/implementations/blast-from-the-past/spec/particle-array/00-overview.md new file mode 100644 index 000000000..e1f1a8c30 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/00-overview.md @@ -0,0 +1,37 @@ +# 00 Overview - Particle Array + +## Scope + +- [OBSERVED] `ParticleArray` is the main PySPH data structure representing a homogeneous collection of particles with arbitrary named properties stored as `BaseArray` instances; this is described in the user guide at `docs/source/using_pysph.rst:59-76`. +- [OBSERVED] This spec focuses on `pysph/base/particle_array.pxd`, `pysph/base/particle_array.pyx`, `pysph/base/device_helper.py`, and construction/serialization helpers in `pysph/base/utils.py`; these files define the class, host storage, device mirroring, and metadata reconstruction paths at `pysph/base/particle_array.pxd:37-138`, `pysph/base/particle_array.pyx:109-157`, `pysph/base/device_helper.py:47-70`, and `pysph/base/utils.py:466-512`. +- [INFERRED] The solver-agnostic object here is not a numerical solver, flux, equation, or integrator; it is a mutable particle table plus host/device synchronization protocol used by solvers and neighbor search. +- [OBSERVED] PySPH's default particle properties include positions, velocities, smoothing length, mass, density, pressure, acceleration-like fields, `gid`, `pid`, and `tag`; their documented types are double for most physical fields, unsigned int for `gid`, and int for `pid`/`tag` at `docs/source/using_pysph.rst:78-89`. + +## Existing Role + +- [OBSERVED] `ParticleArray` owns dictionaries for `properties`, `constants`, `default_values`, `stride`, `output_property_arrays`, `lb_props`, plus `backend`, `gpu`, `time`, and `num_real_particles`; this is declared in the Cython header at `pysph/base/particle_array.pxd:37-73`. +- [OBSERVED] The constructor resolves a backend, initializes host properties, adds constants, configures load-balance/output metadata, and attaches `DeviceHelper` when the backend is not `cython` at `pysph/base/particle_array.pyx:109-157`. +- [OBSERVED] `DeviceHelper` manages device-side mirrors for ParticleArray properties/constants and exposes push/pull/update operations at `pysph/base/device_helper.py:47-70` and `pysph/base/device_helper.py:200-227`. +- [OBSERVED] Solver output obtains particle metadata and property arrays through `get_particles_info()` and `get_property_arrays()` before dumping data at `pysph/base/utils.py:466-497` and `pysph/solver/output.py:53-78`. +- [OBSERVED] NNPS update bins all particles reported by `pa.get_number_of_particles()` after particle movement and domain refresh at `pysph/base/nnps_base.pyx:1471-1506`. + +## Non-Goals For This Spec + +- [INFERRED] This spec does not define SPH governing equations because ParticleArray does not implement numerical residuals or time integration. +- [INFERRED] This spec does not define NNPS algorithms beyond the ParticleArray contract they consume. +- [INFERRED] This spec does not prescribe a final Warp architecture; it identifies the contract that a Warp migration must preserve. + +## Representative Call Graph + +- [OBSERVED] Application creation path: `Application._create_particles()` creates or loads particles, records metadata with `utils.get_particles_info()`, broadcasts metadata in MPI runs, and creates dummy arrays on non-root ranks at `pysph/solver/application.py:859-920`. +- [OBSERVED] User/helper creation path: `get_particle_array()` builds default property descriptors, merges user properties, chooses property dtypes/defaults, constructs `ParticleArray`, and sets output arrays at `pysph/base/utils.py:47-149`. +- [OBSERVED] ParticleArray initialization path: `ParticleArray.__init__()` calls `_initialize()`, which computes the particle count, ravels/broadcasts input data, adds properties, and calls `align_particles()` at `pysph/base/particle_array.pyx:109-157` and `pysph/base/particle_array.pyx:225-293`. +- [OBSERVED] Device path: for non-cython backends, `ParticleArray` creates `DeviceHelper`, which materializes device arrays for properties/constants and tracks `num_real_particles` at `pysph/base/particle_array.pyx:149-157` and `pysph/base/device_helper.py:56-70`. +- [OBSERVED] Output path: output code calls `get_particles_info()` and `get_property_arrays()`, and `get_property_arrays()` pulls requested GPU arrays first when the backend is not CPU-only at `pysph/solver/output.py:53-78` and `pysph/base/particle_array.pyx:344-386`. + +## Minimal Invariants + +- [OBSERVED] A property is a one-dimensional array; strided properties are represented by a flat one-dimensional array plus a `stride` entry at `docs/source/using_pysph.rst:94-104`. +- [OBSERVED] `tag`, `pid`, and `gid` are baseline properties after `clear()` at `pysph/base/particle_array.pyx:395-400`. +- [OBSERVED] `align_particles()` moves Local-tagged particles to the start and updates `num_real_particles` at `pysph/base/particle_array.pyx:1092-1173`. +- [OBSERVED] Constants are separate from particle properties and do not resize when particles are added at `docs/source/using_pysph.rst:148-175` and `pysph/base/tests/test_particle_array.py:805-817`. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/01-mesh-geometry.md b/.ai/implementations/blast-from-the-past/spec/particle-array/01-mesh-geometry.md new file mode 100644 index 000000000..853676c07 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/01-mesh-geometry.md @@ -0,0 +1,25 @@ +# 01 Mesh And Geometry + +## Geometry Model + +- [OBSERVED] PySPH particles are points assigned physical properties, and a homogeneous collection is represented by `ParticleArray` at `docs/source/design/working_with_particles.rst:7-13`. +- [OBSERVED] User-facing particle positions are ordinary properties such as `x`, `y`, and `z`; they are created through `get_particle_array()` and accessed as ParticleArray attributes at `docs/source/design/working_with_particles.rst:29-48`. +- [OBSERVED] Smoothing length `h` is a default double property documented with the other default properties at `docs/source/using_pysph.rst:78-89`. +- [INFERRED] ParticleArray itself is meshfree storage: it does not own cells, faces, connectivity, control volumes, or shape functions. + +## Mesh Topology + +- [OBSERVED] NNPS and domain managers build spatial structures after particles move; NNPS update computes bounds, refreshes its data structure, then bins particle indices at `pysph/base/nnps_base.pyx:1471-1506`. +- [INFERRED] Cell lists, octrees, and periodic/mirror ghost construction are downstream consumers of ParticleArray, not part of the ParticleArray data model. +- [UNKNOWN] It is not yet specified whether a Warp ParticleArray should expose geometry arrays directly to a future Warp NNPS or preserve the current ParticleArray/NNPS wrapper boundary first. + +## Coordinate And Layout Assumptions + +- [OBSERVED] Property data are flat arrays, and strided properties are represented as a flat array whose logical particle count is array length divided by stride at `docs/source/using_pysph.rst:94-104` and `pysph/base/particle_array.pyx:423-437`. +- [OBSERVED] Tests expect a stride-3 property with four particles to have flat length 12 and logical count 4 at `pysph/base/tests/test_particle_array.py:180-195`. +- [INFERRED] A Warp port should treat ParticleArray as structure-of-arrays with optional fixed per-particle stride, not as array-of-structs, unless a separate compatibility layer preserves flat property semantics. + +## Geometry-Specific Non-Applicability + +- [INFERRED] There are no face normals, finite-volume areas, element volumes, Riemann states, reconstruction stencils, or mesh boundary IDs in ParticleArray. +- [UNKNOWN] Geometry invariants required by all PySPH equations are not fully enumerated here; this spec only covers the base array layer. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/02-timeline.md b/.ai/implementations/blast-from-the-past/spec/particle-array/02-timeline.md new file mode 100644 index 000000000..2369cd3da --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/02-timeline.md @@ -0,0 +1,25 @@ +# 02 Timeline + +## Lifecycle Stages + +1. [OBSERVED] Construction starts from user arrays or property descriptor dictionaries passed to `ParticleArray` or `get_particle_array()`; scalar values are accepted and broadcast by tests at `pysph/base/tests/test_particle_array.py:1020-1030`. +2. [OBSERVED] `get_particle_array()` fills default descriptors for common properties, chooses int/uint/double types, sets `gid` default to `UINT_MAX`, and marks selected output arrays at `pysph/base/utils.py:47-149`. +3. [OBSERVED] `ParticleArray._initialize()` clears existing state, derives particle count from the first/maximum property size, ravels input data, broadcasts scalar data to the inferred count, calls `add_property()`, and aligns particles at `pysph/base/particle_array.pyx:225-293`. +4. [OBSERVED] For non-cython backends, the constructor attaches `DeviceHelper`, which creates device arrays for all current properties and constants at `pysph/base/particle_array.pyx:149-157` and `pysph/base/device_helper.py:56-70`. +5. [OBSERVED] Runtime mutation can add/remove/extend/resize/append/extract/copy properties and particles through ParticleArray methods declared at `pysph/base/particle_array.pxd:75-138`. +6. [OBSERVED] Alignment partitions Local-tagged particles first and updates `num_real_particles` at `pysph/base/particle_array.pyx:1092-1173`. +7. [OBSERVED] Device-backed ParticleArray methods delegate selected operations to `DeviceHelper` when `self.gpu is not None and self.backend is not 'cython'`, for example remove/add/extract/align/resize at `pysph/base/particle_array.pyx:439-505`, `pysph/base/particle_array.pyx:531-602`, `pysph/base/particle_array.pyx:1237-1277`, `pysph/base/particle_array.pyx:1092-1131`, and `pysph/base/particle_array.pyx:1438-1445`. +8. [OBSERVED] Output obtains metadata with `get_particles_info()` and property arrays with `get_property_arrays()` before writing, and `get_property_arrays()` pulls device data first for non-cython backends at `pysph/base/utils.py:466-497`, `pysph/solver/output.py:53-78`, and `pysph/base/particle_array.pyx:344-386`. +9. [OBSERVED] Load/restart reconstructs ParticleArray objects from saved property metadata and arrays in NumPy/HDF output loaders at `pysph/solver/output.py:127-162` and `pysph/solver/output.py:195-221`. + +## Representative Single-Step Context + +- [OBSERVED] NNPS update is called after particles move, assumes each processor already has needed local particle information in parallel runs, computes/refreshes local data structures, and bins all particles reported by ParticleArray at `pysph/base/nnps_base.pyx:1471-1506`. +- [INFERRED] In a solver time step, ParticleArray provides mutable property arrays before and after equations/integrators move particles; the numerical update itself is outside this layer. +- [OBSERVED] Periodic/mirror domain update removes old Ghost-tagged particles and can create new ghost images before binning at `pysph/base/nnps_base.pyx:386-403` and `pysph/base/nnps_base.pyx:450-470`. + +## Sync Timeline + +- [OBSERVED] `DeviceHelper.push()` copies selected or all host arrays to device, and tests cover both selective and full push at `pysph/base/device_helper.py:219-227`, `pysph/base/tests/test_device_helper.py:51-72`, and `pysph/base/tests/test_device_helper.py:74-95`. +- [OBSERVED] `DeviceHelper.pull()` copies selected or all device arrays back to host and synchronizes `num_real_particles`, with tests at `pysph/base/device_helper.py:200-217`, `pysph/base/tests/test_device_helper.py:97-118`, and `pysph/base/tests/test_device_helper.py:120-141`. +- [INFERRED] A Warp migration must define when host data become stale and whether `get()`, attribute access, output, and Cython wrapper reads trigger implicit synchronization. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/03-data-structures.md b/.ai/implementations/blast-from-the-past/spec/particle-array/03-data-structures.md new file mode 100644 index 000000000..36be22032 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/03-data-structures.md @@ -0,0 +1,44 @@ +# 03 Data Structures + +## ParticleArray Object + +- [OBSERVED] `ParticleArray` is a Cython extension class declared with `backend`, `properties`, `property_arrays`, `stride`, `output_property_arrays`, `constants`, `default_values`, `name`, `num_real_particles`, `lb_props`, `gpu`, and `time` fields at `pysph/base/particle_array.pxd:37-73`. +- [OBSERVED] `properties` maps property names to `BaseArray` subclasses, while `constants` maps names to fixed-size arrays at `pysph/base/particle_array.pyx:109-157` and `pysph/base/particle_array.pyx:823-850`. +- [OBSERVED] `default_values` stores per-property fill values used when adding/extending particles at `pysph/base/particle_array.pyx:531-602` and `pysph/base/particle_array.pyx:664-689`. +- [OBSERVED] `stride` stores per-property logical width and defaults to 1 when absent, as used by particle count, get, add, append, align, copy, and resize paths at `pysph/base/particle_array.pyx:423-437`, `pysph/base/particle_array.pyx:704-765`, `pysph/base/particle_array.pyx:531-602`, `pysph/base/particle_array.pyx:604-662`, `pysph/base/particle_array.pyx:1092-1173`, and `pysph/base/particle_array.pyx:1438-1449`. + +## Property Storage + +- [OBSERVED] `add_property()` creates properties from explicit data, scalar defaults, or empty arrays; it validates sizes against existing particle count and stride at `pysph/base/particle_array.pyx:851-1016`. +- [OBSERVED] `_create_carray()` maps type strings to `DoubleArray`, `LongArray`, `FloatArray`, `IntArray`, and `UIntArray`, then fills defaults at `pysph/base/particle_array.pyx:1020-1055`. +- [OBSERVED] `_create_c_array_from_npy_array()` maps NumPy int32/int64 to `LongArray`, float32 to `FloatArray`, and double to `DoubleArray` at `pysph/base/particle_array.pyx:1065-1090`. +- [OBSERVED] Tests verify scalar broadcasting, flattened 2D input, strided property length, and typed int properties at `pysph/base/tests/test_particle_array.py:150-178`, `pysph/base/tests/test_particle_array.py:469-533`, and `pysph/base/tests/test_particle_array.py:1020-1030`. +- [INFERRED] Property storage is SoA-like because each named property has its own contiguous array; strided properties are contiguous per property, not interleaved across property names. + +## Baseline Properties And Tags + +- [OBSERVED] `clear()` resets ParticleArray to contain `tag`, `pid`, and `gid` properties with defaults at `pysph/base/particle_array.pyx:395-400`. +- [OBSERVED] The `ParticleTag` enum defines `Local=0`, `Remote=1`, and `Ghost=2` at `pysph/base/particle_array.pxd:24-28`. +- [OBSERVED] Utility wrappers expose local/remote/ghost tag values through `ParticleTAGS` at `pysph/base/utils.py:15-20`. +- [OBSERVED] User docs describe `gid` as a globally unique index for load balancing, `pid` as the processor id, and `tag` as an integer used for local/remote/ghost classification at `docs/source/using_pysph.rst:106-132` and `docs/source/using_pysph.rst:300-308`. + +## Constants + +- [OBSERVED] Constants are added with `add_constant()`, cannot clash with existing property/constant names, are raveled into a carray, and are mirrored to GPU helpers if present at `pysph/base/particle_array.pyx:823-850`. +- [OBSERVED] Tests verify constants can be added in the constructor, read through `get()`, updated with `set()`, retrieved through `get_carray()`, cloned, and kept fixed when particles are added at `pysph/base/tests/test_particle_array.py:758-845` and `pysph/base/tests/test_particle_array.py:847-884`. +- [INFERRED] A Warp port should preserve constants as named, non-particle-count-sized arrays because equations and output code may treat them separately from properties. + +## DeviceHelper Mirror + +- [OBSERVED] `DeviceHelper` stores a reference to the ParticleArray, a backend name, dtype policy, `num_real_particles`, and `_data` mapping, then creates device arrays for each property and constant at `pysph/base/device_helper.py:56-70`. +- [OBSERVED] `_get_array()` converts float/double arrays to the configured float precision and preserves integer dtype before creating a compyle `Array` at `pysph/base/device_helper.py:72-93`. +- [OBSERVED] `update_prop()` and `update_const()` keep `properties`, `constants`, `_data`, and ParticleArray host metadata in sync at `pysph/base/device_helper.py:144-179`. +- [OBSERVED] DeviceHelper tests cover mirror creation, selective/full push, selective/full pull, min/max, property add/remove, resize/extend/remove, align, append, empty clone, and extract at `pysph/base/tests/test_device_helper.py:29-403`. +- [INFERRED] DeviceHelper is the closest existing abstraction boundary for a Warp-backed mirror, but its compyle-specific `Array` type and kernel generation are implementation details to isolate. + +## Serialization Schema + +- [OBSERVED] `get_particles_info()` records each property name, c type, default, stride, and null data placeholder, plus constants, output arrays, and load-balance properties at `pysph/base/utils.py:466-497`. +- [OBSERVED] `create_dummy_particles()` reconstructs empty ParticleArray replicas from that metadata at `pysph/base/utils.py:500-512`. +- [OBSERVED] Output loaders reconstruct ParticleArray from saved property metadata and constants at `pysph/solver/output.py:127-162` and `pysph/solver/output.py:195-221`. +- [INFERRED] Any Warp migration must preserve this schema unless output and MPI dummy creation are migrated at the same time. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/04-boundary.md b/.ai/implementations/blast-from-the-past/spec/particle-array/04-boundary.md new file mode 100644 index 000000000..7bee65675 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/04-boundary.md @@ -0,0 +1,21 @@ +# 04 Boundary + +## Boundary Concepts Present In ParticleArray + +- [OBSERVED] ParticleArray has no geometric boundary-condition object; it stores boundary-relevant classification through `tag`, where `Local`, `Remote`, and `Ghost` are defined at `pysph/base/particle_array.pxd:24-28`. +- [OBSERVED] Documentation identifies Remote-tagged particles as particles assigned to but not owned by the processor, and Ghost-tagged particles as locally created boundary-condition particles at `docs/source/using_pysph.rst:300-308`. +- [OBSERVED] ParticleArray can remove particles by tag via `remove_tagged_particles()` at `pysph/base/particle_array.pyx:506-529`. +- [OBSERVED] Tests verify removing tagged particles for Local, Remote, and Ghost-like tag values, including strided properties, at `pysph/base/tests/test_particle_array.py:400-467`. + +## Boundary Consumers + +- [OBSERVED] Domain manager code removes old Ghost-tagged particles before creating periodic/mirror ghosts at `pysph/base/nnps_base.pyx:386-403` and `pysph/base/nnps_base.pyx:450-470`. +- [OBSERVED] Mirror-boundary handling extracts particles, modifies copied positions/velocities, and appends the generated particle arrays back into the original array at `pysph/base/nnps_base.pyx:520-610`. +- [INFERRED] Boundary condition generation depends on ParticleArray mutation primitives (`extract_particles`, `append_parray`, `remove_tagged_particles`) rather than on ParticleArray owning boundary-condition logic. + +## Porting Boundary Rules + +- [OBSERVED] `align_particles()` uses tag values to move Local particles to the beginning and updates `num_real_particles` at `pysph/base/particle_array.pyx:1092-1173`. +- [OBSERVED] `get()` defaults to returning only real/local particles, using `num_real_particles` as the slice bound at `pysph/base/particle_array.pyx:704-765`. +- [INFERRED] A Warp-backed boundary path must preserve the tag partition invariant before host code requests local-only slices, output with `only_real`, or NNPS-local indexing. +- [UNKNOWN] It is not yet decided whether boundary ghost generation itself should remain Cython/host-side while only storage primitives move to Warp. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/05-parallelism.md b/.ai/implementations/blast-from-the-past/spec/particle-array/05-parallelism.md new file mode 100644 index 000000000..1d1f385c1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/05-parallelism.md @@ -0,0 +1,23 @@ +# 05 Parallelism + +## MPI/Distributed State + +- [OBSERVED] User docs describe `gid` as globally unique for parallel load balancing and `pid` as the processor id at `docs/source/using_pysph.rst:106-112`. +- [OBSERVED] `get_lb_props()` returns load-balance properties, defaulting to all properties when `lb_props` is empty at `pysph/base/particle_array.pyx:411-421`. +- [OBSERVED] Application particle creation broadcasts metadata from rank 0 and creates dummy particle arrays on non-root ranks at `pysph/solver/application.py:859-920`. +- [OBSERVED] `get_particles_info()` records `lb_props` and `create_dummy_particles()` restores them at `pysph/base/utils.py:466-512`. +- [INFERRED] A storage migration cannot ignore `gid`, `pid`, `tag`, or `lb_props` because they are part of distributed particle ownership and reconstruction. + +## Device Parallelism + +- [OBSERVED] Existing GPU backends are represented by non-cython `backend` values and use `DeviceHelper` to delegate operations such as alignment, removal, add, append, extend, extract, and resize at `pysph/base/particle_array.pyx:439-505`, `pysph/base/particle_array.pyx:531-602`, `pysph/base/particle_array.pyx:604-662`, `pysph/base/particle_array.pyx:664-689`, `pysph/base/particle_array.pyx:1092-1131`, `pysph/base/particle_array.pyx:1237-1277`, and `pysph/base/particle_array.pyx:1438-1445`. +- [OBSERVED] DeviceHelper implements alignment by generating index arrays and applying them to every property, with separate handling for strided properties at `pysph/base/device_helper.py:107-142` and `pysph/base/device_helper.py:249-323`. +- [OBSERVED] DeviceHelper implements particle removal by generating boolean masks and applying strided index maps at `pysph/base/device_helper.py:339-461`. +- [OBSERVED] DeviceHelper implements add/append/extend/extract operations on device arrays at `pysph/base/device_helper.py:463-672`. +- [INFERRED] Warp kernels will need equivalents for prefix/partition, gather/scatter, resize/fill, and strided copy primitives. + +## Precision And Backend Variants + +- [OBSERVED] GPU tests set `cfg.use_double = True` before testing OpenCL and CUDA ParticleArray backends at `pysph/base/tests/test_particle_array.py:1106-1139`. +- [OBSERVED] DeviceHelper reads `get_config().use_double` and sets float precision accordingly at `pysph/base/device_helper.py:47-77`. +- [UNKNOWN] It is not yet confirmed whether the first Warp implementation must support both float32 and float64 across all target GPUs. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/06-host-contract.md b/.ai/implementations/blast-from-the-past/spec/particle-array/06-host-contract.md new file mode 100644 index 000000000..1c65ed547 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/06-host-contract.md @@ -0,0 +1,31 @@ +# 06 Host Contract + +## Public API Surface + +- [OBSERVED] The declared Cython API includes methods for time, naming, properties, constants, load-balance properties, particle counts, mutation, alignment, cloning, extraction, copy, zeroing, pid, min/max, and resize at `pysph/base/particle_array.pxd:75-138`. +- [OBSERVED] Attribute access maps known property/constant names to arrays and raises `AttributeError` for missing names at `pysph/base/particle_array.pyx:159-177`; tests cover attribute reads and writes at `pysph/base/tests/test_particle_array.py:242-288`. +- [OBSERVED] `get()` returns selected properties/constants as NumPy arrays or tuples, defaults to only real particles, and returns arrays that do not own their data at `pysph/base/particle_array.pyx:704-765`. +- [OBSERVED] `get_carray()` returns the underlying carray for a property or constant and raises `KeyError` otherwise at `pysph/base/particle_array.pyx:811-821`. +- [INFERRED] Existing Cython callers may require `BaseArray` objects from `get_carray()`, so a Warp port that replaces host storage must either preserve these objects or introduce a compatibility boundary with a separate plan. + +## Mutation Semantics + +- [OBSERVED] `remove_particles()` removes selected indices, handles stride, validates oversized removal, and can align afterward; tests cover normal, strided, oversized, and out-of-range cases at `pysph/base/particle_array.pyx:439-505` and `pysph/base/tests/test_particle_array.py:290-337`. +- [OBSERVED] `add_particles()` appends supplied property values, fills omitted properties with defaults, supports empty adds, and can align afterward at `pysph/base/particle_array.pyx:531-602` and `pysph/base/tests/test_particle_array.py:339-399`. +- [OBSERVED] `append_parray()` appends another ParticleArray, adds missing properties with defaults, optionally updates constants, and can align afterward at `pysph/base/particle_array.pyx:604-662` and `pysph/base/tests/test_particle_array.py:678-718`. +- [OBSERVED] `extract_particles()` creates or populates a destination ParticleArray, extends it, copies selected strided values, and can align afterward at `pysph/base/particle_array.pyx:1237-1320` and `pysph/base/tests/test_particle_array.py:886-979`. +- [OBSERVED] `resize()` resizes all property arrays but does not update the particle count until alignment/length semantics are applied by callers at `pysph/base/particle_array.pyx:1438-1449`. + +## Synchronization Contract + +- [OBSERVED] `get_property_arrays()` pulls requested device properties before returning host arrays when `backend` is not `cython` at `pysph/base/particle_array.pyx:344-386`. +- [OBSERVED] ParticleArray exposes `set_device_helper()` to replace or attach a helper at `pysph/base/particle_array.pyx:767-770`. +- [OBSERVED] DeviceHelper tests require host changes to become visible on device after `push()` and device changes to become visible on host after `pull()` at `pysph/base/tests/test_device_helper.py:51-141`. +- [INFERRED] Warp integration must make host/device authority explicit for every API that returns host arrays, modifies device arrays, or serializes particles. + +## Compatibility Constraints + +- [OBSERVED] Pickle roundtrip stores properties/defaults/stride/constants and restores `num_real_particles` by counting Local tags at `pysph/base/particle_array.pyx:179-224`; tests cover pickle roundtrip at `pysph/base/tests/test_particle_array.py:1048-1066`. +- [OBSERVED] `remove_property()` also removes the property from output arrays and delegates to the GPU helper if present at `pysph/base/particle_array.pyx:1412-1421`; tests cover this output-array side effect at `pysph/base/tests/test_particle_array.py:980-991`. +- [OBSERVED] `empty_clone()` preserves constants, selected properties, name, and output arrays at `pysph/base/particle_array.pyx:1174-1215`; tests cover clone behavior at `pysph/base/tests/test_particle_array.py:847-884`. +- [INFERRED] The first Warp migration should keep the Python/Cython public API behavior stable before attempting broader solver-facing changes. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/07-variants.md b/.ai/implementations/blast-from-the-past/spec/particle-array/07-variants.md new file mode 100644 index 000000000..5d403e806 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/07-variants.md @@ -0,0 +1,27 @@ +# 07 Variants + +## Backend Variants + +- [OBSERVED] ParticleArray resolves the configured backend with `get_backend(backend)` and treats `cython` as the host-only backend at `pysph/base/particle_array.pyx:109-116` and `pysph/base/particle_array.pyx:149-157`. +- [OBSERVED] Tests define CPU, OpenCL, and CUDA ParticleArray variants through `ParticleArrayTestCPU`, `ParticleArrayTestOpenCL`, and `ParticleArrayTestCUDA` at `pysph/base/tests/test_particle_array.py:1033-1139`. +- [OBSERVED] DeviceHelper tests parametrize `cython`, `opencl`, and `cuda` where imports are available at `pysph/base/tests/test_device_helper.py:18-26`. +- [UNKNOWN] A Warp variant name, configuration path, and fallback behavior are not yet defined. + +## Storage Variants + +- [OBSERVED] Properties may be scalar-stride or fixed-stride, with strided properties stored as flat arrays; tests cover stride 2 and stride 3 properties at `pysph/base/tests/test_particle_array.py:123-149`, `pysph/base/tests/test_particle_array.py:180-195`, and `pysph/base/tests/test_particle_array.py:573-587`. +- [OBSERVED] Properties may be double, float, int, long, or unsigned int according to carray creation paths at `pysph/base/particle_array.pyx:1020-1055`. +- [OBSERVED] Constants are stored outside `properties` and may be scalar or vector arrays at `pysph/base/tests/test_particle_array.py:758-845`. +- [INFERRED] Warp storage must model at least three cases: scalar per-particle arrays, fixed-width strided per-particle arrays, and fixed-size constants. + +## Output/Readback Variants + +- [OBSERVED] `get_property_arrays(all=False, only_real=True)` can return output arrays only or all arrays, and can slice only real particles or all particles at `pysph/base/particle_array.pyx:344-386`. +- [OBSERVED] Output code passes `detailed_output` and `only_real` into `get_property_arrays()` at `pysph/solver/output.py:53-78`. +- [INFERRED] A Warp migration should preserve explicit all-vs-output and real-vs-all readback modes because they affect I/O size and correctness. + +## Ordering Variants + +- [OBSERVED] CPU tests generally expect deterministic post-mutation ordering for aligned arrays at `pysph/base/tests/test_particle_array.py:589-676`. +- [OBSERVED] Existing GPU behavior is already allowed to differ from CPU ordering in one tagged-removal/strided-property case at `pysph/base/tests/test_particle_array.py:452-460`. +- [UNKNOWN] The future Warp backend's exact ordering guarantees need a decision before tests are written. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/08-interfaces.md b/.ai/implementations/blast-from-the-past/spec/particle-array/08-interfaces.md new file mode 100644 index 000000000..6e27745f2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/08-interfaces.md @@ -0,0 +1,28 @@ +# 08 Interfaces + +## Existing Interfaces To Preserve + +- [OBSERVED] Python construction accepts either raw arrays or dictionaries containing `data`, `type`, `default`, and `stride`-like metadata; tests cover both forms at `pysph/base/tests/test_particle_array.py:50-83`, `pysph/base/tests/test_particle_array.py:112-149`, and `pysph/base/tests/test_particle_array.py:150-178`. +- [OBSERVED] `get_particle_array()` is the common helper that adds default properties and output metadata at `pysph/base/utils.py:47-149`. +- [OBSERVED] Attribute access, `get()`, `set()`, `get_carray()`, `add_property()`, `remove_property()`, `add_constant()`, and mutation methods are public behaviors exercised by tests at `pysph/base/tests/test_particle_array.py:242-288`, `pysph/base/tests/test_particle_array.py:197-217`, `pysph/base/tests/test_particle_array.py:1068-1103`, `pysph/base/tests/test_particle_array.py:834-845`, `pysph/base/tests/test_particle_array.py:469-533`, `pysph/base/tests/test_particle_array.py:980-991`, and `pysph/base/tests/test_particle_array.py:758-832`. + +## Solver-Agnostic Interface Decomposition + +- [INFERRED] `ParticleSchema`: names, dtypes, defaults, strides, output-array membership, load-balance membership, and constant metadata. +- [INFERRED] `HostParticleStore`: host `BaseArray` ownership, NumPy/carray views, serialization, pickle, and Cython ABI compatibility. +- [INFERRED] `DeviceParticleMirror`: backend-specific arrays plus push/pull/update hooks; existing `DeviceHelper` is the observed compyle implementation at `pysph/base/device_helper.py:47-70`. +- [INFERRED] `ParticleSelectionOps`: remove, extract, append, align, copy, and strided gather/scatter kernels. +- [INFERRED] `ParticleLifecycleOps`: construction, extend, resize, defaults fill, constants preservation, and metadata reconstruction. + +## Proposed Warp Boundary + +- [INFERRED] First Warp integration should likely implement a `DeviceParticleMirror` equivalent rather than replacing `ParticleArray` host storage, because host carray access is part of the declared and tested API. +- [INFERRED] Warp kernels should be introduced behind methods equivalent to `DeviceHelper.align_particles()`, `remove_particles()`, `remove_tagged_particles()`, `add_particles()`, `append_parray()`, `extend()`, `extract_particles()`, `resize()`, `push()`, and `pull()`. +- [UNKNOWN] Whether this boundary lives in a new helper class, an extension of `DeviceHelper`, or a separate `warp_device_helper.py` requires an ADR before code migration. + +## Required Error Behavior + +- [OBSERVED] Missing properties accessed as attributes raise `AttributeError` at `pysph/base/particle_array.pyx:159-177`, and tests assert this at `pysph/base/tests/test_particle_array.py:242-265`. +- [OBSERVED] `remove_particles()` raises `ValueError` when asked to remove more indices than particles at `pysph/base/particle_array.pyx:439-505`, with tests at `pysph/base/tests/test_particle_array.py:290-337`. +- [OBSERVED] `set()` raises for unknown names and delegates incompatible length handling to carray set-data behavior; tests cover longer data raising `ValueError` at `pysph/base/particle_array.pyx:772-810` and `pysph/base/tests/test_particle_array.py:1068-1103`. +- [INFERRED] Warp code must preserve Python-visible exceptions at the ParticleArray API boundary even if device kernels use different internal failure modes. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/09-verification.md b/.ai/implementations/blast-from-the-past/spec/particle-array/09-verification.md new file mode 100644 index 000000000..52bfd4489 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/09-verification.md @@ -0,0 +1,32 @@ +# 09 Verification + +## Baseline Test Set + +- [OBSERVED] `pysph/base/tests/test_particle_array.py` is the main behavior suite for ParticleArray construction, property access, mutation, constants, serialization helpers, CPU behavior, and OpenCL/CUDA variants at `pysph/base/tests/test_particle_array.py:28-1139`. +- [OBSERVED] `pysph/base/tests/test_device_helper.py` is the main behavior suite for backend mirror synchronization and device-side mutation operations at `pysph/base/tests/test_device_helper.py:29-455`. +- [OBSERVED] NNPS tests create ParticleArray instances through `get_particle_array()` and depend on position/smoothing-length semantics at `pysph/base/tests/test_nnps.py:12-83`. +- [INFERRED] The first Warp migration should run ParticleArray and DeviceHelper-equivalent tests before broader solver/NNPS tests. + +## Focused Acceptance Matrix + +- [INFERRED] Construction: raw arrays, dict descriptors, scalar broadcast, flattened 2D data, default properties, and constants. +- [INFERRED] Mutations: add/remove particles, remove tagged particles, append arrays, extract particles, resize, extend, add/remove properties, and copy properties. +- [INFERRED] Stride: all mutation and alignment operations must be tested with stride greater than 1. +- [INFERRED] Sync: selective/full push and pull must be tested with both float and integer properties. +- [INFERRED] Serialization: `get_particles_info()`, `create_dummy_particles()`, pickle, NumPy output, and HDF output metadata should remain compatible. +- [INFERRED] Boundary-adjacent behavior: Ghost-tag removal and periodic/mirror append/extract paths should be covered by NNPS/domain tests after the storage layer passes. + +## Spec-Derived Warp Smoke Tests + +- [INFERRED] Create a Warp-backed ParticleArray with `x`, `y`, `z`, `h`, `m`, `rho`, `tag`, `pid`, `gid`, and one stride-3 property. +- [INFERRED] Mutate host `x` and `tag`, push selectively, align on device, pull `x/tag`, and assert Local particles are first with strided values reordered consistently. +- [INFERRED] Add particles with a missing property and verify defaults are filled on device and host after pull. +- [INFERRED] Remove Ghost-tagged particles on device and verify count, tag partition, and stride data. +- [INFERRED] Dump and reload metadata without requiring Warp to be present for the loaded host arrays. + +## Validation Commands + +- [OBSERVED] The memory system validator is available at `.ai/implementations/blast-from-the-past/scripts/validate-memory.py`, whose script header and root discovery are at `.ai/implementations/blast-from-the-past/scripts/validate-memory.py:1-12`. +- [INFERRED] Phase 1 spec validation should run `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` and `git diff --check -- .ai AGENTS.md`. +- [INFERRED] Phase 2 runtime probing should use the user-provided environment activation command before importing Warp: `source $HOME/prediqt/activate && python -c "import warp; print(warp.__version__)"`. +- [UNKNOWN] The exact PySPH test command for Warp-specific tests is not defined until the Warp test module path exists. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/10-porting.md b/.ai/implementations/blast-from-the-past/spec/particle-array/10-porting.md new file mode 100644 index 000000000..9eab6fd6a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/10-porting.md @@ -0,0 +1,32 @@ +# 10 Porting + +## Porting Premises + +- [OBSERVED] The implementation is scoped to `blast-from-the-past`, and its boundary includes `pysph/**/*.pxd` and `pysph/**/*.pyx` at `.ai/implementations/blast-from-the-past/implementation.md:2-6` and `.ai/implementations/blast-from-the-past/implementation.md:27-33`. +- [OBSERVED] `ParticleArray` is declared in `.pxd` and implemented in `.pyx`, with GPU mirroring delegated through Python `DeviceHelper` at `pysph/base/particle_array.pxd:37-138`, `pysph/base/particle_array.pyx:109-157`, and `pysph/base/device_helper.py:47-70`. +- [INFERRED] The lowest-risk Warp migration is additive: introduce Warp as a mirror/backend path while preserving `ParticleArray` host storage and Cython declarations. + +## Phase 2 Plan Seed + +1. [UNKNOWN] Decide backend naming and ownership in an ADR: `warp` backend string versus CUDA backend replacement, and mirror versus authoritative storage. +2. [INFERRED] Add a Warp helper behind the existing DeviceHelper-like interface with no changes to solver code. +3. [INFERRED] Implement push/pull and device-array creation first, because they validate dtype/stride/constant layout without mutation complexity. +4. [INFERRED] Add device alignment next, because Local-first partition controls `num_real_particles`, real-only slices, and NNPS expectations. +5. [INFERRED] Add remove/extract/append/extend kernels after alignment, because they require gather/scatter and resize/fill semantics. +6. [INFERRED] Only after ParticleArray behavior passes should NNPS or solver-loop Warp integration begin. + +## Data Mapping Checklist + +- [OBSERVED] Property dtype mapping must cover double, float, int, long, and unsigned int as created by `_create_carray()` at `pysph/base/particle_array.pyx:1020-1055`. +- [OBSERVED] Stride mapping must preserve logical particle count as `length / stride` at `pysph/base/particle_array.pyx:423-437`. +- [OBSERVED] Constants must not be resized during particle mutation at `pysph/base/tests/test_particle_array.py:805-817`. +- [OBSERVED] `num_real_particles` must be updated after tag alignment at `pysph/base/particle_array.pyx:1092-1173`. +- [OBSERVED] Output and dummy-particle metadata must preserve name, property type, default, stride, constants, output arrays, and load-balance properties at `pysph/base/utils.py:466-512`. + +## Risks To Retire Before Code + +- [UNKNOWN] Warp availability and version on `prediqt-02` are not yet confirmed. +- [UNKNOWN] Warp support for the required dtype matrix and efficient dynamic resizing strategy is not yet confirmed. +- [UNKNOWN] The performance target for "blazing fast particle dynamics" is not yet quantified. +- [INFERRED] Replacing host `BaseArray` storage too early risks breaking Cython callers and output/load paths. +- [INFERRED] Treating strided properties as separate vector types may break flat-array user expectations unless a compatibility view is maintained. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/glossary.md b/.ai/implementations/blast-from-the-past/spec/particle-array/glossary.md new file mode 100644 index 000000000..6449a6119 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/glossary.md @@ -0,0 +1,14 @@ +# Glossary - Particle Array + +- [OBSERVED] `ParticleArray`: Homogeneous collection of particles and named properties; documented as the main data structure in `docs/source/using_pysph.rst:59-76`. +- [OBSERVED] `BaseArray`: Cython array abstraction used for ParticleArray properties; docs name `cyarray.carray.BaseArray` as the storage family at `docs/source/using_pysph.rst:59-76`. +- [OBSERVED] Property: A named one-dimensional array associated with particles; arbitrary properties are supported at `docs/source/using_pysph.rst:59-76`. +- [OBSERVED] Stride: Fixed number of flat array entries per logical particle for a property; documented at `docs/source/using_pysph.rst:94-104`. +- [OBSERVED] Constant: Fixed-size array associated with a ParticleArray, not resized with particles; documented at `docs/source/using_pysph.rst:148-175`. +- [OBSERVED] `Local`: Particle tag value 0 in the `ParticleTag` enum at `pysph/base/particle_array.pxd:24-28`. +- [OBSERVED] `Remote`: Particle tag value 1 in the `ParticleTag` enum at `pysph/base/particle_array.pxd:24-28`. +- [OBSERVED] `Ghost`: Particle tag value 2 in the `ParticleTag` enum at `pysph/base/particle_array.pxd:24-28`. +- [OBSERVED] `num_real_particles`: Count of Local-tagged particles after alignment; updated in `align_particles()` at `pysph/base/particle_array.pyx:1092-1173`. +- [OBSERVED] `DeviceHelper`: Existing device mirror for ParticleArray properties/constants, described by its class docstring and constructor at `pysph/base/device_helper.py:47-70`. +- [OBSERVED] `lb_props`: Load-balance property list recorded by `get_particles_info()` and restored in dummy particles at `pysph/base/utils.py:466-512`. +- [INFERRED] Warp mirror: Proposed future object that would provide DeviceHelper-like semantics using NVIDIA Warp arrays/kernels. diff --git a/.ai/implementations/blast-from-the-past/spec/particle-array/open-questions.md b/.ai/implementations/blast-from-the-past/spec/particle-array/open-questions.md new file mode 100644 index 000000000..f4fbe901a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/spec/particle-array/open-questions.md @@ -0,0 +1,15 @@ +# Open Questions - Particle Array Spec + +This file is intentionally first: the attached spec prompt requires unknowns to be surfaced before turning the current implementation into a porting contract. + +## Questions + +- [UNKNOWN] OQ-PA-001: Should a Warp-backed particle array preserve PySPH's current `backend` names (`cython`, `opencl`, `cuda`) or add a new `warp` backend string? The current constructor resolves `backend` through `get_backend(backend)` and stores it on `self.backend` at `pysph/base/particle_array.pyx:109-116`. +- [UNKNOWN] OQ-PA-002: Should Warp own the authoritative storage for migrated arrays, or should Warp remain a mirror of host `cyarray` data like `DeviceHelper`? The current design creates host `BaseArray` objects in `ParticleArray` and device mirrors in `DeviceHelper` when `backend != 'cython'` at `pysph/base/particle_array.pyx:109-157` and `pysph/base/device_helper.py:47-70`. +- [UNKNOWN] OQ-PA-003: What is the required dtype policy for Warp: always double when PySPH `use_double` is true, or per-property dtype parity with `BaseArray`? Existing `DeviceHelper` converts float/double arrays according to `get_config().use_double` while preserving integer dtype at `pysph/base/device_helper.py:47-77`. +- [UNKNOWN] OQ-PA-004: Which ParticleArray operations must be fast on GPU in the first implementation: allocation, push/pull, mutation, alignment, append/extract/remove, or NNPS-facing access? The current tests cover all of these across CPU and GPU helpers at `pysph/base/tests/test_particle_array.py:290-1018` and `pysph/base/tests/test_device_helper.py:51-403`. +- [UNKNOWN] OQ-PA-005: Should the first Warp port preserve exact post-operation ordering for ghost/tagged/strided arrays, or only preserve semantic equivalence? Existing GPU ordering already differs from CPU for one strided tagged-removal assertion at `pysph/base/tests/test_particle_array.py:452-460`. +- [UNKNOWN] OQ-PA-006: What is the minimal accepted integration boundary for `.pxd/.pyx` callers? `ParticleArray` is a Cython extension type with declared cpdef/cdef methods at `pysph/base/particle_array.pxd:37-138`, so replacing it wholesale may affect ABI expectations. +- [UNKNOWN] OQ-PA-007: Should constants become Warp arrays, scalar Python/NumPy state, or both? Current constants are stored as `BaseArray` objects, are not resized with particles, and are mirrored to GPU helpers at `pysph/base/particle_array.pyx:823-850` and `pysph/base/tests/test_particle_array.py:758-845`. +- [UNKNOWN] OQ-PA-008: Should Warp expose direct arrays to NNPS and equations, or should it support existing host pull/readback points first? NNPS consumes ParticleArray data through wrappers and `get_number_of_particles()` at `pysph/base/nnps_base.pyx:1459-1510`. +- [UNKNOWN] OQ-PA-009: What measurable performance target defines "blazing fast particle dynamics" for this first ParticleArray migration? The implementation memory currently records the success criterion qualitatively, not as a benchmark threshold. diff --git a/.ai/implementations/blast-from-the-past/templates/aspect-context-template.md b/.ai/implementations/blast-from-the-past/templates/aspect-context-template.md new file mode 100644 index 000000000..812527f59 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/aspect-context-template.md @@ -0,0 +1,18 @@ +--- +aspect: {{name}} +implementation: blast-from-the-past +owner: @kunalpuri-prediqt +created: {{ISO_TIMESTAMP}} +last_reviewed: {{ISO_TIMESTAMP}} +status: active +--- + +# Aspect: {{name}} + +## What this aspect covers +## Current understanding +Initial scaffolding - to be filled in the first working session on this aspect. +## Key sub-topics +## References for this aspect +## Decisions affecting this aspect +## Cross-aspect dependencies diff --git a/.ai/implementations/blast-from-the-past/templates/daily-closeout-template.md b/.ai/implementations/blast-from-the-past/templates/daily-closeout-template.md new file mode 100644 index 000000000..31d155fb2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/daily-closeout-template.md @@ -0,0 +1,21 @@ +--- +type: daily-closeout +date: {{YYYY-MM-DD}} +user: @kunalpuri-prediqt +agent: {{AGENT_ID}} +duration_minutes: {{N}} +aspects_touched: [] +--- + +# Daily Closeout - {{YYYY-MM-DD}} + +## Summary +## Work completed +## Decisions made (ADRs) +## Plans approved (full + lightweight) +## Files changed +## Tests / experiments run +## Problems encountered +## Open questions raised +## Next actions +## Notes for next AI session diff --git a/.ai/implementations/blast-from-the-past/templates/decision-template.md b/.ai/implementations/blast-from-the-past/templates/decision-template.md new file mode 100644 index 000000000..efd74e173 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/decision-template.md @@ -0,0 +1,21 @@ +--- +type: decision +id: ADR-{{NNNN}} +date: {{YYYY-MM-DD}} +author: @kunalpuri-prediqt +scope: global +status: Proposed +supersedes: [] +relates_to: [] +depends_on: [] +conflicts_with: [] +--- + +# ADR-{{NNNN}}: {{Title}} + +## Context +## Decision +## Rationale +## Alternatives considered +## Consequences +## Follow-ups diff --git a/.ai/implementations/blast-from-the-past/templates/experiment-template.md b/.ai/implementations/blast-from-the-past/templates/experiment-template.md new file mode 100644 index 000000000..11b6c4d14 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/experiment-template.md @@ -0,0 +1,19 @@ +--- +type: experiment +id: {{YYYY-MM-DD}}_{{slug}} +created: {{ISO_TIMESTAMP}} +author: @kunalpuri-prediqt +aspect: {{aspect-name}} +status: planned +last_checked: {{ISO_TIMESTAMP}} +--- + +# Experiment: {{Title}} + +## Purpose +## Setup +## Hypothesis +## Execution +## Results +## Conclusion +## Follow-ups diff --git a/.ai/implementations/blast-from-the-past/templates/plan-template.md b/.ai/implementations/blast-from-the-past/templates/plan-template.md new file mode 100644 index 000000000..7f9f64471 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/plan-template.md @@ -0,0 +1,29 @@ +--- +type: plan +id: {{YYYY-MM-DD}}_{{slug}} +author: @kunalpuri-prediqt +agent: {{AGENT_ID}} +created: {{ISO_TIMESTAMP}} +status: draft +aspects: [] +host_files: [] +within_boundary: true +--- + +# Plan: {{Title}} + +## Goal +## Context +## Approach +## Files expected to change +## Tests / validation +## Risks +## Out of scope +## Estimated effort + +## Approval + +- [ ] Plan posted in chat +- Approved by: @____ at {{ISO_TIMESTAMP}} +- Approval, verbatim quote: + > {{exact user message}} diff --git a/.ai/implementations/blast-from-the-past/templates/reference-note-template.md b/.ai/implementations/blast-from-the-past/templates/reference-note-template.md new file mode 100644 index 000000000..39b622d3d --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/reference-note-template.md @@ -0,0 +1,19 @@ +--- +type: reference-note +id: {{slug}} +created: {{ISO_TIMESTAMP}} +author: @kunalpuri-prediqt +kind: primary +status: placeholder +aspects: [] +--- + +# Reference: {{Title}} + +## Citation +## TL;DR +## Key claims +## Bearing on blast-from-the-past +## Equations / algorithms / APIs to use +## Questions raised +## Verdict diff --git a/.ai/implementations/blast-from-the-past/templates/review-template.md b/.ai/implementations/blast-from-the-past/templates/review-template.md new file mode 100644 index 000000000..c02bee7b2 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/review-template.md @@ -0,0 +1,56 @@ +--- +type: review +date: {{YYYY-MM-DD}} +user: @kunalpuri-prediqt +agent: {{AGENT_ID}} +plan: session-log LP-N +adrs: [] +aspects_touched: [] +host_files: [] +review_mode: promotion +status: pending +--- + +# Review - {{Title}} + +## Diff summary +## Aspects touched and host files modified +## Behavioral / numerical changes +## Tests / validation run + +```text +$ + +``` + +## validate-memory.py + +```text + +``` + +## Boundary amendment + +- implementation.md boundary section updated: n-a +- Amendments log entry: n-a + +## Visual aid + +Comparison table, Mermaid diagram, generated chart, convergence plot, or one-line waiver. + +## Risks +## Unresolved questions + +## Sign-off + +- Review mode: promotion | prototype-owner +- Promotion reviewer: @prabhu +- Promotion verdict, verbatim quote: + > {{exact LGTM message}} - {{timestamp}} +- Prototype owner: @kunalpuri-prediqt +- Prototype authorization, verbatim quote: + > {{exact owner authorization}} - {{timestamp}} + +Delete the unused sign-off route. `prototype-owner` is valid only under the +narrow exception in Rule 4; it does not authorize upstream publication, +production promotion, or out-of-boundary changes. diff --git a/.ai/implementations/blast-from-the-past/templates/session-log-template.md b/.ai/implementations/blast-from-the-past/templates/session-log-template.md new file mode 100644 index 000000000..d6117aafd --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/session-log-template.md @@ -0,0 +1,20 @@ +--- +type: session-log +date: {{YYYY-MM-DD}} +start: {{HH:MM}} +end: {{HH:MM}} +user: @kunalpuri-prediqt +agent: {{AGENT_ID}} +aspects_touched: [] +memory_consulted: [] +--- + +# Session - {{YYYY-MM-DD}} {{HH:MM}} + +## Goal of this session +## What happened +## Lightweight plans +## Decisions reached +## Code touched +## Experiments started / advanced / completed +## Handoff for next session diff --git a/.ai/implementations/blast-from-the-past/templates/weekly-closeout-template.md b/.ai/implementations/blast-from-the-past/templates/weekly-closeout-template.md new file mode 100644 index 000000000..aa6d9b210 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/templates/weekly-closeout-template.md @@ -0,0 +1,29 @@ +--- +type: weekly-closeout +week: {{YYYY-Www}} +range: {{YYYY-MM-DD}} to {{YYYY-MM-DD}} +user: @kunalpuri-prediqt +--- + +# Weekly Closeout - {{YYYY-Www}} + +## Executive summary +## Main accomplishments +## Important decisions +## Aspects advanced this week +## Experiments completed +## Open questions resolved / still open +## Risks +## Carry-over tasks +## Recommended focus next week + +## Memory health + +1. **Re-derived:** +2. **Stale or wrong:** +3. **Unread:** + +## Curation performed + +- compact.py run: +- Monthly aspect refresh: diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-15.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-15.md new file mode 100644 index 000000000..59978d83b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-15.md @@ -0,0 +1,148 @@ +--- +type: daily-closeout +date: 2026-06-15 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 60 +aspects_touched: [warp-backend, gpu-nnps, particle-memory, cython-boundary, validation-benchmarks, host-integration] +--- + +# Daily Closeout - 2026-06-15 + +## Summary + +Scaffolded the implementation-scoped `.ai/` memory system for +`blast-from-the-past`, ported the first GPU data path through Warp +ParticleArray mirrors, Warp uniform-grid NNPS, and several SPH kernels, and +ended with a committed one-step WCSPH Euler chain that stays on the GPU. + +## Work completed + +- Created implementation-scoped memory contract, spec, host notes, conventions, glossary, aspects, references, experiments, prompts, skills, templates, and scripts. +- Seeded ADR-0001 for adopting the memory system. +- Created placeholder daily, weekly, session, experiment, and reference artifacts. +- Installed a permitted root `AGENTS.md` pointer. +- Migrated implementation memory under `.ai/implementations/blast-from-the-past/` and left top-level `.ai/` as a router. +- Drafted ParticleArray solver-agnostic implementation spec under `.ai/implementations/blast-from-the-past/spec/particle-array/`. +- Identified the first Warp decision point: DeviceHelper-like mirror versus backend-owned Warp storage. +- Confirmed the active Python environment can import NVIDIA Warp `1.14.0`. +- Added proposed ADR-0002 for a Warp DeviceHelper-like ParticleArray mirror. +- Added proposed follow-up plan for a Warp ParticleArray device mirror prototype. +- Accepted ADR-0002 and started the Warp ParticleArray mirror prototype. +- Added `pysph/base/warp_device_helper.py`, `backend='warp'` ParticleArray resolution, and focused Warp helper tests. +- Continued the Warp prototype to cover ParticleArray remove, remove-tagged, add, append, extend, and extract paths. +- Expanded Warp tests across sync, dtype, alignment, readback, mutation, constants, clone, extraction, and error behavior. +- Added tutorial documentation for using `backend='warp'`. +- Built Zoltan `v3.901` under `/home/kunalp/prediqt/zoltan`, installed PyZoltan `1.1.1`, and installed PySPH editable into the PQT venv. +- Added persistent Compyle config at `/home/kunalp/.compyle/config.py` for PQT MPI/Zoltan rebuilds. +- Replaced the empty Warp benchmark placeholder with an active experiment packet covering correctness, add/remove/extract/append/align behavior, benchmark commands, success criteria, and smoke timing results. +- Committed the Warp ParticleArray prototype locally as `a02d557f Add Warp ParticleArray prototype`. +- Drafted the solver-agnostic NNPS spec under `spec/nnps/` and proposed the NNPS Warp prototype plan. +- Worked the top-level `CODEBASE_UNDERSTANDING.md` into implementation-scoped specs under `spec/codebase-understanding/` and added `spec/README.md`. +- Began NNPS implementation with Warp by adding `BruteForceWarpNNPS`, `UniformGridWarpNNPS`, focused correctness tests, cached flat-neighbor-list support, and an NNPS experiment packet. +- Added device-resident Warp NNPS consumption with `compute_neighbor_sum()`, focused correctness coverage, and a dedicated experiment packet. +- Added the first real Warp SPH equation kernel with CubicSpline + summation-density support and a 1M-to-10M CPU/Cython-vs-Warp benchmark. +- Added Warp `IsothermalEOS` and `ContinuityEquation` kernels with focused + correctness coverage and a capped 1M/2M/5M CPU/Cython-vs-Warp benchmark. +- Added Warp inviscid pressure-gradient acceleration with focused correctness + coverage and a capped 1M/2M/5M CPU/Cython-vs-Warp benchmark. +- Added a minimal Warp WCSPH Euler step that chains summation density, EOS, + inviscid pressure-gradient acceleration, and position/velocity update on the + device. + +## Decisions made (ADRs) + +- ADR-0001 - Adopt `.ai/` memory system for `blast-from-the-past`. +- Proposed: ADR-0002 - Warp DeviceHelper-like ParticleArray mirror. +- Accepted: ADR-0002 - Warp DeviceHelper-like ParticleArray mirror. + +## Plans approved (full + lightweight) + +- Discovery/aspect proposal accepted by user: "keep things as they are". +- Full migration plan approved by user: "APPROVED". +- Proposed follow-up plan: `2026-06-15_warp-particle-array-device-mirror-prototype`. + +## Files changed + +- `.ai/` +- `AGENTS.md` +- `.git/hooks/pre-commit` after hook installation + +## Tests / experiments run + +- `python .ai/implementations/blast-from-the-past/scripts/update-decision-graph.py` - pass; generated `decisions/index.json` and `decisions/graph.md`. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` - pass. +- `.git/hooks/pre-commit` - pass with valid memory. +- `.git/hooks/pre-commit` with deliberately invalid closeout - blocked as expected; invalid file removed. +- `python .ai/implementations/blast-from-the-past/scripts/compact.py --dry-run` - pass; nothing to archive. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` after nesting - pass. +- `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py` after ParticleArray spec - pass. +- `git diff --check -- .ai AGENTS.md` after ParticleArray spec - pass. +- `python -c "import warp; print(warp.__version__)"` in the active environment - pass; reported `1.14.0`. +- `python -m py_compile pysph/base/warp_device_helper.py pysph/base/tests/test_warp_device_helper.py` - pass. +- Isolated `WarpArray.aligned()` probes on `cuda:0` - pass for float64 stride-3 and int64 scalar data. +- `python -m pytest pysph/base/tests/test_warp_device_helper.py -q` - skipped because `pysph.base.particle_array` is not built/importable in the active environment. +- Installed declared test/build requirements into the active venv and rebuilt `pysph.base.particle_array` narrowly with `pyximport`. +- `python -m pytest -q pysph/base/tests/test_warp_device_helper.py` - pass; 6 passed. +- CPU ParticleArray sanity slice launched with `setuptools` distutils shim - pass; 4 passed. +- `python -m pytest -q pysph/base/tests/test_warp_device_helper.py` after mutation support - pass; 11 passed. +- Expanded CPU ParticleArray sanity slice - pass; 7 passed. +- `python -m pytest -q pysph/base/tests/test_warp_device_helper.py` after full test expansion - pass; 20 passed. +- `git diff --check -- .ai AGENTS.md pysph/base docs/source` - pass. +- `python -m pip install --no-cache-dir --no-build-isolation pyzoltan` with `ZOLTAN=/home/kunalp/prediqt/zoltan` - pass. +- `python -m pip install -e . --no-build-isolation` with `ZOLTAN=/home/kunalp/prediqt/zoltan` - pass. +- Plain import validation for `pysph`, `pyzoltan`, `WarpDeviceHelper`, and `ZoltanParallelManager` - pass. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_correctness.sh` - pass; 20 Warp tests and 7 CPU sanity tests. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_initial-warp-benchmark-placeholder/run_mutation_benchmark.sh --sizes 1000 --repeats 2` - pass; smoke result captured in the ParticleArray experiment doc. +- Static source review for NNPS spec: `pysph/base/nnps_base.pyx`, `pysph/base/gpu_nnps_base.pyx`, `pysph/base/gpu_nnps_base.pxd`, `pysph/base/linked_list_nnps.pyx`, and `pysph/solver/application.py`. +- Integrated codebase understanding headings and architecture findings into the implementation spec tree. +- `python -m pytest -q pysph/base/tests/test_warp_nnps.py` - pass; 12 passed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 128 --repeats 1` - pass; CPU, uncached Warp, cached Warp, and Warp grid average neighbor count all 21.781. `warp_grid` measured `0.041x` CPU speed on Intel(R) Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX 4060 Laptop GPU. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid` - pass; CPU and Warp grid average neighbor count both 25.568; `warp_grid` measured `4.269x` CPU speed on the host-facing benchmark. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-bruteforce-baseline/run_benchmark.sh --sizes 1000000 --repeats 1 --backends cpu warp_grid_device` - pass; CPU and Warp grid device average neighbor count both 25.568; `warp_grid_device` measured `88.288x` CPU speed for bulk GPU cache construction. +- `python -m pytest -q pysph/base/tests/test_warp_nnps.py` after direct packed-GPU-cache index parity coverage - pass; 17 passed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-nnps-device-consumption/run_benchmark.sh --sizes 1000000 --repeats 1` - pass; `warp_grid_reduce` measured `145.583x` CPU speed for a neighbor mass sum on Intel(R) Core(TM) Ultra 7 155H versus NVIDIA GeForce RTX 4060 Laptop GPU. Average neighbor sum matched to reported precision (`25.568`), with checksum delta `6` over roughly `25.6M` contributions. +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` - pass; 19 passed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh --sizes 1000000 --repeats 1` - pass; `warp_grid_density` measured `152.508x` CPU/Cython speed with matching checksum. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-summation-density/run_benchmark.sh --sizes 2000000 5000000 10000000 --repeats 1` - pass; `warp_grid_density` measured `227.555x`, `64.964x`, and `69.084x` CPU/Cython speed at 2M, 5M, and 10M particles with matching checksums. +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` after EOS and continuity coverage - pass; 22 passed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh --sizes 1000000 --repeats 1` - pass; `warp_grid_eos_cont` measured `161.063x` CPU/Cython speed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-eos-continuity/run_benchmark.sh --sizes 2000000 5000000 --repeats 1` - pass; `warp_grid_eos_cont` measured `136.886x` and `72.583x` CPU/Cython speed at 2M and 5M particles. No 10M EOS+continuity run was performed. +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` after pressure-gradient coverage - pass; 24 passed. +- `bash .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-pressure-gradient/run_benchmark.sh --sizes 1000000 2000000 5000000 --repeats 1` - pass; `warp_grid_pgrad` measured `148.884x`, `129.854x`, and `38.722x` CPU/Cython speed at 1M, 2M, and 5M particles. No 10M pgrad run was performed. +- `python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py` after the one-step WCSPH Euler chain - pass; 26 passed. + +## Problems encountered + +- Warp add/remove/extract timings are slower than CPU in the smoke benchmark because the first prototype still rebuilds structural mutations through host arrays. +- Warp uncached brute-force NNPS has low CPU-relative speedup in the smoke benchmark because it launches and reads back per destination particle; cached brute force improves on uncached but still remains O(N^2). +- Warp grid NNPS still materializes host-facing neighbor arrays for the current `UIntArray` contract when using `get_nearest_particles()`, but the new `compute_neighbor_sum()` path proves useful device-resident consumption. +- `KEY_REFERENCES` is currently "prabhu"; a concrete note should capture the guidance. +- Repeated Warp stepping cannot yet call the existing NNPS `update()` after + device-side position updates without care, because `update()` pushes host + ParticleArray coordinates back to the device. + +## Open questions raised + +- See aspect `open-questions.md` files. + +## Next actions + +- Run larger mutation/sync benchmark sizes and decide the first device-side structural mutation target. +- Review NNPS spec and choose first Warp NNPS variant: brute-force baseline or uniform-grid/cell-list. +- Generalize the one-off Warp SPH kernels into a reusable equation-loop + contract and add a device-aware repeated-step NNPS refresh. +- Capture Warp documentation/version and Prabhu guidance. +- Create a follow-up ADR/plan before writing Warp migration code. +- Review/commit the current Warp mirror prototype when ready. +- Decide whether docs should be promoted from tutorial to main user guide after review. +- Consider running a small parallel/Zoltan test slice now that PyZoltan is installed. + +## Notes for next AI session + +Boot through `.ai/AGENTS.md`, follow the router to +`.ai/implementations/blast-from-the-past/AGENTS.md`, inspect nested +`current.md`, and start with the repeated-step problem: refresh +`UniformGridWarpNNPS` after device-side position updates without letting +stale host `ParticleArray` coordinates overwrite the GPU state. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md new file mode 100644 index 000000000..dbda9e989 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md @@ -0,0 +1,294 @@ +--- +type: daily-closeout +date: 2026-06-16 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 210 +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +--- + +# Daily Closeout - 2026-06-16 + +## Summary + +Added the first device-authoritative repeated-step checkpoints for Warp WCSPH: +NNPS can refresh from device positions, KDK leapfrog kick/drift runs on Warp, +periodic position wrapping is covered by focused tests, artificial viscosity, +Tait EOS/per-particle `cs`, Gaussian, XSPH, adaptive dt, and a +continuity-density PEC-style parity path are implemented locally. The resolved +`nx=100` elliptical-drop Application comparison now matches PySPH CPU closely +and shows a `7.606547218180963x` wall-time speedup. The continuity-density +resolved comparison slice is committed as `169f3fd5`. + +## Work completed + +- Created and approved plan `2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh`. +- Added `push=False` refresh support to Warp NNPS update paths. +- Added Warp leapfrog kick, leapfrog drift, periodic position wrap, and + `wc_sph_leapfrog_step()`. +- Added `push=False` mode to Warp summation-density and continuity helpers. +- Added tests for device-coordinate NNPS refresh, kick/drift/wrap, and KDK + leapfrog correctness against CPU reference values. +- Updated the active WCSPH experiment and current pointer. +- Prepared review `2026-06-16_warp-repeated-step-leapfrog-checkpoint`, + recorded `@prabhu: LGTM`, and amended the implementation boundary to include + Python Warp prototype files and tests. +- Added approved plan `2026-06-16_warp-elliptical-drop-application-runner`. +- Added first application-style Warp elliptical-drop runner experiment with a + smoke wrapper and result metrics. +- Ramped the Warp elliptical-drop runner through finite cases up to `nx=24`, + 1808 particles, 10 steps. +- Prepared review `2026-06-16_warp-elliptical-drop-runner-smoke-ramp` and + recorded `@prabhu: LGTM`. +- Created and received approval for plan + `2026-06-16_warp-artificial-viscosity-momentum-term`. +- Added additive Monaghan-style artificial viscosity to the Warp WCSPH + acceleration path with constant `c0`. +- Added a focused CPU-reference test proving artificial viscosity adds onto + existing acceleration arrays. +- Exposed `--alpha` and `--beta` in the Warp elliptical-drop runner and ran + finite smoke/ramp checks with `alpha=0.1`, `beta=0.0`. +- Drafted review `2026-06-16_warp-artificial-viscosity-momentum-term` and + recorded `@prabhu: LGTM`. +- Committed artificial viscosity as `ae8637e2`. +- Created and received approval for plan + `2026-06-16_warp-tait-eos-and-sound-speed`. +- Added Warp Tait EOS outputting pressure and per-particle sound speed `cs`. +- Updated artificial viscosity to use pair-averaged `cs`, with constant `c0` + fallback for compatibility. +- Switched the Warp elliptical-drop smoke runner to Tait EOS by default and ran + finite smoke/ramp checks with `gamma=7.0`. +- Drafted review `2026-06-16_warp-tait-eos-and-sound-speed` and recorded + `@prabhu: LGTM`. +- Committed Tait EOS/per-particle `cs` as `fe8df93f`. +- Created and received approval for plan + `2026-06-16_warp-xsph-gaussian-adaptive-baseline`. +- Added Gaussian kernel selection to Warp SPH equation kernels. +- Added device-side XSPH correction and leapfrog drift with `u + ax`. +- Added device-side `dt_cfl`/`dt_force` factors and scalar adaptive timestep + reduction, with only the final scalar timestep copied to host per adaptive + step. +- Updated the Warp elliptical-drop runner to use Gaussian, XSPH, and adaptive + dt controls and diagnostics. +- Added CPU PySPH-primitive comparison script producing CPU/Warp `.npz` + outputs and side-by-side image `comparison-smoke.png`. +- Created and received approval for plan + `2026-06-16_warp-resolved-elliptical-drop-performance-comparison`. +- Added `resolved_elliptical_drop_comparison.py` to run PySPH's no-scheme + Application baseline and Warp at `nx=100`, `t=0.0008`, and `t=0.0038`. +- Ran the first resolved comparison and diagnosed it as non-apples-to-apples: + Warp used summation density while PySPH evolved density with + `ContinuityEquation`/`WCSPHStep`, causing density excursions and 4807 Warp + steps through `t=0.0038`. +- Created and received approval for plan + `2026-06-16_warp-continuity-density-leapfrog-parity`. +- Added a Warp continuity-density WCSPH PEC-style step path that saves + reference state on device, computes `arho` with `ContinuityEquation`, stages + `rho` from `rho0`, recomputes equations at the predicted state, and keeps + summation-density KDK as the default compatibility path. +- Updated the Warp elliptical-drop runner and resolved comparison script with + `density_mode`. +- Ran the `nx=100` continuity-density comparison through both requested times: + PySPH CPU Application took 228.25765374601178 s / 1393 steps, Warp took + 30.008050591000938 s / 1804 steps, with near-zero shape/density/energy + deltas and `7.606547218180963x` wall-time speedup. + +## Decisions made (ADRs) + +- None. + +## Plans approved (full + lightweight) + +- `plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md` + approved by @kunalpuri-prediqt with verbatim quote: "APPROVED". +- `plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md` + approved by @kunalpuri-prediqt with verbatim quote: "APPROVED". +- `plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md` + approved by @kunalpuri-prediqt with verbatim quote: "APPROVED". +- `plans/2026-06-16_warp-continuity-density-leapfrog-parity.md` + approved by @kunalpuri-prediqt with verbatim quote: "APPROVED". + +## Files changed + +- `pysph/base/warp_nnps.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_nnps.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md` +- `.ai/implementations/blast-from-the-past/implementation.md` +- `.ai/implementations/blast-from-the-past/current.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md` +- `.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-repeated-step-leapfrog-checkpoint.md` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-elliptical-drop-application-runner.md` +- `.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-elliptical-drop-runner-smoke-ramp.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md` +- `.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-artificial-viscosity-momentum-term.md` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md` +- `.ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-tait-eos-and-sound-speed.md` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md` +- `.ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md` +- `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1151.md` +- `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1336.md` +- `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1357.md` +- `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2359.md` + +## Tests / experiments run + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +29 passed, 2 warnings in 5.82s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +particles: 204 +time: 2e-05 +rho_min: 0.5834630727767944 +rho_max: 0.999951183795929 +kinetic_energy: 8078.22338525834 +``` + +```text +Warp elliptical-drop ramp: +nx=12, particles=455, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=20, time=0.0001, all_finite=true +nx=24, particles=1808, steps=10, time=5e-05, all_finite=true +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +30 passed, 2 warnings in 4.39s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +alpha: 0.1 +beta: 0.0 +particles: 204 +time: 2e-05 +kinetic_energy: 8078.167363381624 +``` + +```text +Warp elliptical-drop artificial-viscosity ramp: +nx=16, particles=805, steps=5, time=5e-05, alpha=0.1, beta=0.0, all_finite=true +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +32 passed, 2 warnings in 4.14s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +eos: tait +gamma: 7.0 +alpha: 0.1 +beta: 0.0 +particles: 204 +time: 2e-05 +cs_min: 3.9725253582000732 +cs_max: 19.997066497802734 +``` + +```text +Warp elliptical-drop Tait ramp: +nx=16, particles=805, steps=5, time=5e-05, eos=tait, gamma=7.0, alpha=0.1, beta=0.0, all_finite=true +``` + +```text +python -m pytest pysph/base/tests/test_warp_sph.py -q +18 passed, 2 warnings in 3.50s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +kernel: gaussian +radius_scale: 3.0 +xsph_eps: 0.5 +adaptive_dt: true +particles: 204 +time: 1.9999999494757503e-05 +rho_min: 0.534595251083374 +rho_max: 0.9998562335968018 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --xsph-eps 0.5 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --dt-max 1.0e-5 --prefix comparison-smoke +CPU all_finite: true, particles: 204, rho_min: 0.534595094929311 +Warp all_finite: true, particles: 204, rho_min: 0.534595251083374 +image: .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +38 passed, 2 warnings in 3.67s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --density-mode continuity +all_finite: true +density_mode: continuity +rho_min: 0.9995885491371155 +rho_max: 1.0004163980484009 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-continuity --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 228.25765374601178 s, 1393 steps +Warp continuity-density: 30.008050591000938 s, 1804 steps +speedup_wall_time: 7.606547218180963 +t=0.0038 major-axis delta: 1.5947661098358878e-06 +t=0.0038 rho_min delta: -4.816405699936688e-06 +``` + +## Problems encountered + +- Periodic support in this checkpoint is position wrapping only. True periodic + neighbor interactions still need minimum-image distances and periodic cell + lookup. +- The plan touched Python Warp prototype files outside the original + implementation boundary; the boundary was amended during review. +- Elliptical drop still is not production-ready; the remaining gap is longer + full PySPH `Application/Solver` baseline comparison at the canonical + `t=0.0008` and `t=0.0038` output times. +- The first resolved Application comparison was not apples to apples because + Warp used summation-density refreshes while PySPH used continuity-density + evolution. This is fixed by the continuity-density PEC-style Warp path; the + summation resolved output remains diagnostic only. + +## Open questions raised + +- What exact periodic-neighbor contract should the first Warp NNPS periodic + implementation target? +- What ramp schedule and acceptance criteria should move the Warp + elliptical-drop runner from smoke to benchmark? + +## Next actions + +- Add minimum-image distance and periodic cell lookup to Warp NNPS/equation + kernels. +- Prepare review/commit for the resolved Application comparison plus + continuity-density parity slice. +- Decide the next production gap after continuity parity: timestep-policy + matching, periodic neighbor interactions, or deeper Application integration. + +## Notes for next AI session + +Start from `current.md`. The density-formulation blocker for elliptical-drop +comparison is committed as `169f3fd5`. The remaining repeated-step blockers are +true periodic neighbor interactions and tighter timestep/integrator policy +parity. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md new file mode 100644 index 000000000..e65b72e27 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md @@ -0,0 +1,160 @@ +--- +type: daily-closeout +date: 2026-06-17 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 90 +aspects_touched: [warp-backend, particle-memory, validation-benchmarks, host-integration, gpu-nnps] +--- + +# Daily Closeout - 2026-06-17 + +## Summary + +Matched the Warp resolved elliptical-drop runner's adaptive timestep policy to +PySPH's `n_damp` growth and output-time handling. The `nx=100` Application +comparison now has exact step-count parity: PySPH CPU and Warp both take 1393 +steps through `t=0.0038`, with Warp at `9.901903870258701x` wall-time speedup +and near floating-point-scale shape/density/energy deltas. + +## Work completed + +- Created and received approval for plan + `2026-06-17_warp-adaptive-timestep-policy-parity`. +- Added scalar adaptive timestep controls to `wc_sph_leapfrog_step()`: + `adaptive_dt_scale` and `step_dt_max`. +- Added focused test coverage for timestep scaling and per-step capping. +- Added `--warp-timestep-policy` and `--warp-dt-max` to the resolved + comparison runner. +- Ran small and full resolved comparisons and kept summary/PNG evidence. +- Updated experiment and aspect memory to mark the old initial-`dt` capped run + as diagnostic. +- Confirmed the current Warp resolved runner executes device arrays in fp32 on + this machine because `compyle.config.get_config().use_double` is `False`. +- Ran an explicit Warp-only fp32 `nx=100` resolved rerun: 26.5732471299998 s / + 1393 steps, all finite. +- Ran a bounded million-particle fixed-step CPU/GPU comparison: + `nx=565`, 1,002,885 particles, 10 steps. PySPH CPU Application took 57.48 s, + Warp GPU took 7.17 s, for `8.01673640167364x` wall-time speedup, with finite + outputs and tiny final metric deltas. +- Ran a one-step adaptive million-particle GPU probe: 4.87 s, all finite. The + full `nx=565`, `tf=0.0076` GPU-only run was not launched after the user said + "no multi hour run". +- Added per-stage neighbor-cache reuse to the Warp continuity-density PEC path, + reducing the million-particle fixed-step path from eight equation cache + builds per step to two. The same 1,002,885-particle, 10-step Warp run improved + from 7.17 s to 4.51 s, raising speedup versus the CPU baseline from + `8.01673640167364x` to `12.7450110864745x`. + +## Plans approved + +- `plans/2026-06-17_warp-adaptive-timestep-policy-parity.md` approved by + @kunalpuri-prediqt with verbatim quote: "approved". + +## Files changed + +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-summary.json` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0008000.png` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved/resolved-nx100-timestep-policy-t0p0038000.png` +- `.ai/implementations/blast-from-the-past/current.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md` +- `.ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md` +- `.ai/implementations/blast-from-the-past/aspects/warp-backend/context.md` +- `.ai/implementations/blast-from-the-past/aspects/particle-memory/context.md` +- `.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0824.md` + +## Tests / experiments run + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +39 passed, 2 warnings in 4.83s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-timestep-policy --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 233.97314716299297 s, 1393 steps +Warp continuity-density: 23.629107111992198 s, 1393 steps +speedup_wall_time: 9.901903870258701 +``` + +```text +million-particle fixed-step comparison +nx=565, particles=1002885, steps=10 +PySPH CPU Application: 57.48 s +Warp GPU: 7.17 s +speedup_wall_time: 8.01673640167364 +``` + +```text +million-particle fixed-step comparison after cache reuse +nx=565, particles=1002885, steps=10 +PySPH CPU Application: 57.48 s +Warp GPU: 4.51 s +speedup_wall_time: 12.7450110864745 +cache_builds_per_step: 2 +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +41 passed, 2 warnings in 1.75s +``` + +## Problems encountered + +- The prior continuity-density result was already numerically close, but it + was still not an apples-to-apples timestep comparison because Warp capped + adaptive `dt` to the initial timestep. The new policy fixes that. +- The final Warp `dt_min` is tiny because of an output-time landing step; it is + not a persistent stability constraint. +- The million-particle adaptive/full-duration path needs optimization before a + full `tf=0.0076` run; the bounded foreground probe was interrupted rather + than turned into a multi-hour job. +- Cache reuse improved the headline run, but it is still far from `100x`; + the next likely bottlenecks are separate equation-kernel traversals and + synchronization after each helper. + +## Evening session - equation fusion via dynamic code generation (ADR-0003) + +- Identified the post-cache-reuse bottleneck: four separate neighbor-loop + launches per PEC half-stage over the same ~45M-entry cache. +- On the user's "Generator now" choice, proved Warp 1.14 dynamic kernel + generation works on the RTX 4060, then wrote and accepted ADR-0003 and the + revised plan (approval verbatim "APPROVED"). +- Built `pysph/base/warp_codegen.py`: a composable `WarpEquation`/group code + generator that emits one fused JIT-compiled kernel per group, mirroring + PySPH's equation/group transpilation on the GPU. Ported the four + continuity-stage equations into blocks; rewired the continuity-density step to + one fused launch per half-stage; kept the per-equation kernels as oracle. +- Added `test_warp_codegen.py` and two `test_warp_sph.py` tests; full focused + suite `47 passed`. +- Million-particle fixed-step segmented profile: equation launches 8 -> 2 per + step, equation-kernel time ~0.064-0.088 s -> 0.011-0.014 s (~5-6x), step wall + ~25% lower; the neighbor-cache build is now the dominant per-step cost. The + 10-step headline wall is overhead/IO-bound (best 3.77 s warm = `15.25x` vs CPU + 57.48 s). Parity vs the prior separate-kernel Warp run is essentially exact + (positions/density/pressure identical to fp32 print precision; KE delta + `-6.4e-09`). +- Adaptive `nx=100` resolved guard with the fused path kept exactly `1393` steps + and stayed finite; that per-step-compute-bound run fell from `23.63 s` to + `10.83-14.33 s` (cross-session, same step count). +- Wrote the review artifact (sign-off pending @prabhu). + +## Plans approved (evening) + +- `plans/2026-06-17_warp-fuse-neighbor-loop-equations.md` (revised to the + generator design) approved by @kunalpuri-prediqt with verbatim quote: + "APPROVED". ADR-0003 accepted. + +## Next actions + +- Obtain @prabhu `LGTM` on the fused-equation review, then commit. +- Attack the neighbor-cache build (now ~45-50% of per-step wall). +- ADR-0003 follow-up: migrate the per-equation helpers, summation density, and + the adaptive `_wcsph_dt_factors` traversal onto the generator; retire the + duplicated hand kernels. +- Continue toward a cleaner production performance/results report for the + resolved drop; add true periodic-neighbor distance support later. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-18.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-18.md new file mode 100644 index 000000000..62c0673c8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-18.md @@ -0,0 +1,134 @@ +--- +type: daily-closeout +date: 2026-06-18 +user: @kunalpuri-prediqt +agent: claude +duration_minutes: 360 +aspects_touched: [gpu-nnps, warp-backend, validation-benchmarks] +--- + +# Daily Closeout - 2026-06-18 + +## Summary + +Committed the ADR-0003 fused-equation slice after @prabhu `LGTM` (`429fa23e`), +then landed ADR-0004 grid-direct neighbor traversal to attack the neighbor-cache +build that dominated the per-step wall after fusion. The WCSPH continuity-density +PEC path no longer materializes a flat CSR neighbor list: the fused equation +kernel and the adaptive CFL dt-factors kernel walk the uniform-grid cell list +directly with the support cutoff inline (`neighbor_mode='grid'` in +`warp_codegen.py`; grid-direct `_wcsph_dt_factors_grid_{f32,f64}`). The flat +path is retained for the host query API, the per-equation oracle, summation +density, and `compute_neighbor_sum`. + +## Results + +- Focused suite: 50 passed. +- Million-particle (`nx=565`) segmented profile: `build_neighbor_cache_gpu` + called 0 times on the continuity path; step wall 0.076-0.098 -> 0.059-0.064 s + (~25-35% lower); grid build ~0.0005 s; equation 0.011-0.014 -> 0.023-0.025 + s/launch (absorbs the cutoff traversal); KE delta vs flat fused -1.99e-06. +- Adaptive `nx=100` resolved guard: exactly 1393 steps (identical to committed), + fp32-scale deltas (KE relative 1.5e-08); Warp wall 7.82 s vs committed 23.63 s. +- A 6-dimension adversarial review workflow found 1 confirmed nit (adaptive-dt + grid default leaked into the summation path); fixed by defaulting that helper + to `'flat'` and having the continuity step opt into `'grid'`. + +## Decisions + +- ADR-0004 grid-direct neighbor traversal for the WCSPH continuity hot path + (Accepted). + +## Open approvals + +- Grid-direct review `reviews/2026-06-18_warp-grid-direct-neighbor-traversal.md` + pending @prabhu `LGTM` before commit. + +## Afternoon update - generator migration (ADR-0003 follow-up) + BUILD.md + +- Committed `BUILD.md` (`f2e9597d`): reproducible build + GPU-compatibility guide + (V100/A100/H100/RTX 5090/RTX PRO 6000 Blackwell). Grid-direct slice committed + earlier as `6dfcca4b`. +- Generator migration: `warp_codegen` is now the single source for every + neighbor-loop kernel; retired the ~14 duplicated hand `@wp.kernel`s. Added + `accumulate_outputs` (additive read-modify-write, for standalone viscosity), + `SummationDensity` + `WcsphCflFactor` blocks (CFL `dt_cfl` = `wp.max` neighbor + reduction), and a shared `_run_equation_group`; repointed all 5 equation + helpers + `compute_wcsph_adaptive_timestep`; refactored + `compute_wcsph_accel_continuity` onto the launcher. +- Fixed a latent disk-cache bug: the generated kernel name used + `len(_KERNEL_CACHE)` (order-dependent), forcing a full cold recompile every + session; now a deterministic md5 of the structural cache key (verified + byte-stable cross-process; warm guard loads both generated modules cached). +- Added fail-fast guards on non-canonical `out_prop`/`out_props` (the one + confirmed adversarial-review finding, minor). +- Validation: focused suite `52 passed`; continuity hot path perf-neutral + (byte-identical fused grid kernel; million steady floor `0.059 s`, 0 flat + builds, KE identical); adaptive `nx=100` kept exactly `1393` steps (KE rel + `~8e-9`). The same-session CPU-vs-Warp headlines (nx=100 `23.6x`; 1M/100-step + `41.2x` wall / `57.6x` per-step) are unchanged. + +## Late update - grid-direct everywhere (summation step paths) + +- Committed the generator migration (`ef4963c7`). Then extended grid-direct to + the summation Euler (`wc_sph_euler_step`) and KDK leapfrog step paths: the 5 + standalone helpers gained `neighbor_mode='flat'` (default), and the summation + step paths pass `'grid'`. No fusing; composition preserved. +- Now no device step path builds a flat CSR neighbor cache; + `build_neighbor_cache_gpu` is narrowed to the host query API, + `compute_neighbor_sum`, and the flat-mode oracle/cross-array tests. +- Focused suite `53 passed` (summation Euler/KDK parity holds under grid-direct, + no tolerance changes; new no-flat-cache assertion). Continuity adaptive guard + unchanged (`1393` steps). Adversarial review (4 dimensions): 0 findings. + +## Evening update - periodic min-image + results-report prep + +- Grid-direct-summation slice committed (`63dfa254`). +- Periodic minimum-image (ADR-0004 follow-up): `warp_codegen` periodic grid + variant (wrapped cell index + min-image dx), `set_periodic_box` + tiled cubic + bounds, periodic auto-detect. A 5-dimension adversarial review found a MAJOR + bug (out-of-box source positions clamp-binned -> missed wrap-around neighbors) + + a minor + 3 nits; all fixed (clamp->wrap binning; box-too-small guard; clear + errors). Periodic density matches CPU min-image to 5.5e-6 (incl. out-of-box); + periodic lattice uniform; suite `57 passed`; non-periodic unchanged (`1393` + guard, 0 recompiles). Review pending @prabhu sign-off. +- Prepared results-report tooling (`generate_results_report.py` + + `RESULTS_REPORT_HOWTO.md`) to RUN on a capable GPU machine. + +## Evening update 2 - decision lenses on the cross-GPU artifact ($ / energy / roofline) + +- Added a **$ cost-of-compute lens** to `gpu-sweep/` (committed `cc5759ee`, pushed + to fork): `$ per billion particle-steps` = `($/hr)/(throughput*3600)*1e9` at the + Brev rates. At 1M: RTX 5090 / L40S `$0.0032` < RTX PRO 6000 `$0.0060` < B300 + `$0.0187`. Two figures (`cost_per_billion_1M.png` bar, `..._vs_particles.png` + log-log) + a cost table in README + experiment.md. Performance figures kept + (cost is an *additional* lens, per user). +- User asked "is there another metric we should consider?" -> ran a 5-lens expert + panel (HPC roofline, datacenter TCO, energy/sustainability, computational + physicist, GPU architect) + a synthesis. Convergent top metrics: **roofline / + bandwidth-utilization** (#1, adjudicates the "bandwidth-bound" claim) and + **energy-to-solution / perf-per-watt** (breaks the 5090=L40S dollar tie). User + chose to add both **as estimates from existing data** (explicitly: no further + GPU runs, so estimates are the final reported values). +- **Energy lens** (`energy_per_gpstep_1M.png` + table): `kJ/Gp-step` = + `TDP_W/throughput*1e6` from datasheet board power. At 1M: L40S `3.8` < RTX PRO + 6000 `4.9` < 4060 `6.9` < 5090 `8.5` < B300 `10.0`. The 350 W L40S does the same + SPH work for ~2.2x fewer joules than the 575 W 5090 (both $0.0032/Gp-step), so + L40S wins on a power-capped/owned fleet; B300 least energy-efficient (~2.6x L40S). +- **Roofline lens** (`mbu_at_1M.png` + table): analytic MBU = `throughput x ~1.12 + KB/p-step / peak_BW` (B_eff = own state + measured avg_neighbors 44.9 x ~24 B). + At 1M every card is under ~12% of peak (B300 ~2% of 8 TB/s). **Refines/partly + corrects the earlier "bandwidth/occupancy-bound" guess: the ~1.43e8 Blackwell + ceiling is occupancy/launch/grid-build bound, NOT a memory wall** -- large + untapped headroom; next perf win is occupancy/launch tuning. Updated the README + Observations bullet accordingly. +- Both estimate lenses carry explicit "model-based, not measured" caveats (TDP + overstates non-FLOP-bound draw; B_eff is an upper bound ignoring L2 reuse and is + a whole-step number). No new GPU runs; all numbers from the existing sweep JSONs. + +## Next + +- Cross-GPU artifact complete (perf + $ + energy + roofline lenses). Production + results report still committed-ready to RUN on a capable GPU. Follow-ups: + non-cubic periodic boxes; narrow the flat host-query path. (No measured + power/profiler run planned -- energy/roofline stay estimates.) diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-19.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-19.md new file mode 100644 index 000000000..f964f9432 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-19.md @@ -0,0 +1,144 @@ +--- +type: daily-closeout +date: 2026-06-19 +user: @kunalpuri-prediqt +agent: claude +duration_minutes: 60 +aspects_touched: [warp-backend, validation-benchmarks, gpu-nnps, host-integration] +--- + +# Daily Closeout - 2026-06-19 + +> Two sessions today. **Session 1 (morning)** closed out the 3D dam-break +> (Lobovsky, ADR-0005). **Session 2 (evening)** opened the next benchmark -- +> a floating / rigid body (ADR-0006), P0 + P1. See the second block below. + +## Summary + +Implemented and validated plan step 5 of the 3D dam-break (Lobovsky no-obstacle) +benchmark, ADR-0005 -- the experiment-packet runner + two-tier CPU-parity harness +on top of the already-present additive backend physics (WendlandQuintic id 2, +gravity, multi-array stepping, Tait-HG walls). ADR-0005 moved Proposed -> Accepted. + +## Results + +- Backend targeted tests (Wendland 3D, ramped gravity + 2D guard, Tait-HG, + two-array 3D dam-break step): `5 passed`. +- Tier-1 (hand-rolled CPU EPEC parity): fluid x/y/z/u/v/w + fluid/wall rho match + Warp (fp32) to ~1e-8 relative; pressure 0.17 Pa abs < fp32 Tait floor 2.06 Pa. +- Tier-2 (real `dam_break_3d_lobovsky.py` Application, dx=0.12, tf=0.05): KE / + surge-front / max-height / density agree to fp32 across checkpoints; p_max + 1.11% relative when developed; per-particle x/z ~1e-7 near rest. The near-rest + ~13% relative pressure is the fp32 Tait-EOS cancellation floor and recovers as + the flow develops. +- New cache-stability guard `test_2d_path_generated_source_is_byte_identical_to_golden` + passes (md5-pins the 2D-path generated source; asserts no `wendland`). +- Full focused suite rerun (warm cache) -- count recorded in current.md. + +## Findings / decisions + +- `wc_sph_dam_break_step` is E-P-E-C == reference `EPECIntegrator` (ADR EPEC-vs-PEC + risk resolved). +- PySPH `n_damp` damps the *timestep*, not gravity; runner applies it to dt with + full gravity. `c0 = 10*sqrt(2*9.81*0.55) ~ 32.85` (reference scheme constant). +- Cache invariant restated precisely: emitted 2D source byte-identical + a + logic-preserving `id==2` router branch (one-time recompile possible, results + unchanged). ADR-0005 wording corrected; status -> Accepted. +- Adversarial review (5 dims): 24 confirmed findings; the "blocker" (Wendland + leaves missing from `_WARP_DEVICE_FUNCS`) was refuted by the passing Wendland + runs (transitive resolution via module globals); leaves added for hygiene. +- Build cost: Wendland in the shared router inflated every generated kernel's PTX + to 10-13 MB, so a cold full-suite compile now runs ~20 min (in-process nvrtc). + +## Code touched + +- `pysph/base/warp_sph.py` (Wendland leaves into `_WARP_DEVICE_FUNCS`; no cache-key + or source impact), `pysph/base/tests/test_warp_codegen.py` (regression guard). +- New `experiments/2026-06-18_warp-dam-break-3d-runner/` (runner, smoke wrapper, + tier-1, tier-2, experiment.md, summary JSONs, smoke npz). +- ADR-0005 + graph/index, aspect contexts, current.md, session log, this daily, + review `reviews/2026-06-19_warp-3d-dam-break-lobovsky.md`. + +## Handoff + +- Lobovsky no-obstacle correctness established (3 tiers), fused, cross-GPU-swept + (L40S + RTX PRO 6000 Blackwell + 4060 anchor), committed (`81d618bc`/`0d8bafae`/ + `394b5b0e`), pushed to fork, and folded into **PR #435** (description updated + manually -- the Claude GitHub MCP can't write to upstream pypr, 403). @prabhu + review pending. +- Cross-GPU @ ~1M (fused, fixed-dt): RTX PRO 6000 39.1 Mp-st/s (163x) > L40S 25.9 + (108x) > 4060 4.5 (19x); L40S cheapest/most energy-efficient. +- Follow-ups (ADR-0005): RTX 5090 + B300 sweep (boxes busy); SPHERIC/Kleefsman + obstacle case; full `tf=2.5` run + probe-pressure vs Lobovsky data; + cubic/gaussian cross-check. + +--- + +# Session 2 (evening) - Floating / rigid body benchmark (ADR-0006, P0 + P1) + +## Summary + +Opened the next benchmark: a **floating rigid body** coupled to the SPH fluid, +the dramatic case being a 3D dam-break surge tossing a floating box (mirrors +`pysph/examples/rigid_body/dam_break3D_sph.py`). The **deliverable is a +photorealistic animation** of the Warp GPU simulation. Decided the render stack +(Blender Cycles "Tier A": splashsurf -> Cycles/OptiX -> cloud finals -> ffmpeg; +**Omniverse rejected** for this 8 GB / WSL2 box -- it can't surface a Warp point +cloud natively and headless cloud batch is broken). Then de-risked and landed the +core GPU primitive (P0 kill-test + P1 backend reduction), purely additive. + +A fixed wall and a floating body differ by one thing: the body is *integrated* +(6-DOF). So the floating body is the existing multi-array dam-break step with +"fixed" lifted -- reduce force+torque over the body, integrate the COM on the +host, rigid-transform the body particles on the device. + +## Results + +- **P0 kill-test** (`experiments/2026-06-19-warp-floating-body-rigid/`, RTX 4060): + an `atomic_add` SUM-reduction reproduces the 16-slot `RigidBodyMoments` `mi` + vector (2.7e-15 given f32 inputs; 8e-14 f64 data); host finalize (COM / inertia + tensor / torque-about-COM / `omega_dot`) matches numpy to 6e-14; device + rigid-transform exact (5.5e-17). **fp32 `atomic_add` is non-deterministic + (3.0e-6 run-to-run spread); f64 accumulators drop it to ~1e-15.** +- **P1 backend** (`pysph/base/warp_sph.py`, all additive): `_rigid_moments_reduce_f32`/ + `_rigid_moments_reduce_f64` (f64 accumulators both paths) + `_rigid_finalize_moments` + (host 6-DOF moments, reusing `rigid_body.py:128-207`) + `compute_rigid_body_moments`. +- **Tests**: `test_rigid_body_moments_matches_reference_3d[False/True]` (two + asymmetric bodies; f32 + f64 paths vs the numpy `RigidBodyMoments` reference) and + `test_rigid_moments_f32_kernel_is_accurate_and_deterministic`. Representative + regression subset + the rigid tests + the cache guard: **10 passed in 4 s**. +- **Cache stability preserved**: `test_2d_path_generated_source_is_byte_identical_to_golden` + still passes; the new kernels touch no generated source / kernel-id router. + +## Findings / decisions + +- **Locked decision: accumulate the rigid reduction in f64** -- fp32 `atomic_add` + is order-dependent / non-associative (unlike the dt-reduce's `atomic_max`), so + it is not bit-reproducible; f64 accumulation is ~deterministic (f64 round-off). +- **CPU rigid-body reference can't run on this Python 3.14 venv**: `compyle 0.9.1` + uses the removed `ast.Str` for `rigid_body.py`'s matrix `declare()`s. The Warp + GPU path is unaffected (custom codegen + host numpy). Validation is against a + faithful numpy reimplementation (tier-1 style); tier-2 deferred behind a compyle + upgrade. (Plain WCSPH refs still run -- their generated code is cached.) +- Coupling fidelity (P3): must implement the *real* Liu coupling, not a wall + mirror; equilibrium draft is coupling-blind, so validate the first-plunge + transient. Watch the `arho` double-count if reusing the fused fluid block. +- Render: chose Blender Tier A; Omniverse rejected (see Summary). + +## Code touched + +- `pysph/base/warp_sph.py` (+205): rigid reduction kernels + finalize + driver. +- `pysph/base/tests/test_warp_sph.py` (+~150): rigid tests + `wp` import binding. +- New `decisions/.../adr-0006_...md`; new `experiments/2026-06-19-warp-floating-body-rigid/` + (P0 kill-test + experiment.md); decision graph/index regenerated. + +## Handoff + +- P0 + P1 done, validated, cache-stable, committed. **Next: P2** -- host 6-DOF + integrate (RK2/Euler) + device rigid-transform wired into a per-step update. +- Then P3 (Liu coupling group + `NumberDensity` pre-pass + sibling driver + `wc_sph_dam_break_rigid_step`, body excluded from the PEC stage; resolve the + `arho` double-count), P4 (EPEC fidelity + driver guard), assemble the 3D + surge-tosses-a-box case, render via splashsurf -> Blender. +- Render gates de-risked: `pysplashsurf` installed + functional; ffmpeg still to + install (final stitch only). diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-20.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-20.md new file mode 100644 index 000000000..a5553d46a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-20.md @@ -0,0 +1,38 @@ +--- +type: daily-closeout +date: 2026-06-20 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 70 +aspects_touched: [warp-backend, particle-memory, validation-benchmarks, host-integration] +--- + +# Daily Closeout - 2026-06-20 + +## Summary + +Completed the new-week orientation and experiment-state audit. The prior NNPS/SPH, elliptical-drop, and 3D dam-break experiments have recorded successful outputs and are now marked complete; no numerical run is active. ADR-0006 floating-body work is the sole active experiment. + +## Handoff + +P2 is implemented as fully device-resident GPU moment finalization/3x3 angular +solve, RK2 6-DOF compact-state integration, and rigid particle motion. The +NumPy/PySPH formulas are now the validation oracle only. Focused P1/P2 + cache +guard: `10 passed`; final full Warp SPH regression: `49 passed`. Review +`2026-06-20_warp-device-resident-rigid-body-p2.md` awaits @prabhu `LGTM` before +commit. P3 fluid/body coupling is the next implementation slice. + +Later P2 runtime checkpoint: a 315-particle asymmetric forced/torqued box ran +2,000 GPU RK2 steps in 0.545 s, finite with error flag 0, expected COM velocity +within 1.6e-9, and relative rigid-geometry drift 9.4e-7. The fluid-coupled case +still requires P3. + +Substantial showcase: ran the existing 3D dam-break at 125,687 particles to +`t=0.8016` (2,837 adaptive GPU steps), all finite and 13.61x per-step over one +CPU core. Produced a four-view verification figure plus a polished PyVista +speed-coloured hero frame at the stronger `t=0.3996` collapse phase. Blender and +ffmpeg are absent, so no photorealistic animation was claimed. + +P2 commit remains blocked on the required @prabhu `LGTM`. Prepared the P3 Tier +2 plan for deterministic two-pass Liu fluid/rigid coupling and a sibling +dam-break rigid driver; no P3 code started. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-06-21.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-21.md new file mode 100644 index 000000000..c7723267b --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-06-21.md @@ -0,0 +1,34 @@ +--- +type: daily-closeout +date: 2026-06-21 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 45 +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +--- + +# Daily Closeout - 2026-06-21 + +## Summary + +Completed ADR-0006 P3 verification and review assembly. The final exact-tree +Warp SPH suite passed all 54 tests after the known cold generated-Wendland +compile. The P3 review embeds the scientific coupled-run hero image and records +the 7,458-particle first-plunge transient, numerical bounds, risks, and the +explicit monolithic-EPEC-oracle validation deviation. + +## Handoff + +P3 received exact reviewer approval (`alright. @prabhu: LGTM and closeout`) +and the reviewed 12-file scope was committed locally. Unrelated showcase/audit +files and the user's untracked `CODEBASE_UNDERSTANDING.md` remain excluded. +P4 remains contact, long-horizon rigid fidelity, assembled cinematic +checkpoints, and photorealistic rendering. + +The owner then requested that every remaining modified/untracked artifact be +versioned. @prabhu supplied exact `LGTM` for a separate closeout/showcase +package covering experiment status audits, the developed 3D dam-break metrics +and review images, prior closeout memory, the duplicate coupled-run source +image, and the owner-provided root codebase report. The integration boundary +was amended for the root report; the approved package was committed locally, +no code changed, and the worktree is clean. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-07-06.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-07-06.md new file mode 100644 index 000000000..2fa337663 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-07-06.md @@ -0,0 +1,53 @@ +--- +type: daily-closeout +date: 2026-07-06 +user: @kunalpuri-prediqt +agent: codex +duration_minutes: 55 +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +--- + +# Daily Closeout - 2026-07-06 + +## Summary + +Opened the dynamic adaptive particle-resolution track for genuinely large 3D +Warp problems. The approved plan has five kill-gated checkpoints: reference/ +obstacle baseline, multilevel NNPS, GPU particle lifecycle, conservative 3D +APR physics, and an adaptive obstacle dam-break. + +The implementation owner also approved a narrow prototype review exception. +In-boundary experimental Warp work may now be committed with quoted owner +authorization and a `prototype:` subject; promotion, upstream publication, and +generic host changes still require exact `@prabhu: LGTM`. + +## Results + +- Committed the approved APR plan and prototype-governance package as + `12248a8b`; no push. +- Audited the Muta--Ramachandran source and primary 3D/GPU APR references. The + open PySPH code is not a ready 3D GPU port: automatic background adaptation + is 2D and GPU particle lifecycle is unimplemented. +- Added optional fixed-obstacle support to the existing experiment runner; + backend code did not change. +- RTX 4060, `dx=0.10`, 250 steps: 1,000 fluid + 3,824 wall + 4 obstacle, + `t=0.258454926`, all finite, front `x=2.4901464`, obstacle pressure maximum + `150.147 kPa`, exact zero obstacle coordinate drift, 5.76 s warm process wall. +- Original no-obstacle one-step mode remains finite. +- A deterministic NumPy kill test confirms the 13-daughter + icosahedron-plus-center stencil is substantially better than cubic-plus-center + and benefits from unequal masses. It does not reproduce Vacondio Table 1 + under the PySPH Wendland convention (`3.58032e-4`/`0.6566` measured versus + `8.326e-5`/`0.33` published); ADR-0007 is correctly deferred. + +## Handoff + +P0 remains active. Resolve the Vacondio/PySPH kernel or stencil convention, +then add obstacle probe/impulse metrics and uniform coarse/fine baselines before +drafting ADR-0007. + +Later planning session: drafted the focused Tier-2 multilevel GPU NNPS plan. +It isolates exact device-built cross-level neighbor traversal from later +split/merge allocation, preserves existing uniform generated source, and +requires brute-force set parity plus candidate/time scaling before ADR-0007. +The plan awaits owner approval. diff --git a/.ai/implementations/blast-from-the-past/updates/daily/2026-07-07.md b/.ai/implementations/blast-from-the-past/updates/daily/2026-07-07.md new file mode 100644 index 000000000..a461fb3ab --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/daily/2026-07-07.md @@ -0,0 +1,89 @@ +--- +type: daily-closeout +date: 2026-07-07 +user: @kunalpuri-prediqt +agent: claude +duration_minutes: 0 +aspects_touched: [gpu-nnps, warp-backend, particle-memory, validation-benchmarks] +--- + +# Daily Closeout - 2026-07-07 + +## Summary + +Implemented the first runtime GPU APR milestone end to end: an exact, +device-built multilevel cell-list NNPS and its generated-equation integration. +Steps 1-3 of `plans/2026-07-06_warp-multilevel-gpu-nnps.md` are committed, all +eight kill-gate fixtures pass, the device is genuinely resident (no coordinate +host readback), a localized-refinement fixture shows ~9x less candidate work, +and generated SPH equation groups now consume the multilevel structure directly. +ADR-0007 is recorded (Proposed). + +## Work completed + +- Plan review findings folded in (`bbcb6d5e`). +- Step 1 CPU-oracle contract + adversarial oracle verification (`4107249b`). +- Step 2 `MultilevelGridWarpNNPS` in its own module: flattened per-level cell + list, variable-stencil cross-level traversal, GPU-resident assign/AABB, eight + fixtures, ~9x clustered scaling gate (`4107249b`, `4fc39880`, `711c0356`). +- ADR-0007 Proposed + decision graph regenerated (`64e1c6da`). +- Step 3 generated multilevel equation loop: codegen mode + launcher + routing + + periodic guard; summation-density multilevel==uniform-grid in 2D/3D + (`8861d331`). + +## Decisions made (ADRs) + +- ADR-0007 (Proposed): device-built multilevel cell-list NNPS for adaptive + resolution. Accept gated on the dense-vs-sparse memory check. + +## Plans approved (full + lightweight) + +None new. Work executed under the already-approved +`plans/2026-07-06_warp-multilevel-gpu-nnps.md` (owner authorization "begin"), +now status `in-progress`. + +## Files changed + +New `pysph/base/warp_multilevel_nnps.py`; edits to `warp_nnps.py`, +`warp_codegen.py`, `warp_sph.py`, `test_warp_nnps.py`, `test_warp_sph.py`; the +plan, ADR-0007, decision graph; memory files. + +## Tests / experiments run + +Suites (SEPARATE invocations): `test_warp_nnps.py` 34, `test_warp_codegen.py` +10, `test_warp_sph.py` 57 (56 sim + the multilevel-periodic guard). Oracle +verification workflow: 24,964 destination comparisons, zero disagreements. +Candidate-reduction diagnostic: 197,236 vs 1,774,231 pairs (~9x) on the +synthetic clustered-refinement fixture. + +## Problems encountered + +- The combined single-process Warp test suite hangs at ~71-73% inside the WSL2 + in-process PTX-JIT compiler. Diagnosed (faulthandler/SIGABRT stack) as a + PRE-EXISTING module-footprint accumulation limit (clean tree hangs too as the + suite grew 47->54+ tests), not a regression. Mitigation: multilevel kernels + live in their own module, and the gate is separate per-file pytest runs. +- An fp32 dtype bug in `assign_particle_levels` (fp64 edges vs fp32 device h) + and two missing input-validation guards were caught by tests/verification and + fixed. A pre-existing `BruteForceWarpNNPS` fp32 cached-path stray-index bug at + the exact cutoff was found and recorded (not fixed; out of scope). + +## Open questions raised + +- Do dense per-level grids beat sparse hash/sort storage on representative + localized-refinement distributions? (Decision gate before accepting ADR-0007.) +- Can the per-`update()` O(nlevels) metadata readback be eliminated for the + production path? + +## Next actions + +- Adaptive-timestep + fused continuity/pressure/viscosity multilevel parity. +- fp64 exercise of the multilevel path. +- Dense-vs-sparse memory measurement, then move ADR-0007 to Accepted. + +## Notes for next AI session + +HEAD `8861d331` on `blast-from-the-past`. Venv +`/home/kunalp/.pqt_venv_e0b41259/bin/python`; commit `--no-verify`. Run the +three warp test files SEPARATELY. See memory +`warp-multilevel-kernels-separate-module` and `warp-nnps-fp32-cached-boundary-bug`. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-15_0719.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-15_0719.md new file mode 100644 index 000000000..30d00eba7 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-15_0719.md @@ -0,0 +1,47 @@ +--- +type: session-log +date: 2026-06-15 +start: 07:19 +end: 07:19 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, cython-boundary, validation-benchmarks, host-integration] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/conventions.md, .ai/implementations/blast-from-the-past/glossary.md, .ai/implementations/blast-from-the-past/templates/decision-template.md, .ai/implementations/blast-from-the-past/templates/session-log-template.md, .ai/implementations/blast-from-the-past/templates/daily-closeout-template.md, .ai/implementations/blast-from-the-past/templates/weekly-closeout-template.md, .ai/implementations/blast-from-the-past/templates/experiment-template.md, .ai/implementations/blast-from-the-past/templates/reference-note-template.md] +--- + +# Session - 2026-06-15 07:19 + +## Goal of this session + +Install the `.ai/` implementation-scoped memory system for `blast-from-the-past`. + +## What happened + +- Collected and confirmed implementation inputs. +- Performed narrow discovery around `.pxd/.pyx` boundary and GPU NNPS files. +- Proposed and received approval for six aspects. +- Created the `.ai/` memory structure, scripts, templates, aspect skeletons, and initial closeouts. +- Generated ADR index/graph, ran validation, installed the pre-commit hook, and confirmed the hook blocks invalid closeouts. +- Created and received approval for the migration plan to nest implementation memory under `.ai/implementations/blast-from-the-past/`. +- Moved implementation-scoped files into the nested directory, added top-level `.ai` router files, updated the root pointer and pre-commit hook, and revalidated. + +## Lightweight plans + +- None. This was scaffold setup under the master prompt's approved flow. + +## Decisions reached + +- ADR-0001 - Adopt `.ai/` memory system. + +## Code touched + +- No host application code touched. +- Memory files under `.ai/implementations/blast-from-the-past/`, top-level `.ai` router files, and root pointer `AGENTS.md` created/updated. + +## Experiments started / advanced / completed + +- Placeholder experiment created for smoke testing only. + +## Handoff for next session + +Start by booting through `.ai/AGENTS.md`, then define measurable success criteria and the first baseline benchmark. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1151.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1151.md new file mode 100644 index 000000000..3267a8ac1 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1151.md @@ -0,0 +1,98 @@ +--- +type: session-log +date: 2026-06-16 +start: 11:51 +end: 12:18 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-15.md, .ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md, .ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md, .ai/implementations/blast-from-the-past/skills/working-with-host-code.md, .ai/implementations/blast-from-the-past/skills/coding-style.md, .ai/implementations/blast-from-the-past/skills/testing.md, .ai/implementations/blast-from-the-past/templates/plan-template.md, .ai/implementations/blast-from-the-past/templates/review-template.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-elliptical-drop-application-runner.md, .ai/implementations/blast-from-the-past/experiments/2026-06-15_warp-wcsph-euler-step/experiment.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md, .ai/implementations/blast-from-the-past/reviews/2026-06-16_warp-repeated-step-leapfrog-checkpoint.md] +--- + +# Session - 2026-06-16 11:51 + +## Goal of this session + +Answer the next-equation and roadmap question for Warp WCSPH work, then +implement the approved repeated-step leapfrog checkpoint. + +## What happened + +- Booted through the implementation router and confirmed no experiment output logs needed reconciliation. +- Reviewed current Warp SPH, NNPS, particle-memory, validation, and host-integration notes. +- Inspected the current Warp equation and NNPS code surfaces. +- Identified the immediate blocker for repeated stepping: `UniformGridWarpNNPS.update()` pushes host coordinates before rebuilding, while `wc_sph_euler_step()` updates coordinates on the device. +- Created and posted Tier 2 plan `2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh`. +- Received required approval: "APPROVED". +- Added device-authoritative NNPS refresh with `update(push=False)`. +- Added Warp leapfrog kick/drift kernels, periodic position wrapping, and + `wc_sph_leapfrog_step()`. +- Added focused tests for device-coordinate refresh, kick/drift/wrap, and KDK + leapfrog correctness against CPU references. +- Updated the active WCSPH experiment, current pointer, and daily closeout. +- Prepared the required review artifact, recorded `@prabhu: LGTM`, amended + the implementation boundary, and proceeded to local commit only. +- Created and approved plan + `2026-06-16_warp-elliptical-drop-application-runner`. +- Added `warp_elliptical_drop_runner.py`, `run_correctness.sh`, and + `experiment.md` for an application-style Warp elliptical-drop smoke run. +- Ran the smoke wrapper successfully with finite metrics and wrote + `results-smoke.npz`. +- Ran a small ramp ladder through `nx=12`, `nx=16`, and `nx=24`; all cases + reported finite metrics. +- Prepared the required review artifact, recorded `@prabhu: LGTM`, and + proceeded to local commit for the runner/ramp slice. + +## Lightweight plans + +- Full plan: `plans/2026-06-16_warp-repeated-step-leapfrog-and-periodic-refresh.md`. + Approval quote recorded verbatim in the plan: "APPROVED". +- Full plan: `plans/2026-06-16_warp-elliptical-drop-application-runner.md`. + Approval quote recorded verbatim in the plan: "APPROVED". + +## Decisions reached + +- No ADR decision reached. +- Review sign-off reached: `@prabhu: LGTM`. +- Review sign-off reached for runner/ramp slice: `@prabhu: LGTM`. + +## Code touched + +- `pysph/base/warp_nnps.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_nnps.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/` + +## Experiments started / advanced / completed + +- Advanced `experiments/2026-06-15_warp-wcsph-euler-step/experiment.md`. +- Validation: + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +29 passed, 2 warnings in 5.82s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +particles: 204 +time: 2e-05 +``` + +```text +Warp elliptical-drop ramp: +nx=12, particles=455, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=5, time=5e-05, all_finite=true +nx=16, particles=805, steps=20, time=0.0001, all_finite=true +nx=24, particles=1808, steps=10, time=5e-05, all_finite=true +``` + +## Handoff for next session + +The stale-host-coordinate repeated-step blocker is fixed for the Warp prototype. +The first application-style Warp elliptical-drop smoke runner exists and passes +for conservative smoke/ramp cases. Next concrete work is to close the physics +gap, starting with artificial viscosity in the Warp momentum path, then Tait +EOS and XSPH/Gaussian support before PySPH Application/Solver integration. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1336.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1336.md new file mode 100644 index 000000000..aad4aee25 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1336.md @@ -0,0 +1,73 @@ +--- +type: session-log +date: 2026-06-16 +start: 13:36 +end: 14:00 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks, host-integration] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1151.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-artificial-viscosity-momentum-term.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-16 13:36 + +## Goal of this session + +Implement the approved artificial-viscosity term for the Warp WCSPH momentum +path and exercise it in the elliptical-drop runner. + +## What happened + +- Resumed after plan approval with the approved plan + `2026-06-16_warp-artificial-viscosity-momentum-term`. +- Added float32/float64 Warp kernels for Monaghan-style artificial viscosity + using constant `c0`, `alpha`, and `beta`. +- Added `compute_artificial_viscosity()` as an additive acceleration pass over + existing `au`, `av`, and `aw`. +- Wired optional `alpha` and `beta` through `wc_sph_leapfrog_step()` and + `wc_sph_euler_step()` while preserving the inviscid default behavior. +- Added a CPU-reference test covering approaching pairs and proving the + viscosity pass adds onto existing acceleration. +- Added `--alpha` and `--beta` to the Warp elliptical-drop runner and recorded + them in output metrics. +- Updated experiment, current, validation, GPU NNPS, open-question, and daily + memory. +- Drafted review + `2026-06-16_warp-artificial-viscosity-momentum-term`, pending + `@prabhu: LGTM`. + +## Approval + +- Full plan: `plans/2026-06-16_warp-artificial-viscosity-momentum-term.md`. + Approval quote recorded verbatim in the plan: "APPROVED". + +## Validation + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +30 passed, 2 warnings in 4.39s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +alpha: 0.1 +beta: 0.0 +particles: 204 +time: 2e-05 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 16 --steps 5 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-avisc-nx16-steps5.npz +all_finite: true +particles: 805 +time: 5e-05 +kinetic_energy: 7868.737673401772 +``` + +## Handoff + +Artificial viscosity is implemented and validated locally, and review +`2026-06-16_warp-artificial-viscosity-momentum-term` is drafted. Before any +commit, post/record `@prabhu: LGTM`, update the review sign-off, and then +commit. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1357.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1357.md new file mode 100644 index 000000000..3cf0bf0e7 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_1357.md @@ -0,0 +1,81 @@ +--- +type: session-log +date: 2026-06-16 +start: 13:57 +end: 14:18 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-tait-eos-and-sound-speed.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-16 13:57 + +## Goal of this session + +Implement the approved Warp Tait EOS and per-particle sound speed `cs` path, +then answer whether that makes elliptical drop production-ready. + +## What happened + +- Recorded approval for plan + `2026-06-16_warp-tait-eos-and-sound-speed`: "APPROVED". +- Confirmed from `pysph.sph.wc.basic.TaitEOS` that PySPH computes + `p = p0 + (rho0*c0*c0/gamma) * ((rho/rho0)**gamma - 1)` and + `cs = c0 * (rho/rho0)**(0.5*(gamma - 1))`. +- Added Warp float32/float64 Tait EOS kernels and `compute_tait_eos()`. +- Updated artificial viscosity to use pair-averaged `cs` with a constant `c0` + fallback for callers that have not run Tait EOS. +- Added `eos='tait'`/`gamma` options to WCSPH Euler and leapfrog helpers while + preserving `eos='isothermal'` as the compatibility default. +- Switched the elliptical-drop runner default to Tait EOS with `gamma=7.0`. +- Added focused tests for direct Tait EOS and a small WCSPH Euler step using + Tait pressure plus `cs`-based artificial viscosity. +- Ran the Tait smoke and a small `nx=16` ramp; both stayed finite. +- Drafted review `2026-06-16_warp-tait-eos-and-sound-speed` and recorded + `@prabhu: LGTM`. + +## Approval + +- Full plan: `plans/2026-06-16_warp-tait-eos-and-sound-speed.md`. + Approval quote recorded verbatim in the plan: "APPROVED". + +## Validation + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +32 passed, 2 warnings in 4.14s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +eos: tait +gamma: 7.0 +particles: 204 +cs_min: 3.9725253582000732 +cs_max: 19.997066497802734 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 16 --steps 5 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --output .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/results-tait-nx16-steps5.npz +all_finite: true +particles: 805 +time: 5e-05 +cs_min: 5.072288990020752 +cs_max: 19.99852752685547 +``` + +## Production readiness answer + +Tait EOS and per-particle `cs` are not enough for production elliptical drop. +The runner is a stronger smoke workload, but production/published readiness +still needs XSPH, Gaussian kernel support or a kernel decision, adaptive +timestep/CFL tracking, and PySPH baseline comparison. + +## Handoff + +The Tait/`cs` slice is implemented, validated, reviewed, and committed as +`fe8df93f`. The next action is to choose the next production gap for +elliptical drop: XSPH, Gaussian support or a kernel decision, adaptive +timestep/CFL tracking, or PySPH baseline comparison. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2227.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2227.md new file mode 100644 index 000000000..ed1397059 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2227.md @@ -0,0 +1,74 @@ +--- +type: session-log +date: 2026-06-16 +start: 22:27 +end: 23:00 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, particle-memory, validation-benchmarks] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md] +--- + +# Session - 2026-06-16 22:27 + +## Goal of this session + +Implement the approved XSPH, Gaussian kernel, adaptive timestep, and CPU/Warp +side-by-side comparison slice for the Warp elliptical-drop runner. + +## What happened + +- Recorded approval for plan + `2026-06-16_warp-xsph-gaussian-adaptive-baseline`: "APPROVED". +- Added Gaussian kernel value/gradient selection to Warp SPH kernels while + preserving CubicSpline as the default. +- Added device-side XSPH correction and `leapfrog_drift_xsph()` using + `u + ax`, matching PySPH's leapfrog XSPH correction pattern. +- Added `dt_cfl`/`dt_force` factor kernels and scalar device reductions for + adaptive WCSPH timesteps. +- Updated `wc_sph_leapfrog_step()` to optionally compute adaptive dt and + return the scalar timestep used. +- Updated the Warp elliptical-drop runner with Gaussian, XSPH, adaptive dt, + timestep diagnostics, and explicit final checkpoint pulls. +- Added `compare_warp_pysph_elliptical_drop.py`, which runs the Warp path and + a CPU PySPH-primitive baseline and writes side-by-side image output. +- Updated experiment and aspect memory with the new validation state and the + remaining full-Application baseline caveat. + +## Approval + +- Full plan: `plans/2026-06-16_warp-xsph-gaussian-adaptive-baseline.md`. + Approval quote recorded verbatim in the plan: "APPROVED". + +## Validation + +```text +python -m pytest pysph/base/tests/test_warp_sph.py -q +18 passed, 2 warnings in 3.50s +``` + +```text +bash .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/run_correctness.sh +all_finite: true +kernel: gaussian +radius_scale: 3.0 +xsph_eps: 0.5 +adaptive_dt: true +particles: 204 +rho_min: 0.534595251083374 +rho_max: 0.9998562335968018 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/compare_warp_pysph_elliptical_drop.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --xsph-eps 0.5 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --dt-max 1.0e-5 --prefix comparison-smoke +CPU all_finite: true, particles: 204, rho_min: 0.534595094929311 +Warp all_finite: true, particles: 204, rho_min: 0.534595251083374 +image: experiments/2026-06-16_warp-elliptical-drop-runner/comparison-smoke.png +``` + +## Handoff + +This slice is implemented and validated locally but still needs review/LGTM +and commit. The next validation escalation is a longer production-oriented +elliptical-drop comparison against the full PySPH `Application/Solver` outputs +at `t=0.0008` and `t=0.0038`. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2359.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2359.md new file mode 100644 index 000000000..172b9b97f --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2359.md @@ -0,0 +1,95 @@ +--- +type: session-log +date: 2026-06-16 +start: 23:59 +end: 00:26 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-16_2227.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-resolved-elliptical-drop-performance-comparison.md, .ai/implementations/blast-from-the-past/plans/2026-06-16_warp-continuity-density-leapfrog-parity.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-16 23:59 + +## Goal of this session + +Make the resolved elliptical-drop comparison apples to apples by matching +PySPH's continuity-density evolution in the Warp path, then rerun the resolved +`nx=100` comparison. + +## What happened + +- Recorded approval for plan + `2026-06-16_warp-continuity-density-leapfrog-parity`: "APPROVED". +- Confirmed `elliptical_drop_no_scheme.py` uses `TaitEOS`, then + `ContinuityEquation`, `MomentumEquation`, and `XSPHCorrection` with + `WCSPHStep`. +- Added device-side Warp kernels/helpers to save WCSPH reference state + (`x0/y0/z0`, `u0/v0/w0`, `rho0`) and apply PEC-style stages that update + velocity, position, and density from `arho`. +- Extended `wc_sph_leapfrog_step()` with `density_mode='continuity'` while + preserving the existing summation-density KDK path as the default. +- Threaded `density_mode` through the Warp elliptical-drop runner and resolved + comparison script. +- Ran a small resolved `nx=8` continuity smoke; CPU/Warp metrics matched nearly + exactly. +- Ran the full resolved `nx=100` continuity comparison through `t=0.0008` and + `t=0.0038`. +- Updated experiment and aspect memory to label the earlier summation-density + resolved run as diagnostic only. +- Pruned bulky generated HDF5/NPZ checkpoint dumps and kept summary JSON plus + side-by-side PNG artifacts. + +## Approval + +- Full plan: `plans/2026-06-16_warp-continuity-density-leapfrog-parity.md`. + Approval quote recorded verbatim in the plan: "APPROVED". + +## Validation + +```text +python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py +pass +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py -q -k 'pec_stage or continuity_mode' +2 passed, 2 warnings in 11.98s +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +38 passed, 2 warnings in 3.67s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py --nx 8 --steps 2 --dt 1.0e-5 --rho0 1.0 --c0 20.0 --p0 0.0 --alpha 0.1 --beta 0.0 --gamma 7.0 --adaptive-dt --cfl 0.25 --dt-min 1.0e-7 --density-mode continuity +all_finite: true +density_mode: continuity +rho_min: 0.9995885491371155 +rho_max: 1.0004163980484009 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 8 --output-times 0.0008 --prefix continuity-smoke-nx8 --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/continuity-smoke --max-steps 100000 +PySPH CPU Application: 2.1658526139945025 s, 48 steps +Warp continuity-density: 0.47872533099143766 s, 32 steps +speedup_wall_time: 4.52420725159672 +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-continuity --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 228.25765374601178 s, 1393 steps +Warp continuity-density: 30.008050591000938 s, 1804 steps +speedup_wall_time: 7.606547218180963 +t=0.0008 major-axis delta: 3.6375168877000874e-08 +t=0.0038 major-axis delta: 1.5947661098358878e-06 +t=0.0038 rho_min delta: -4.816405699936688e-06 +``` + +## Handoff + +The continuity-density parity slice was reviewed with `@prabhu: LGTM` and +committed locally as `169f3fd5`. The next likely technical question is whether +to match PySPH's timestep policy/output-time handling more exactly, or move to +true periodic neighbor interactions. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0824.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0824.md new file mode 100644 index 000000000..f013d32f3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0824.md @@ -0,0 +1,81 @@ +--- +type: session-log +date: 2026-06-17 +start: 08:24 +end: 09:05 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-16.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-17 08:24 + +## Goal of this session + +Make the resolved elliptical-drop Warp run use the same adaptive timestep +policy as the PySPH CPU Application so step counts are apples to apples. + +## What happened + +- Created plan `2026-06-17_warp-adaptive-timestep-policy-parity`. +- Recorded approval for the plan: "approved". +- Added scalar-only adaptive timestep policy hooks to `wc_sph_leapfrog_step()`: + `adaptive_dt_scale` for PySPH-style damping and `step_dt_max` for temporary + output-time landing caps. +- Added a focused test proving adaptive timestep scaling and current-step cap + behavior without changing the existing adaptive candidate calculation. +- Updated `resolved_elliptical_drop_comparison.py` with + `--warp-timestep-policy {pysph,current}` and `--warp-dt-max`. +- Mirrored PySPH's `n_damp` sine ramp in the resolved runner while keeping + repeated particle state device-authoritative and avoiding full host/device + particle transfers inside the loop. +- Ran a small `nx=8` smoke and the full `nx=100` resolved Application + comparison. +- Pruned bulky generated HDF5/NPZ checkpoint dumps and kept summary JSON plus + side-by-side PNG artifacts. +- Updated experiment, aspect, current, and closeout memory with the final + timestep-policy result. + +## Approval + +- Full plan: `plans/2026-06-17_warp-adaptive-timestep-policy-parity.md`. + Approval quote recorded verbatim in the plan: "approved". + +## Validation + +```text +python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py +pass +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py -q -k 'adaptive_timestep_scale or adaptive_timestep_matches' +2 passed, 2 warnings +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +39 passed, 2 warnings in 4.83s +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 8 --output-times 0.0008 --prefix timestep-policy-smoke-nx8 --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/timestep-policy-smoke --max-steps 100000 +PySPH CPU Application: 48 steps +Warp continuity-density: 48 steps +``` + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-timestep-policy --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved --max-steps 10000000 +PySPH CPU Application: 233.97314716299297 s, 1393 steps +Warp continuity-density: 23.629107111992198 s, 1393 steps +speedup_wall_time: 9.901903870258701 +t=0.0038 major-axis delta: -3.1258252297661215e-07 +t=0.0038 rho_min delta: -8.228944999855159e-07 +``` + +## Handoff + +The timestep-policy parity slice was reviewed with `@prabhu: LGTM` and +committed locally. The next likely work is the production resolved-drop +performance/results report and true periodic-neighbor distance support. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0930.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0930.md new file mode 100644 index 000000000..587a87437 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_0930.md @@ -0,0 +1,50 @@ +--- +type: session-log +date: 2026-06-17 +start: 09:30 +end: 09:39 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [particle-memory, validation-benchmarks, warp-backend] +memory_consulted: [AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-17 09:30 + +## Goal of this session + +Answer whether the current Warp tests/resolved run use fp64, and run an +explicit fp32 `nx=100` Warp-only comparison on this machine. + +## What happened + +- Verified that `WarpEllipticalDropRunner.create_particles()` builds host + arrays as `float64`. +- Verified that `WarpDeviceHelper` casts floating properties to + `compyle.config.get_config().use_double`. +- Probed the active environment: `get_config().use_double` is `False`, so + current Warp device arrays are `float32`. +- Ran an explicit Warp-only fp32 resolved `nx=100` rerun with the same timestep + policy and checkpoints. +- Pruned raw checkpoint `.npz` outputs and kept the compact summary JSON. + +## Validation / result + +```text +python .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/resolved_elliptical_drop_comparison.py --nx 100 --output-times 0.0008,0.0038 --prefix resolved-nx100-fp32-warp-only --output-dir .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/fp32 --skip-pysph-application --max-steps 10000000 +Warp fp32 wall time: 26.5732471299998 s +steps: 1393 +average step time: 0.019076272167982626 s +all_finite: true at both checkpoints +``` + +```text +python .ai/implementations/blast-from-the-past/scripts/validate-memory.py +validate-memory: PASS +``` + +## Handoff + +No code changes were needed for fp32. A true fp64 Warp comparison would require +forcing `get_config().use_double = True` before creating the Warp ParticleArray, +or adding an explicit runner precision flag. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1636.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1636.md new file mode 100644 index 000000000..82045128e --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1636.md @@ -0,0 +1,68 @@ +--- +type: session-log +date: 2026-06-17 +start: 16:36 +end: 16:44 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [validation-benchmarks, warp-backend, gpu-nnps] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md] +--- + +# Session - 2026-06-17 16:36 + +## Goal of this session + +Run a million-particle, ten-timestep CPU/GPU elliptical-drop comparison, then +probe whether a million-particle GPU-only full elliptical-drop run is feasible +without starting a multi-hour job. + +## What happened + +- Started from the existing Warp elliptical-drop runner and PySPH + `elliptical_drop_no_scheme.py` Application baseline. +- Chose `nx=565`, which creates 1,002,885 particles for the circular patch. +- Chose fixed timestep for the ten-step CPU/GPU comparison so both backends do + exactly the same number of steps without adaptive damping or output-time + policy differences. +- Ran the ten-step PySPH Application CPU baseline and matching Warp runner. +- Started a foreground full-run probe, observed it was still inside the first + adaptive step after about 56 seconds, and interrupted it. +- Ran a bounded one-step adaptive GPU probe instead; it completed in 4.87 s and + stayed finite. +- Did not start the full `nx=565`, `tf=0.0076` GPU-only run after the user said + "no multi hour run". + +## Validation + +```text +python pysph/examples/elliptical_drop_no_scheme.py --nx 565 --tf 0.000003732778967800475 --timestep 0.0000003732778967800475 --no-adaptive-timestep --n-damp 0 --pfreq 10 --fname million-pysph --directory .../million-cpu-gpu-10step/pysph --logfile '' --quiet +real 57.48 +``` + +```text +python .../warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .../million-cpu-gpu-10step/warp/million-warp.npz +real 7.17 +``` + +Ten-step result summary: + +- particles: 1,002,885 +- CPU wall time: 57.48 s +- Warp wall time: 7.17 s +- speedup: 8.01673640167364x +- both final checkpoints finite +- summary: `experiments/2026-06-16_warp-elliptical-drop-runner/million-cpu-gpu-10step/million-cpu-gpu-10step-summary.json` + +One-step adaptive GPU probe: + +```text +python .../warp_elliptical_drop_runner.py --nx 565 --steps 1 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --adaptive-dt --cfl 0.3 --dt-min 1.0e-10 --dt-max 0.0000003732778967800475 --density-mode continuity --output .../full-nx565-gpu/adaptive-one-step-probe.npz +real 4.87 +all_finite: true +``` + +## Handoff + +No long-running processes remain. The full million-particle GPU elliptical-drop +run was intentionally not launched because it is likely multi-hour. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1713.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1713.md new file mode 100644 index 000000000..6b5e6bc87 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1713.md @@ -0,0 +1,88 @@ +--- +type: session-log +date: 2026-06-17 +start: 17:13 +end: 17:17 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks] +memory_consulted: [AGENTS.md, .ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/experiment.md, .ai/implementations/blast-from-the-past/templates/plan-template.md, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-adaptive-timestep-policy-parity.md, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md] +--- + +# Session - 2026-06-17 17:13 + +## Goal of this session + +Reduce the million-particle Warp continuity-density step from repeated +neighbor-cache rebuilds toward one cache per PEC half-stage, then rerun the +bounded headline benchmark. + +## Approval + +- Plan: `plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md` +- Approval quote recorded verbatim in the plan: "approved" + +## What happened + +- Added optional `cache` arguments to Warp neighbor-loop helpers while + preserving their default behavior. +- Updated `_wc_sph_pec_continuity_step()` to build one neighbor cache before + each half-stage and reuse it for pressure gradient, artificial viscosity, + continuity, XSPH, and adaptive timestep computation. +- Added focused tests proving direct helper behavior and that the full + continuity-density step builds two equation caches per step. +- Reran the million-particle segmented cache profile and the comparable + fixed-step headline Warp run. + +## Validation + +```text +python -m py_compile pysph/base/warp_sph.py pysph/base/tests/test_warp_sph.py +pass +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py -k 'prebuilt_neighbor_cache or reuses_one_neighbor_cache or continuity_mode_matches_cpu_pec_state or adaptive_timestep' +5 passed, 18 deselected, 2 warnings in 2.65s +``` + +```text +python -m pytest -q pysph/base/tests/test_warp_sph.py pysph/base/tests/test_warp_nnps.py +41 passed, 2 warnings in 1.75s +``` + +Segmented million-particle profile after cache reuse: + +```text +particles 1002885 +step 1 wall 0.133124 cache_calls 2 cache_time 0.050880 avg_neighbors_per_cache 44.873 +step 2 wall 0.098335 cache_calls 2 cache_time 0.037409 avg_neighbors_per_cache 44.873 +step 3 wall 0.109608 cache_calls 2 cache_time 0.046048 avg_neighbors_per_cache 44.873 +step 4 wall 0.099814 cache_calls 2 cache_time 0.036122 avg_neighbors_per_cache 44.873 +step 5 wall 0.103505 cache_calls 2 cache_time 0.038493 avg_neighbors_per_cache 44.873 +step 6 wall 0.108904 cache_calls 2 cache_time 0.040877 avg_neighbors_per_cache 44.873 +step 7 wall 0.111996 cache_calls 2 cache_time 0.042252 avg_neighbors_per_cache 44.873 +step 8 wall 0.118203 cache_calls 2 cache_time 0.044779 avg_neighbors_per_cache 44.873 +step 9 wall 0.130790 cache_calls 2 cache_time 0.047994 avg_neighbors_per_cache 44.873 +step 10 wall 0.138290 cache_calls 2 cache_time 0.052126 avg_neighbors_per_cache 44.873 +``` + +Comparable headline run: + +```text +python .../warp_elliptical_drop_runner.py --nx 565 --steps 10 --dt 0.0000003732778967800475 --rho0 1.0 --c0 1400.0 --p0 0.0 --alpha 0.1 --beta 0.0 --eos tait --gamma 7.0 --kernel gaussian --xsph-eps 0.5 --density-mode continuity --output .../million-cpu-gpu-10step-cache-reuse/warp/million-warp.npz +real 4.51 +``` + +Result: + +- Previous Warp: 7.17 s / 10 steps +- New Warp: 4.51 s / 10 steps +- CPU baseline: 57.48 s / 10 steps +- New speedup: 12.7450110864745x +- Warp improvement over previous: 1.58980044345898x + +## Handoff + +The next likely optimization is fusing or batching neighbor-loop equation +traversals and reducing synchronization after each helper. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1810.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1810.md new file mode 100644 index 000000000..5e57f2929 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1810.md @@ -0,0 +1,82 @@ +--- +type: session-log +date: 2026-06-17 +start: 18:10 +end: 18:55 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [warp-backend, gpu-nnps, validation-benchmarks] +memory_consulted: [.ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-17.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1713.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0002_propose-warp-device-mirror-for-particle-array.md, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-reuse-neighbor-cache-per-stage.md, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md, .ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/warp_elliptical_drop_runner.py] +--- + +# Session - 2026-06-17 18:10 + +## Goal of this session + +Build a minimal composable Warp equation-group code generator (ADR-0003) and +make the continuity-density PEC half-stage its first consumer: fuse continuity + +pressure gradient + artificial viscosity + XSPH into one generated kernel, then +rerun the million-particle headline benchmark. + +## Approval + +- Plan: `plans/2026-06-17_warp-fuse-neighbor-loop-equations.md` + (revised to the generator design). +- ADR: `ADR-0003` accepted. +- Approval quote recorded verbatim in the plan and below: "APPROVED" + +## What happened + +- Completed boot sequence; confirmed the four separate neighbor-loop launches + per PEC half-stage as the bottleneck after cache reuse. +- Asked the architecture question (hand-fuse vs dynamic codegen); user chose + "Generator now". Proved Warp 1.14 dynamic kernel generation works end-to-end + on the RTX 4060 (recorded in ADR-0003), including calling existing device + `wp.func`s from a generated kernel. +- Wrote ADR-0003 (Accepted) and the revised plan; approval verbatim: "APPROVED". +- Built `pysph/base/warp_codegen.py` (WarpEquation protocol + `build_group_kernel` + with templating/exec/`wp.Kernel(source=)` + structural cache). +- Ported continuity/pressure-gradient/Monaghan-viscosity/XSPH into `WarpEquation` + blocks and `_WCSPH_CONTINUITY_BLOCKS`; added `compute_wcsph_accel_continuity` + (one launch + one sync), factored `_apply_wcsph_eos`, rewired + `_wc_sph_pec_continuity_step`, and removed the dead `_compute_wcsph_xsph`. +- Added `test_warp_codegen.py` and two `test_warp_sph.py` tests; full focused + suite `47 passed`. +- Reran the segmented million-particle profile, the headline fixed-step + benchmark, and a Warp-only adaptive `nx=100` guard. Recorded the summary JSON + and the review artifact (sign-off pending @prabhu). + +## Lightweight plans + +- None (Tier 2 plan + ADR-0003 covered the work). + +## Decisions reached + +- ADR-0003: adopt dynamic Warp equation-group code generation (Accepted). + +## Code touched + +- `pysph/base/warp_codegen.py` (new) +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_codegen.py` (new) +- `pysph/base/tests/test_warp_sph.py` + +## Experiments started / advanced / completed + +- Advanced `experiments/2026-06-16_warp-elliptical-drop-runner`: added the + fused-equation (code-generation) million-particle rerun, segmented profile, + and adaptive `nx=100` guard. New summary + `million-cpu-gpu-10step-fused-eqns/million-cpu-gpu-10step-fused-eqns-summary.json`. +- Result: equation-kernel launches 8 -> 2 per step; equation time/step + ~0.064-0.088 s -> 0.011-0.014 s (~5-6x); step wall (steady) ~25% lower; cache + build now dominant. 10-step headline wall overhead/IO-bound (best 3.77 s, + `15.25x` vs CPU). Parity vs prior Warp essentially exact (KE delta -6.4e-09); + adaptive guard kept exactly 1393 steps. + +## Handoff for next session + +- Review sign-off from @prabhu is pending; commit only after `LGTM`. +- Next optimization candidates: (a) reduce/avoid the neighbor-cache build (now + ~45-50% of step wall), and (b) ADR-0003 follow-up: migrate the standalone + per-equation helpers, summation-density, and the adaptive `_wcsph_dt_factors` + traversal onto the generator and retire the duplicated hand kernels. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1030.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1030.md new file mode 100644 index 000000000..f0c00f12a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1030.md @@ -0,0 +1,86 @@ +--- +type: session-log +date: 2026-06-18 +start: 10:30 +end: 11:15 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [gpu-nnps, warp-backend, validation-benchmarks] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/reviews/2026-06-17_warp-fuse-neighbor-loop-equations-via-code-generation.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-17_1810.md, .ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md, .ai/implementations/blast-from-the-past/plans/2026-06-17_warp-fuse-neighbor-loop-equations.md, pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/base/warp_nnps.py] +--- + +# Session - 2026-06-18 10:30 + +## Goal of this session + +Record @prabhu's `LGTM` on the ADR-0003 fused-equation review, commit that +slice, then attack the neighbor-cache build (the dominant per-step cost after +fusion) via ADR-0004 grid-direct neighbor traversal: have the fused continuity +equation kernel and the adaptive CFL dt-factors kernel walk the uniform-grid +cell list directly instead of materializing a flat CSR neighbor list. + +## What happened + +- Recorded @prabhu `LGTM` on the fused-equation review and committed the slice + as `429fa23e` (pre-commit hook calls bare `python`, absent in this shell; ran + `validate-memory.py` manually with the venv python -> PASS, committed with + `--no-verify`). +- Discovered the dev environment: venv `/home/kunalp/.pqt_venv_e0b41259/` + (Python 3.14, warp 1.14, RTX 4060); saved as a reference memory. +- Chose grid-direct over incremental cache tuning (user picked "Grid-direct"); + wrote ADR-0004 (Accepted) + plan; approval quote "APPROVED". +- Implemented `neighbor_mode='grid'` in `warp_codegen.py` (signature swap, + pre/post-cutoff geometry split, cell-block walk with inline support cutoff, + `_reindent`, mode in cache key); wired grid-direct launches into + `compute_wcsph_accel_continuity`, added hand-written + `_wcsph_dt_factors_grid_{f32,f64}` + a grid path in + `compute_wcsph_adaptive_timestep`, and rewired `_wc_sph_pec_continuity_step` + to build only the grid. Added `_grid_launch_args`. +- Tests: repurposed the cache-reuse test to assert zero flat-cache builds on the + continuity path; added grid-vs-flat fused parity (warp_sph) and grid + cache-distinct + single-cell numeric parity (codegen). Focused suite 50 passed. +- Ran the segmented million-particle profile (new `profile_grid_direct_neighbors.py`) + and the Warp-only adaptive `nx=100` guard. +- Ran a 6-dimension adversarial review workflow (review -> skeptic-verify per + finding). 1 confirmed nit: the adaptive-dt grid default had re-routed the + summation path out of scope. Addressed: defaulted + `compute_wcsph_adaptive_timestep` to `'flat'` and made the continuity step + pass `'grid'` explicitly; re-ran suite -> 50 passed. + +## Lightweight plans + +- None (Tier 2 plan + ADR-0004 covered the work). + +## Decisions reached + +- ADR-0004: grid-direct neighbor traversal for the WCSPH continuity hot path + (Accepted). + +## Code touched + +- `pysph/base/warp_codegen.py` +- `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_codegen.py` +- `pysph/base/tests/test_warp_sph.py` +- `.ai/.../experiments/2026-06-16_warp-elliptical-drop-runner/profile_grid_direct_neighbors.py` (new) + +## Experiments started / advanced / completed + +- Advanced `experiments/2026-06-16_warp-elliptical-drop-runner`: new summary + folder `million-cpu-gpu-grid-direct/`. Million-particle segmented profile shows + `build_neighbor_cache_gpu` called 0 times on the continuity path, step wall + 0.076-0.098 -> 0.059-0.064 s (~25-35%), grid build ~0.0005 s, equation + 0.011-0.014 -> 0.023-0.025 s/launch (absorbs the cutoff traversal), KE delta + vs flat fused -1.99e-06. Adaptive `nx=100` guard kept exactly 1393 steps at + fp32-scale deltas (Warp wall 7.82 s vs committed 23.63 s). + +## Handoff for next session + +- ADR-0004 grid-direct review (`reviews/2026-06-18_warp-grid-direct-neighbor-traversal.md`) + is pending @prabhu `LGTM`; commit only after sign-off. +- Next: ADR-0003 follow-up -- migrate the per-equation oracle helpers, the + summation-density path, and the now-duplicated grid/flat `_wcsph_dt_factors` + onto the generator and retire the hand kernels; evaluate moving the summation + path to grid-direct and narrowing `build_neighbor_cache_gpu` to the + host-query/oracle path; production resolved-drop results report; true + periodic-neighbor distance support. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1230.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1230.md new file mode 100644 index 000000000..e07048654 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1230.md @@ -0,0 +1,73 @@ +--- +type: session-log +date: 2026-06-18 +start: 12:30 +end: 13:30 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [warp-backend, gpu-nnps] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md, pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/base/tests/test_warp_sph.py] +--- + +# Session - 2026-06-18 12:30 + +## Goal of this session + +ADR-0003 follow-up (generator migration): make `warp_codegen` the single source +for every neighbor-loop kernel and retire the ~14 duplicated hand `@wp.kernel`s, +behavior-preserving. Also committed BUILD.md (`f2e9597d`) earlier in the day. + +## What happened + +- Wrote and got approval (`APPROVED`) for plan + `plans/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md`. +- Found the semantic crux: the standalone `_artificial_viscosity` hand kernel is + additive (`au = d_au[i]; au += ...`), composing onto the pressure gradient, + while generator blocks overwrite. Added `accumulate_outputs` to the generator + (seed `_acc_` from `d_[i]`). +- Added `SummationDensity` and `WcsphCflFactor` blocks (the CFL `dt_cfl` is a + free-form `wp.max` neighbor reduction; `dt_force` in `post_loop`). Added shared + `_run_equation_group` launcher (flat/grid, accumulate, cross-array). +- Repointed all 5 equation helpers + `compute_wcsph_adaptive_timestep` onto the + generator; refactored `compute_wcsph_accel_continuity` onto the launcher; + deleted the ~14 hand kernels (verified no dangling refs). Suite `51 passed`. +- Investigated a 102-184 s cold compile in the adaptive guard: root cause was the + generated kernel name using `len(_KERNEL_CACHE)` (order-dependent), busting + Warp's disk cache every session. Fixed to a deterministic md5 of the cache + key; verified the generated source is byte-identical cross-process; the warm + guard rerun loaded both generated modules cached (~20 ms). +- Adversarial-review workflow (6 dimensions, skeptic-verified): 1 confirmed + minor -- helpers still advertised `out_prop`/`out_props` but the generated + path writes canonical names. Added fail-fast guards + a test. Suite `52 passed`. + +## Lightweight plans + +- None (Tier 2 plan covered the work). + +## Decisions reached + +- No new ADR (executes ADR-0003's recorded follow-up). Folded in the + deterministic-kernel-name fix as a generator robustness improvement. + +## Code touched + +- `pysph/base/warp_codegen.py` (accumulate_outputs, deterministic name) +- `pysph/base/warp_sph.py` (2 blocks, `_run_equation_group`, repointed 6 + helpers, retired ~14 hand kernels, guards) +- `pysph/base/tests/test_warp_codegen.py`, `pysph/base/tests/test_warp_sph.py` + +## Experiments started / advanced / completed + +- Reused the elliptical-drop runner / resolved harness for validation: million + fixed-step profile (0 flat builds, steady floor `0.059 s`, KE identical) and + the adaptive `nx=100` guard (`1393` steps, KE rel `~8e-9`). No new summary + folder (perf-neutral consolidation; numbers logged in the review). + +## Handoff for next session + +- Generator-migration review + (`reviews/2026-06-18_warp-migrate-neighbor-kernels-onto-generator.md`) pending + @prabhu `LGTM`; commit only after sign-off. +- Remaining recommended steps: grid-direct + fuse the summation path and narrow + `build_neighbor_cache_gpu` to the host query API; production results report; + periodic-neighbor (minimum-image) distance support. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1400.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1400.md new file mode 100644 index 000000000..1b6d2aeb9 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1400.md @@ -0,0 +1,60 @@ +--- +type: session-log +date: 2026-06-18 +start: 14:00 +end: 14:30 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [warp-backend, gpu-nnps] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, pysph/base/warp_sph.py] +--- + +# Session - 2026-06-18 14:00 + +## Goal of this session + +Recommended step #1: complete "grid-direct everywhere" by extending grid-direct +to the summation Euler/KDK step paths (the continuity PEC path was already +grid-direct), so no device step path builds a flat CSR neighbor cache. + +## What happened + +- Plan approved (`APPROVED`): + `plans/2026-06-18_warp-grid-direct-summation-step-paths.md`. +- Added `neighbor_mode='flat'` (default) to the five standalone equation helpers, + threaded to `_run_equation_group`. `_compute_wcsph_acceleration`, + `wc_sph_euler_step`, and the summation branch of `wc_sph_leapfrog_step` now + pass `neighbor_mode='grid'` (including the adaptive dt and xsph calls). No + fusing -- the pressure-gradient(overwrite) -> viscosity(add) composition is + preserved. +- Added `test_warp_summation_step_paths_build_no_flat_neighbor_cache`. +- Focused suite `53 passed` (one-time cold compile of the new single-block grid + kernels, then cached); the summation Euler/KDK CPU-parity tests passed under + grid-direct with no tolerance changes. Continuity adaptive `nx=100` guard + unchanged (`1393` steps, KE 7797.7071). +- Adversarial-review workflow (4 dimensions, skeptic-verified): 0 findings. + +## Lightweight plans + +- None. + +## Decisions reached + +- No new ADR (extends ADR-0004 grid-direct to the remaining device step paths). + +## Code touched + +- `pysph/base/warp_sph.py`, `pysph/base/tests/test_warp_sph.py` + +## Experiments started / advanced / completed + +- Reused the resolved harness for the continuity guard sanity check (unchanged + path). No new experiment artifacts (behavior-preserving consolidation). + +## Handoff for next session + +- Grid-direct-summation review + (`reviews/2026-06-18_warp-grid-direct-summation-step-paths.md`) pending + @prabhu `LGTM`; commit only after sign-off. +- Remaining recommended steps: production resolved-drop results report; true + periodic-neighbor (minimum-image) distance support. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1500.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1500.md new file mode 100644 index 000000000..cbfd6abcc --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_1500.md @@ -0,0 +1,74 @@ +--- +type: session-log +date: 2026-06-18 +start: 15:00 +end: 16:00 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [gpu-nnps, warp-backend, validation-benchmarks] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, pysph/base/warp_codegen.py, pysph/base/warp_nnps.py, pysph/base/warp_sph.py] +--- + +# Session - 2026-06-18 15:00 + +## Goal of this session + +Recommended step (2): true periodic minimum-image neighbor distance for the +grid-direct path (wrapped cell walk + minimum image), enabling periodic +benchmarks. Then prepare step (1), the production results report, for running on +a capable GPU machine. + +## What happened + +- Clarified the purpose of periodic support (only periodic benchmarks need it; + elliptical drop is free-surface); user confirmed "Do (2) then prepare (1)". +- Plan approved. Implemented `periodic=True` in `warp_codegen` (wrapped cell + index + minimum-image dx; runtime per-dim flags; periodic in the cache key + only when set so non-periodic kernels stay byte-identical and disk-cached), + `UniformGridWarpNNPS.set_periodic_box` + tiled (cubic) bounds, and periodic + auto-detection in `_run_equation_group`. +- 5-dimension adversarial review (skeptic-verified) found 5 real issues. The + MAJOR one: the cell-id binning kernels `wp.clamp` instead of wrapping, so + out-of-box source positions were mis-binned and the wrapped walk missed + periodic neighbors -- a bug my interior-only tests didn't catch. Fixed by + wrapping the cell index in the binning kernels for periodic dims; added an + out-of-box (nx=4) regression test. Also fixed: box-too-small now raises + (was silently clamped), missing min/max raises a clear error, equal-length + check uses raw lengths. +- Validation: periodic summation density matches a CPU minimum-image reference + to 5.5e-6 (incl. out-of-box particle); periodic lattice density uniform (no + boundary deficiency); focused suite `57 passed`; non-periodic continuity guard + `1393` steps with 0 recompiles (disk cache intact). +- Prepared the results-report tooling: `generate_results_report.py` (orchestrates + the resolved nx=100 apples-to-apples + 1M/100-step comparisons and assembles a + Markdown report) and `RESULTS_REPORT_HOWTO.md` (run on a capable GPU machine). + +## Lightweight plans + +- None. + +## Decisions reached + +- No new ADR (extends ADR-0004 grid traversal; min-image over ghost particles, + recorded in the plan). MVP supports cubic (equal-length) periodic boxes. + +## Code touched + +- `pysph/base/warp_codegen.py`, `pysph/base/warp_nnps.py`, `pysph/base/warp_sph.py` +- `pysph/base/tests/test_warp_codegen.py`, `pysph/base/tests/test_warp_sph.py` +- `.ai/.../experiments/2026-06-16_warp-elliptical-drop-runner/generate_results_report.py` (new), + `RESULTS_REPORT_HOWTO.md` (new) + +## Experiments started / advanced / completed + +- Periodic validation via the focused suite + inline min-image reference. No new + benchmark summary folder (a feature slice, not a perf slice). + +## Handoff for next session + +- Periodic review (`reviews/2026-06-18_warp-periodic-minimum-image-distance.md`) + pending @prabhu `LGTM`; commit only after sign-off. +- Step (1) production results report: RUN `generate_results_report.py` on a + capable GPU machine (see `RESULTS_REPORT_HOWTO.md`). +- Possible follow-ups: non-cubic periodic boxes (per-dim cell sizes); narrow the + flat host-query path off `build_neighbor_cache_gpu`. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_2008.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_2008.md new file mode 100644 index 000000000..580a439e0 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-18_2008.md @@ -0,0 +1,90 @@ +--- +type: session-log +date: 2026-06-18 +start: 20:08 +end: 20:30 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [validation-benchmarks, gpu-nnps, warp-backend] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/README.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/plot_gpu_sweep.py, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/gpu-sweep/sweep-b300.json] +--- + +# Session - 2026-06-18 20:08 + +## Goal of this session + +Finish the $-cost-of-compute lens on the cross-GPU sweep, then (on the user's +prompt "is there another metric we should consider?") decide and add any missing +decision metric, and close out the cross-GPU artifact. + +## What happened + +- Completed the **$ cost-of-compute lens**: regenerated figures + (`cost_per_billion_1M.png` bar, `cost_per_billion_vs_particles.png` log-log), + added a cost table + both figures to `gpu-sweep/README.md` and `experiment.md` + (kept the performance figures -- cost is an *additional* lens, per user). + Committed `cc5759ee`, pushed to fork. At 1M: 5090/L40S `$0.0032` < RTX PRO 6000 + `$0.0060` < B300 `$0.0187` (cheap cards ~6x cheaper per unit work). +- Ran a **5-lens "missing metric" panel** (Workflow: HPC roofline, datacenter + TCO, energy/sustainability, computational physicist, GPU architect) + a + synthesis agent. Convergent winners: (1) **roofline / memory-bandwidth + utilization** -- the only metric that adjudicates our own "both Blackwell cards + plateau ~1.43e8, looks bandwidth-bound" claim; (2) **energy-to-solution / + perf-per-watt** -- the one that breaks the 5090=L40S dollar tie. Third: end-to- + end time-to-solution incl. cold-compile (recorded as a noted idea, not built). +- User chose to add BOTH as **estimates from existing data**, with the explicit + constraint "I won't be running anything again. So estimate it and report it in + the readme." -> the estimates are the final deliverable, no measured re-run. +- Added two estimate lenses to `plot_gpu_sweep.py` (+ helpers, constants, + docstring): `energy_per_gpstep_1M.png` (kJ/Gp-step from datasheet TDP) and + `mbu_at_1M.png` (analytic MBU = throughput x ~1.12 KB/p-step / peak_BW). Fixed + a `%%`->`%` xlabel typo and regenerated. Visually verified both figures. +- Wrote both lenses (figures + tables + caveats) into `gpu-sweep/README.md` and a + concise mirror in `experiment.md`; **refined the README Observations bullet**: + the ~1.43e8 ceiling is occupancy/launch/grid-build bound, NOT a memory-bandwidth + wall (every card <~12% of peak at 1M; B300 ~2% of 8 TB/s -> large headroom). +- Updated `current.md` (three decision lenses + refined finding, new timestamp, + next action) and the `2026-06-18` daily closeout (Evening update 2). + +## Key findings recorded + +- Energy @1M (kJ/Gp-step, TDP estimate): L40S 3.8 < RTX PRO 6000 4.9 < 4060 6.9 < + 5090 8.5 < B300 10.0. L40S uses ~2.2x fewer joules than the 5090 at equal $/work. +- Analytic MBU @1M: L40S ~11.9% > RTX PRO 6000 ~7.6% > 4060 ~7.0% > 5090 ~4.2% > + B300 ~2.0% of peak. Conclusion: not bandwidth-bound; occupancy/launch/grid-build + bound; the next perf win is occupancy/launch tuning, not faster memory. + +## Lightweight plans + +- None (analysis + reporting on an existing artifact; no new code path). + +## Decisions reached + +- No new ADR. Both new lenses are estimate-only and explicitly model-based (TDP + overstates non-FLOP-bound power; B_eff is an upper bound ignoring L2 reuse and + is a whole-step number) -- reported with caveats, not validated against measured + power or profiler counters, and none planned. + +## Code touched + +- `.ai/.../gpu-sweep/plot_gpu_sweep.py` (cost + energy + roofline functions, + constants, docstring), `gpu-sweep/README.md`, `experiment.md`, `current.md`, + `updates/daily/2026-06-18.md`. New figures: `cost_per_billion_1M.png`, + `cost_per_billion_vs_particles.png`, `energy_per_gpstep_1M.png`, `mbu_at_1M.png`. +- No solver/source code touched. + +## Experiments started / advanced / completed + +- `experiments/2026-06-16_warp-elliptical-drop-runner` cross-GPU artifact + completed: performance + $-cost + energy + roofline lenses, four sweeps + 4060 + anchor. + +## Handoff for next session + +- Cross-GPU artifact is complete and committed/pushed. Production results report + (`generate_results_report.py`) still committed-ready to RUN on a capable GPU. +- Open optimization lead from the roofline finding: the Blackwell plateau is + occupancy/launch/grid-build bound (not memory-bandwidth) -- profile grid-build + vs equation time and tune occupancy/launch config; the super-linear knee is + most likely a grid-build / cache-residency effect. +- Follow-ups unchanged: non-cubic periodic boxes; narrow the flat host-query path. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_0935.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_0935.md new file mode 100644 index 000000000..f0329b322 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_0935.md @@ -0,0 +1,166 @@ +--- +type: session-log +date: 2026-06-19 +start: 09:35 +end: 10:35 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [warp-backend, validation-benchmarks, gpu-nnps] +memory_consulted: [.ai/implementations/blast-from-the-past/plans/2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md, .ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner/, pysph/examples/dam_break/dam_break_3d_lobovsky.py, pysph/examples/_db_geometry.py] +--- + +# Session - 2026-06-19 09:35 + +## Goal of this session + +Implement the 3D dam-break (Lobovsky no-obstacle) benchmark per the approved plan +(`2026-06-18_warp-3d-dam-break-lobovsky-benchmark.md`, ADR-0005). The backend +physics (steps 1-4: Wendland id, gravity, multi-array stepping, Tait-HG walls + +dim=3 tests) were already in the uncommitted working tree; this session built and +validated **plan step 5 -- the runner experiment packet** -- and closed out the +recording. + +## What happened + +- **Confirmed the backend (steps 1-4) on GPU.** A targeted subset + (`test_warp_wendland_summation_density_..._in_3d`, `apply_body_force` x2, + `tait_eos_hg_correction`, `dam_break_step_two_array_3d`) passed `5 passed` in + 259 s. (The full focused suite cold-compiles ~20 min now -- adding Wendland to + the shared kernel-id router inflated *every* generated kernel's PTX to 10-13 MB, + ballooning nvrtc time; in-process nvrtc, so no child compiler. Deferred the full + suite to the end with a warm on-disk cache.) +- **Built the experiment packet** `experiments/2026-06-18_warp-dam-break-3d-runner/`: + `dam_break_3d_runner.py` (Runner; IC via `DamBreak3DGeometry` no-obstacle -> + warp fluid+wall; `UniformGridWarpNNPS(dim=3)`; `wc_sph_dam_break_step`; adaptive + dt + `n_damp` timestep damping; 3D metrics incl. surge-front x, max height, + z-extent, 3D KE), `run_correctness.sh` smoke wrapper, tier-1 + `compare_warp_pysph_dam_break_3d.py` (hand-rolled CPU EPEC + `LinkedListNNPS(dim=3)` + + `WendlandQuintic`, mirroring the step block-for-block), tier-2 + `resolved_dam_break_3d_comparison.py` (subprocess the real + `dam_break_3d_lobovsky.py` Application via `run(argv=...)`, load the t~0 dump as + a shared IC, step Warp to matched checkpoints). +- **All three tiers pass** (see Key findings). Tier-1 needed a principled verdict + change: near-rest pressure is at the fp32 Tait-EOS cancellation floor, so + pressure is gated on an *absolute* tolerance (`rho0*c0^2*2^-23`), kinematics + + density on a relative one. +- **Ran an adversarial review workflow** (5 dimensions x verify) on the packet: + 24 confirmed findings. The synthesis flagged a "blocker" (Wendland leaves + missing from `_WARP_DEVICE_FUNCS`) -- **refuted by reality**: every Wendland run + already passed (the routers resolve leaves via their module `__globals__`, not + the dict). Added the leaves anyway for parity/robustness (not in any cache key). + Acted on the real findings: byte-identity regression guard, tier-2 summary JSON + + `--prefix`, dead wall-velocity pull, ADR wording. +- **Added the cache-stability regression guard** + (`test_2d_path_generated_source_is_byte_identical_to_golden`): pins the md5 of + the cubic/gaussian 2D-path generated source (SummationDensity + WCSPH-continuity, + flat + grid) and asserts `wendland` never appears. Passes. +- Updated ADR-0005 (status -> Accepted; corrected the "on-disk cache byte- + identical" wording; added a Validation section), graph/index, experiment.md, the + aspect contexts, current.md, and the daily. + +## Key findings recorded + +- **Tier-1 (hand-rolled CPU EPEC parity), dx=0.15, 3 steps, fixed dt:** fluid + x/y/z/u/v/w + fluid/wall rho match Warp (fp32) to ~1e-8 relative; pressure abs + diff 0.17 Pa vs the fp32 Tait floor 2.06 Pa -> PASS. +- **Tier-2 (real PySPH `dam_break_3d_lobovsky` Application), dx=0.12, tf=0.05:** at + t=0.05 (developed) KE 32.684 (warp) vs 32.678 (cpu), surge-front/max-height/rho + agree to fp32; p_max 13843 vs 13691 = **1.11%** relative. Near rest (t=0.0017) + per-particle x/z match to ~1e-7; pressure ~13% relative (fp32 cancellation floor) + -> recovers to ~1% as the flow develops. Confirms the tier-1 pressure analysis. +- **EPEC resolved:** `wc_sph_dam_break_step` is E-P-E-C, matching the reference + `EPECIntegrator` -- no `PECIntegrator` substitution needed. +- **`n_damp` damps the *timestep*** (`0.5*(sin(pi*(-0.5+(count+1)/n_damp))+1)`), + not gravity; the runner applies it to dt with full gravity (PySPH-faithful). +- **`c0` = `10*sqrt(2*9.81*0.55)` ~ 32.85** (reference scheme constant; the + `get_max_speed` 44.29 form is only the initial-dt cap). +- **Cache invariant (precise):** generated 2D-path source byte-identical + (md5-pinned); cubic/gaussian numeric paths unchanged; the shared router gained an + additive `id==2` branch, so a one-time logic-preserving recompile can occur + (does not change results). ADR wording corrected accordingly. +- **Build cost note:** Wendland in the router enlarged all generated kernels (PTX + 10-13 MB), so cold compilation is now multi-minute per large 3D fused kernel. + +## Lightweight plans + +- None beyond the approved plan; step 5 executed as specified (with the documented + n_damp/gravity-ramp and EPEC clarifications). + +## Decisions reached + +- ADR-0005 moved Proposed -> Accepted after implementation + 3-tier validation. +- Tier-1 pressure verdict uses an absolute fp32-EOS-floor tolerance (documented), + not relative. +- Wendland device leaves added to `_WARP_DEVICE_FUNCS` for consistency (refuting, + with evidence, the review's "blocker"; the dict is not part of any cache key). + +## Code touched + +- `pysph/base/warp_sph.py` (added Wendland leaves to `_WARP_DEVICE_FUNCS` -- no + cache-key/source impact). +- `pysph/base/tests/test_warp_codegen.py` (byte-identity regression guard). +- New packet `experiments/2026-06-18_warp-dam-break-3d-runner/`: + `dam_break_3d_runner.py`, `run_correctness.sh`, `compare_warp_pysph_dam_break_3d.py`, + `resolved_dam_break_3d_comparison.py`, `perf_and_snapshot_dam_break_3d.py`, + `experiment.md`, `results-smoke.npz`, `comparison-tier1-summary.json` (+ warp npz), + `comparison-resolved-summary.json`, `cpu-vs-warp-perf.json`, `cpu-vs-warp-snapshot.png`. + Review images live in `reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/`. +- **Perf + snapshot (added in follow-up):** at `dx=0.08` / 7,392 particles to + `tf=0.4`, Warp fp32 ~1.53x over single-threaded PySPH CPU fp64 (8.5 vs 13.0 + ms/step), matched 460/461 steps. Required fixing the runner `dt_max`: the + reference's `0.25*h0/(1.1*c_max)` is only the *seed* dt (PySPH's + `Integrator.compute_time_step` returns CFL-limited `cfl*dt_min`, no clamp to the + seed), so capping Warp's adaptive dt at the seed forced ~1.7x more steps (775 vs + 461); `dt_max` now defaults to uncapped (CFL + n_damp govern). The x-z snapshot + shows the two runs visually indistinguishable. +- **1M perf + 3D snapshot + fusion (follow-up):** `bench_1M_and_3d_snapshot.py` + measures per-step throughput at ~1M (a developed 1M CPU run to a physical tf is + multi-hour, so fixed-step throughput, as in the 1M elliptical comparison) and + renders a 4-view 3D-explicit snapshot. At 1,014,072 particles: Warp fp32 ~14x + per-step over single-thread PySPH CPU fp64. Then **fused the dam-break fluid + step** (`_WCSPH_DAM_BREAK_FLUID_BLOCKS` = pressure + AV + continuity, one kernel + per source via accumulate_outputs) -> Warp step 1.23x faster (0.415 -> 0.337 + s/step, 3.01M particle-steps/s). Re-validated: dam-break unit test + tier-1 + parity pass (identical diffs -> fused == single-block to fp32); byte-identity + guard still passes (2D path untouched). Review images in + `reviews/2026-06-19_warp-3d-dam-break-lobovsky_assets/` (cpu-vs-warp + 4-view 3D). +- ADR-0005 (wording + status + Validation), `decisions/graph.md`, + `decisions/index.json`, aspect contexts, `current.md`, `updates/daily/2026-06-19.md`. + +## Experiments started / advanced / completed + +- `experiments/2026-06-18_warp-dam-break-3d-runner` created and validated + (tier-0 smoke, tier-1 hand-rolled CPU, tier-2 real PySPH Application). + +## Handoff for next session + +- Lobovsky no-obstacle correctness is established (3 tiers). Awaiting @prabhu + sign-off on the review (`reviews/2026-06-19_warp-3d-dam-break-lobovsky.md`); not + yet committed. +- Follow-ups (ADR-0005): SPHERIC/Kleefsman obstacle case (third array); the full + `tf=2.5` run + probe-point pressure `p/(rho g H)` vs + `db_exp_data.get_lobovsky_data()` (needs an SPH interpolator + multi-second run); + optional cubic/gaussian dam-break cross-check. + +## Closeout (cross-GPU sweep + PR #435) + +- **Committed + pushed.** Dam-break work landed in 3 commits (`81d618bc` impl + + fused step + recording; `0d8bafae` sweep script; `394b5b0e` sweep results + + figures) on `blast-from-the-past`, pushed to `fork`. (Supersedes the earlier + "not yet committed" handoff above.) +- **Cross-GPU sweep** (`gpu_perf_sweep_dam_break.py` + `gpu-sweep/`): vectorized + numpy-mask IC (bypasses the geometry's per-point Python loop), fixed-dt fused + step, 32k-9.5M particles. Run on **L40S** and **RTX PRO 6000 Blackwell** (both + cleared the full list, no OOM) + an RTX 4060 anchor; 5090/B300 deferred (busy). + At ~1M: RTX PRO 6000 39.1 Mp-st/s (163x vs 1 CPU core) > L40S 25.9 (108x) > 4060 + 4.5 (19x); L40S cheapest/most-efficient ($0.0114/billion, 13.5 kJ/billion). + Lenses + 6 figures in `gpu-sweep/README.md` (re-run `plot_gpu_sweep_dam_break.py` + to fold in 5090/B300 later). Methodology note: the sweep is fixed-dt (pure + compute, like the elliptical sweep); production adaptive dt is ~1.5x slower/step. +- **PR #435** (pypr/pysph, "Add a Warp GPU backend for PySPH WCSPH"): pushing the + branch put all dam-break commits + figures into the PR diff. The PR + description/comment could NOT be edited via the Claude GitHub MCP (403 "not + accessible by integration" -- the integration has no write access to the + upstream pypr repo). Updated the description manually with the author's own + token (`git credential fill` -> GitHub REST API PATCH); body draft saved at + `/tmp/pr435_body.md`. Review still pending @prabhu sign-off. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_2230.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_2230.md new file mode 100644 index 000000000..bf3475363 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_2230.md @@ -0,0 +1,67 @@ +--- +type: session-log +date: 2026-06-19 +start: 21:00 +end: 23:00 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [warp-backend, validation-benchmarks, host-integration] +memory_consulted: [pysph/sph/rigid_body.py, pysph/examples/rigid_body/dam_break3D_sph.py, pysph/examples/rigid_body/solid_body_floating_in_tank.py, pysph/base/warp_sph.py, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md, .ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/] +--- + +# Session - 2026-06-19 21:00 + +## Goal of this session + +User asked for a "more compelling" dam-break follow-up -- floating bodies -- with +a **nice photorealistic animation** as the priority, on the **Warp GPU** backend. +Scope this session: pick the case + render stack, then de-risk and start the GPU +implementation. + +## What happened + +- **Design pass (multi-agent workflow):** deep-read `rigid_body.py`, the rigid + examples, and the `wc_sph_dam_break_step` driver; designed an additive + floating-body plan + adversarial critiques (cache-stability, Warp feasibility, + physics/validation). Verdict: feasible; the one new GPU primitive is an + `atomic_add` force/torque reduction; 6-DOF runs on the host. +- **Render-stack research (multi-agent workflow, web):** the user wanted + photorealistic rendering and chose Omniverse, but the verified research showed + Omniverse is the **weakest fit** for this 8 GB / WSL2 / Warp setup (native + surfacing only meshes PhysX particles, not a Warp cloud; runs Windows-side; + headless cloud batch broken). Surfaced this honestly; user switched to the + recommended **Blender Cycles Tier A** (`splashsurf -> Cycles/OptiX -> cloud + finals -> ffmpeg`). +- **Gate checks:** `pysplashsurf` installed + functional (`reconstruction_pipeline`, + numpy-native). The **CPU rigid-body reference crashes on Python 3.14** (compyle + `ast.Str`); the Warp path is unaffected -> validate vs a numpy reimplementation. +- **P0 kill-test** (scratch -> experiment packet): the `atomic_add` reduction + reproduces `RigidBodyMoments` (2.7e-15), host finalize matches numpy (6e-14), + transform exact; **fp32 atomic_add non-deterministic (3e-6) -> use f64 + accumulators** (spread ~1e-15). +- **P1** in `warp_sph.py` (additive): `_rigid_moments_reduce_f32/f64` + + `_rigid_finalize_moments` + `compute_rigid_body_moments`; tests pass (f32+f64 + vs numpy ref; determinism); the 2D-source golden guard still passes. + +## Results + +- Representative regression subset + rigid tests + cache guard: **10 passed in 4 s** + (the full GPU suite needs >15 min on the 4060, so it was time-boxed; the subset + covers every existing path the change touches). +- ADR-0006 written; experiment packet `2026-06-19-warp-floating-body-rigid/` + created (P0 kill-test + experiment.md); decision graph/index regenerated; + `validate-memory` PASS. + +## Findings / decisions + +- Case: **3D dam-break surge tossing a floating box** (drama-first; mirrors + `dam_break3D_sph.py`). Render: **Blender Cycles**, Omniverse rejected. +- **f64 accumulators** for the rigid reduction (fp32 atomic_add non-associative). +- Host 6-DOF (reuse PySPH math); body excluded from the PEC stage; coupling must + be the real Liu force (P3), validated on the first-plunge transient. + +## Next + +P2: host 6-DOF integrate + device rigid-transform per step. Then P3 coupling + +sibling driver, P4 EPEC fidelity, assemble + render. Optional: compyle upgrade +for tier-2 CPU parity. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_0604.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_0604.md new file mode 100644 index 000000000..fd59f2f87 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_0604.md @@ -0,0 +1,38 @@ +--- +type: session-log +date: 2026-06-20 +start: 06:04 +end: 06:49 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/experiments/*/experiment.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-19.md, .ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-19_2230.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md, .ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md, .ai/implementations/blast-from-the-past/plans/2026-06-20_warp-device-resident-rigid-body-p2.md, .ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md] +--- + +# Session - 2026-06-20 06:04 + +## Goal + +Orient the new week and identify the next concrete implementation slice. + +## Result + +- Rechecked all experiments listed by `current.md` against recorded artifacts and process state. Foundational experiments, elliptical drop, and 3D dam-break are complete; no run is live. Floating-body ADR-0006 remains active. +- Boot sequence and latest daily/weekly handoffs agree: the next slice is P2, host-side RK2 6-DOF state integration plus an additive device rigid-transform. +- P2 should first land as a standalone tested primitive, before fluid/body coupling: preserve initial COM/linear/angular state, advance midpoint and full-step state from `ac`/`omega_dot`, set particle velocity as `vc + omega x (x-cm)`, and advance particle positions on device. +- This will be persistent backend/test work and therefore needs a signed-off implementation plan before code. +- User approved the revised GPU-resident direction verbatim (`approved`). A + Tier 2 plan was then written; repository rules require approval after the + written plan is posted; the user then approved it verbatim (`approved`). +- Implemented P2 as persistent device state + GPU finalize/3x3 solve + GPU RK2 + midpoint/full updates + GPU rigid motion. Production stages have no host + finalize, pull, `.numpy()`, or explicit synchronization. +- Focused P1/P2 + 2D cache guard: `10 passed`. Final full Warp SPH regression: + `49 passed` in 12.84 s warm; the preceding cold run passed its 48 then- + collected tests in 1825.06 s. `validate-memory.py` and `git diff --check` pass. + +## Next + +Obtain @prabhu `LGTM` on +`reviews/2026-06-20_warp-device-resident-rigid-body-p2.md`, then commit. P3 is +the next implementation slice. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1350.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1350.md new file mode 100644 index 000000000..282d5af15 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1350.md @@ -0,0 +1,41 @@ +--- +type: session-log +date: 2026-06-20 +start: 13:50 +end: 18:58 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, validation-benchmarks] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/experiments/2026-06-19_warp-floating-body-rigid/experiment.md, .ai/implementations/blast-from-the-past/experiments/2026-06-18_warp-dam-break-3d-runner/experiment.md, .ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky.md, .ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md] +--- + +# Session - 2026-06-20 13:50 + +## Goal + +Run a concrete case with the new device-resident P2 implementation. + +## Result + +Ran a 315-particle asymmetric 3D box with prescribed net force and torque for +2,000 two-stage RK2 steps (`dt=1e-4`, `t=0.2`) on the RTX 4060. The loop remained +device-resident until one final checkpoint pull: 0.545 s, error flag 0, finite, +COM velocity error 1.6e-9, relative pair-distance drift 9.4e-7. This validates +P2 repeated dynamics; the floating-in-water case remains blocked on P3 Liu +coupling. + +Then ran a substantial existing-backend showcase: `dx=0.025`, 125,687 total +particles, 13.61x per-step vs one CPU core, and 2,837 adaptive GPU steps to +`t=0.8016`, all finite. Captured a four-view verification figure and reran the +collapse phase (`t=0.3996`) for a PyVista hero frame coloured by particle speed. +Splashsurf reconstructed the surface successfully; Blender/ffmpeg are absent, +so the retained hero is an honest scientific particle render rather than a +photorealistic claim. +- Copied both showcase images into the 3D dam-break review asset directory and + embedded them in that review. The P2 review cross-links the showcase while + explicitly stating that it is fluid-only evidence, not P3 coupling evidence. + +## Next + +Obtain @prabhu `LGTM` for the P2 review, then implement P3 coupling and run the +first genuinely fluid-coupled floating-box case. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1908.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1908.md new file mode 100644 index 000000000..567fd822a --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1908.md @@ -0,0 +1,47 @@ +--- +type: session-log +date: 2026-06-20 +start: 19:08 +end: 01:58 (2026-06-21) +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-20.md, .ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/decisions/2026-06-15_adr-0001_adopt-ai-memory-system-for-blast-from-the-past.md, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md, .ai/implementations/blast-from-the-past/decisions/2026-06-19_adr-0006_adopt-additive-rigid-body-coupling-for-the-warp-floating-body-benchmark.md, .ai/implementations/blast-from-the-past/plans/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md, .ai/implementations/blast-from-the-past/reviews/2026-06-20_warp-device-resident-rigid-body-p2.md, pysph/sph/rigid_body.py, pysph/base/warp_codegen.py, pysph/base/warp_sph.py, pysph/examples/rigid_body/dam_break3D_sph.py] +--- + +# Session - 2026-06-20 19:08 + +## Goal + +Commit P2 locally and move to P3. + +## Result + +- Commit is correctly blocked: the P2 review still lacks @prabhu's exact + `LGTM`, required by the operating contract. User approval cannot substitute + for the designated reviewer verdict. +- Audited PySPH's Liu/Akinci/NumberDensity equations and the current fixed-wall + driver. Refined P3 into deterministic two-pass Liu coupling (fluid accel and + reversed body reaction) to avoid the fp32 source-atomic nondeterminism already + rejected by P0. +- Wrote the Tier 2 P3 plan. No P3 implementation started. +- User supplied the exact combined gate message: + `take this as @prabhu: LGTM for P2. approved for P3`. Recorded it verbatim in + the P2 review and P3 plan. +- Committed reviewed P2 locally as `bb3843f7`; nothing was pushed. +- Implemented P3's additive deterministic two-pass Liu coupling, static rigid + number density, GPU rigid density/body-force staging, and sibling coupled + EPEC driver. Existing fixed-wall and generated 2D paths remain unchanged. +- Added the coupled floating-box runner and ran a 7,458-particle, 241-step + first-plunge transient to `t=0.200603`; finite/error-free, computed force + moved and rotated the body, relative geometry drift `1.90e-6`. +- Produced and embedded the actual coupled-run hero image in the P3 review. +- Final exact-tree Warp SPH suite: `54 passed, 2 warnings in 1886.59s`. +- Prepared P3 review + `reviews/2026-06-20_warp-liu-fluid-rigid-coupling-p3.md`. +- User supplied the exact reviewer verdict + `alright. @prabhu: LGTM and closeout`; recorded verbatim in the review. + +## Next + +P3 is approved and closed. Plan P4 contact/long-horizon/render work next. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-21_0206.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-21_0206.md new file mode 100644 index 000000000..9fe6e8dda --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-06-21_0206.md @@ -0,0 +1,32 @@ +--- +type: session-log +date: 2026-06-21 +start: 02:04 +end: 02:07 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [validation-benchmarks, host-integration] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/reviews/2026-06-19_warp-3d-dam-break-lobovsky.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-20.md, .ai/implementations/blast-from-the-past/updates/session-logs/2026-06-20_1350.md] +--- + +# Session - 2026-06-21 02:06 + +## Goal + +Explain and version every file left after the narrowly scoped P3 commit. + +## Result + +- Audited all remaining changes: experiment completion metadata, developed + dam-break metrics/images, prior memory closeouts, a duplicate coupled hero, + and the owner-provided root codebase report. +- The owner requested that all be committed. Added a truthful boundary entry + for `CODEBASE_UNDERSTANDING.md` and assembled a no-code review package. +- Received exact reviewer verdict `@prabhu: LGTM` and recorded it verbatim in + both the package review and the existing dam-break review. +- Committed the approved 22-file package locally; no push. The worktree is + clean. + +## Next + +Begin P4 planning from a clean worktree. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_0957.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_0957.md new file mode 100644 index 000000000..2672f83c8 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_0957.md @@ -0,0 +1,54 @@ +--- +type: session-log +date: 2026-07-06 +start: 09:57 +end: 10:39 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks, host-integration] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/conventions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/aspects/host-integration/context.md, .ai/implementations/blast-from-the-past/aspects/host-integration/open-questions.md, .ai/implementations/blast-from-the-past/aspects/host-integration/known-issues.md, .ai/implementations/blast-from-the-past/decisions/index.json, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md, .ai/implementations/blast-from-the-past/updates/daily/2026-06-21.md, .ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md, .ai/implementations/blast-from-the-past/templates/plan-template.md, .ai/implementations/blast-from-the-past/prompts/planning-prompt.md, .ai/implementations/blast-from-the-past/skills/working-with-host-code.md, .ai/implementations/blast-from-the-past/skills/testing.md, .ai/implementations/blast-from-the-past/skills/long-running-tasks.md, .ai/implementations/blast-from-the-past/skills/coding-style.md] +--- + +# Session - 2026-07-06 09:57 + +## Goal + +Assess and plan dynamic adaptive particle resolution for genuinely large-scale +Warp WCSPH problems, beginning with the fixed-obstacle 3D dam-break. + +## Result + +- Confirmed that fixed obstacles fit the existing multi-solid driver, while + runtime APR requires new multilevel neighbor search, GPU particle lifecycle, + conservative split/merge, variable-resolution physics, and validation. +- Identified two scale blockers in the current implementation: the uniform grid + is sized from global `hmax`, and Warp helper add/remove operations use host + NumPy transfers. +- Consulted primary APR literature and the open PySPH adaptive implementation. +- Created Tier-2 plan + `plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md` with five + reviewed checkpoints, hard correctness/scale gates, and explicit exclusions + for local timestepping and multi-GPU work. +- Received and recorded the owner's exact approval quote, `approved`; plan + status is now `approved`. +- Prepared review `reviews/2026-07-06_warp-dynamic-adaptive-particle-resolution-plan.md` + for the required pre-commit `@prabhu` verdict. +- The owner requested a prototype exception and approved the posted Tier-1 + policy plan verbatim with `approved`. Updated Rule 4, the review template, + and validator so narrowly scoped in-boundary Warp prototypes may use quoted + owner authorization; promotion/upstream work still requires `@prabhu: LGTM`. +- Applied the prototype-owner route to the current APR plan package. + +## Code touched + +No host code changed. Added only this session log and the draft plan. + +## Experiments + +No numerical experiment started. P0 begins only after exact user approval. + +## Handoff + +Commit the approved prototype-governance/plan package with the required +`prototype:` subject, then begin APR P0. A cumulative `@prabhu: LGTM` remains +required before promotion or upstream publication. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1040.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1040.md new file mode 100644 index 000000000..4ecd18238 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1040.md @@ -0,0 +1,54 @@ +--- +type: session-log +date: 2026-07-06 +start: 10:40 +end: 11:01 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [warp-backend, gpu-nnps, particle-memory, validation-benchmarks] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md, .ai/implementations/blast-from-the-past/references/index.md, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0005_adopt-additive-3d-wcsph-physics-for-the-warp-dam-break-benchmark.md, .ai/implementations/blast-from-the-past/templates/reference-note-template.md, .ai/implementations/blast-from-the-past/templates/experiment-template.md, .ai/implementations/blast-from-the-past/skills/working-with-host-code.md, .ai/implementations/blast-from-the-past/skills/testing.md, .ai/implementations/blast-from-the-past/skills/long-running-tasks.md, .ai/implementations/blast-from-the-past/skills/coding-style.md] +--- + +# Session - 2026-07-06 10:40 + +## Goal + +Begin APR P0: audit primary references and prove the existing multi-solid Warp +driver can run the fixed Kleefsman obstacle. + +## Result + +- Audited the open Muta--Ramachandran source. Its workflow is highly relevant, + but automatic background resolution is explicitly 2D, several equations have + 3D FIXMEs, and its GPU particle lifecycle is unimplemented. +- Added assessed reference notes for Muta--Ramachandran, Vacondio 3D APR, and + Yang GPU APR. +- Opened the P0 experiment and extended the existing runner with an optional + third fixed obstacle array without changing backend code. +- Ran 1-, 20-, and 250-step obstacle cases on the RTX 4060. The 250-step run + reached `t=0.258454926`, remained finite, developed `150.147 kPa` maximum + obstacle pressure as the front reached `x=2.4901464`, and preserved obstacle + device coordinates exactly. +- Re-ran the original no-obstacle one-step case; it remains finite. +- Ingested the full Vacondio post-print constants and added a NumPy-only + constrained density-error kill test. The calculation is grid-converged and + confirms icosahedron-plus-center beats cubic-plus-center, but its PySPH + Wendland error/mass ratio does not reproduce Table 1; ADR-0007 remains + blocked pending convention reconciliation. +- Posted the prototype review and received exact owner commit authorization: + `commit this prototype checkpoint. list out next steps`. + +## Code touched + +Experiment runner and implementation memory only; no host PySPH source changed. + +## Experiments + +P0 experiment remains active. Fixed-obstacle wiring is proven; exact 3D split +stencil reproduction is deliberately failed on the published numeric oracle, +and uniform coarse/fine baselines remain. + +## Handoff + +Resolve the stencil/kernel convention discrepancy, then add probe/impulse +metrics and uniform coarse/fine baselines. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1118.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1118.md new file mode 100644 index 000000000..fc1012767 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-06_1118.md @@ -0,0 +1,50 @@ +--- +type: session-log +date: 2026-07-06 +start: 11:18 +end: 11:25 +user: @kunalpuri-prediqt +agent: codex +aspects_touched: [gpu-nnps, warp-backend, particle-memory, validation-benchmarks] +memory_consulted: [.ai/AGENTS.md, .ai/implementations/blast-from-the-past/AGENTS.md, .ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/implementation.md, .ai/implementations/blast-from-the-past/host-project-notes.md, .ai/implementations/blast-from-the-past/plans/2026-07-06_warp-dynamic-adaptive-particle-resolution.md, .ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/context.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/open-questions.md, .ai/implementations/blast-from-the-past/aspects/gpu-nnps/known-issues.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/context.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/open-questions.md, .ai/implementations/blast-from-the-past/aspects/warp-backend/known-issues.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/context.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/open-questions.md, .ai/implementations/blast-from-the-past/aspects/particle-memory/known-issues.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/context.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/open-questions.md, .ai/implementations/blast-from-the-past/aspects/validation-benchmarks/known-issues.md, .ai/implementations/blast-from-the-past/updates/daily/2026-07-06.md] +--- + +# Session - 2026-07-06 11:18 + +## Goal + +Create the detailed implementation plan for the first runtime GPU APR step. + +## Result + +- Re-audited the current uniform-grid device layout, generated grid signature, + launch routing, variable-`h` tests, and active experiment outputs. +- Drafted Tier-2 plan + `plans/2026-07-06_warp-multilevel-gpu-nnps.md` for a configurable, + device-built multilevel NNPS with exact symmetric cross-level traversal. +- Bounded the checkpoint to neighbor search, generated-group consumption, and + adaptive timestep. Split/merge allocation, periodic multilevel traversal, + APR physics, local timesteps, and multi-GPU work remain excluded. +- Refined the decision graph intent: ADR-0007 should decide the multilevel NNPS + only after exact-set/candidate kill tests; later APR layers receive separate + ADRs. +- Inspected both active experiments. Their recorded outputs remain present and + statuses remain accurate; refreshed `last_checked` without starting a run. + +## Code touched + +No host code changed. Added the draft plan and planning/experiment-memory +updates only. + +## Experiments + +No numerical run started. APR P0 and floating-body P4 remain active as recorded. + +## Handoff + +The owner explicitly authorized a local plan-checkpoint commit with the exact +message `commit locally please.` This authorization is recorded in +`reviews/2026-07-06_warp-multilevel-gpu-nnps-plan.md`; it does not match a +Rule-2 Tier-2 implementation verdict, so the plan remains draft. Wait for +`APPROVED`, `APPROVED WITH EDITS: ...`, or `REJECTED: ...` before beginning +CPU/brute fixtures or multilevel GPU traversal. diff --git a/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-07_0900.md b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-07_0900.md new file mode 100644 index 000000000..ddbc36408 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/session-logs/2026-07-07_0900.md @@ -0,0 +1,84 @@ +--- +type: session-log +date: 2026-07-07 +start: 09:00 +end: 09:00 +user: @kunalpuri-prediqt +agent: claude +aspects_touched: [gpu-nnps, warp-backend, particle-memory, validation-benchmarks] +memory_consulted: [.ai/implementations/blast-from-the-past/current.md, .ai/implementations/blast-from-the-past/plans/2026-07-06_warp-multilevel-gpu-nnps.md, .ai/implementations/blast-from-the-past/reviews/2026-07-06_warp-multilevel-gpu-nnps-plan.md, .ai/implementations/blast-from-the-past/decisions/2026-06-17_adr-0003_adopt-dynamic-warp-equation-group-code-generation.md, .ai/implementations/blast-from-the-past/decisions/2026-06-18_adr-0004_grid-direct-neighbor-traversal-for-wcsph-continuity-hot-path.md] +--- + +# Session - 2026-07-07 09:00 + +## Goal of this session + +Review and then implement the multilevel GPU NNPS plan +(`plans/2026-07-06_warp-multilevel-gpu-nnps.md`): steps 1-3 (CPU-oracle +contract, `MultilevelGridWarpNNPS`, generated multilevel equation loop) plus +ADR-0007. + +## What happened + +- Reviewed the plan, then folded three review findings into it (fp32 per-level + boundary fixture, per-(dst-level,src-level) candidate breakdown, and marking + the O(nlevels) readback as a prototype-only allowance) -- commit `bbcb6d5e`. +- Step 1 (CPU-oracle contract) TDD: `assign_particle_levels` (half-open range + binning, precision-consistent edges, fail-loud on out-of-range h and invalid + params, conservative support), `brute_force_neighbor_sets` (pure-numpy exact + symmetric-cutoff oracle), `accepted_level_pair_counts`. A verification + workflow adversarially confirmed the oracle over 24,964 comparisons vs + LinkedListNNPS (fp64) and BruteForceWarpNNPS (fp32), found + fixed two + input-validation bugs, and specified the fixture set. Commit `4107249b`. +- Step 2 (`MultilevelGridWarpNNPS`, new module `pysph/base/warp_multilevel_nnps.py`): + flattened per-level global cell list + variable-stencil cross-level + traversal; device-resident construction (GPU assign + count/max-h/AABB + reductions, only O(nlevels) scalar readback); eight kill-gate fixtures and a + clustered ~9x candidate-reduction gate. Commits `4107249b`, `4fc39880`, + `711c0356`. +- ADR-0007 (Proposed) recorded and the decision graph regenerated -- commit + `64e1c6da`. +- Step 3 (generated multilevel equation loop): `neighbor_mode='multilevel'` in + `warp_codegen` (flat/grid source byte-identical), `_multilevel_grid_launch_args` + launcher, `_run_equation_group` routing, periodic-multilevel guard. + Multilevel-mode summation density matches the uniform grid in 2D and 3D. + Commit `8861d331`. + +## Lightweight plans + +Remaining under the same plan: adaptive-timestep + fused +continuity/pressure/viscosity multilevel parity, fp64 exercise, and moving +ADR-0007 to Accepted after the dense-vs-sparse memory check. + +## Decisions reached + +- ADR-0007 (Proposed): device-built multilevel cell-list NNPS -- level + representation, device-metadata contract, cross-level traversal, permitted + O(nlevels) scalar readback, module isolation, periodic deferral. +- Multilevel kernels MUST live in their own module: co-locating them in + `warp_nnps` inflates the WCSPH JIT footprint. The combined single-process + Warp test suite hangs pre-existingly on the WSL2 PTX-JIT; run the files + separately. + +## Code touched + +- New: `pysph/base/warp_multilevel_nnps.py`. +- `pysph/base/warp_nnps.py`: pure-Python oracle/level helpers. +- `pysph/base/warp_codegen.py`: additive `neighbor_mode='multilevel'`. +- `pysph/base/warp_sph.py`: multilevel launcher + routing + periodic guard. +- `pysph/base/tests/test_warp_nnps.py`, `test_warp_sph.py`: fixtures + parity. + +## Experiments started / advanced / completed + +No new numerical experiment artifact. Correctness/scaling validated via the +kill-gate fixtures and the ~9x candidate-reduction diagnostic on a synthetic +localized-refinement case (197k vs 1.77M candidate pairs). + +## Handoff for next session + +Suites pass as SEPARATE invocations: `test_warp_nnps.py` 34, `test_warp_codegen.py` +10, `test_warp_sph.py` 57. Do NOT run them combined in one process (WSL2 +PTX-JIT hang, pre-existing). Venv `/home/kunalp/.pqt_venv_e0b41259/bin/python`; +commit with `--no-verify` (pre-commit hook uses bare `python`). Next: adaptive- +timestep + fused-group multilevel parity to close plan section 5, then the +dense-vs-sparse memory check to accept ADR-0007. HEAD `8861d331`. diff --git a/.ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md b/.ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md new file mode 100644 index 000000000..0c63f1fc3 --- /dev/null +++ b/.ai/implementations/blast-from-the-past/updates/weekly/2026-W25.md @@ -0,0 +1,126 @@ +--- +type: weekly-closeout +week: 2026-W25 +range: 2026-06-15 to 2026-06-21 +user: @kunalpuri-prediqt +--- + +# Weekly Closeout - 2026-W25 + +## Executive summary + +The week the Warp GPU backend went from nothing to two validated benchmarks, a +cross-GPU performance story, an upstream PR, and the start of a third (flagship) +benchmark. Began with the `.ai/` memory scaffold (Mon); by Fri the backend had a +device-mirrored `ParticleArray`, a full Warp SPH kernel set, a dynamic +equation-group code generator, grid-direct (cache-free) neighbour traversal, +periodic boxes, **two validated cases** (2D elliptical drop; 3D Lobovsky +dam-break, 3-tier parity), a **five-GPU throughput sweep** with cost/energy/ +roofline lenses, and **PR #435** open on pypr/pysph. Friday evening opened +**ADR-0006**: a floating / rigid body whose deliverable is a photorealistic +animation -- P0 + P1 landed. + +## Main accomplishments + +- **Warp backend built end to end:** device-mirror `ParticleArray` + (`warp_device_helper.py`), NNPS (brute-force / cached-flat / uniform-grid; + periodic min-image box), SPH kernels (summation density, isothermal + Tait EOS + with `cs`, continuity, pressure gradient, Monaghan AV, XSPH, device-reduced + adaptive dt), Euler / KDK / continuity-PEC step paths. +- **Dynamic equation-group code generator** (`warp_codegen.py`, ADR-0003): fused + one cached JIT kernel per `(equation-set, dtype)`; fixed an order-dependent + kernel-name bug that busted Warp's on-disk cache each session (now a + deterministic md5 structural key). Migrated ~14 hand kernels onto it. +- **Grid-direct everywhere** (ADR-0004): no step path materialises a flat CSR + neighbour list; periodic minimum-image distance added. +- **Validation case 1 -- 2D elliptical drop:** resolved CPU-vs-Warp parity + (fp32-scale deltas) + perf. +- **Validation case 2 -- 3D Lobovsky dam-break (ADR-0005):** additive Wendland id, + ramped gravity, multi-array stepping, Tait-HG walls; 3-tier parity + (backend tests; hand-rolled CPU EPEC ~1e-8; real `dam_break_3d_lobovsky.py` + Application to fp32 scale, p_max ~1.1% once developed). Fluid step fused. +- **Cross-GPU sweep:** L40S, RTX 5090, RTX PRO 6000 Blackwell, B300 + RTX 4060 + anchor; speedup-vs-1-CPU-core, $-cost, energy, and roofline lenses. Production + result on RTX PRO 6000 Blackwell: 1M / 100 steps **271x wall / 414x per-step** + vs single-threaded PySPH CPU, KE delta 8e-10. +- **PR #435** (pypr/pysph, from fork `blast-from-the-past`): dam-break folded in; + description maintained via the author token (Claude GitHub MCP is 403 upstream). +- **BUILD.md** + results-report tooling; `setup.py` MPI/Zoltan gating fix. +- **ADR-0006 floating / rigid body (Fri eve):** render stack chosen (Blender + Cycles Tier A; Omniverse rejected for this 8 GB/WSL2/Warp box); **P0** kill-test + (atomic_add reduction reproduces `RigidBodyMoments`, fp32 atomic non-determinism + -> f64 accumulators) and **P1** backend reduction + tests landed, cache-stable. + +## Important decisions + +- ADR-0001 - Adopt `.ai/` memory system. +- ADR-0002 - Warp device mirror for `ParticleArray`. +- ADR-0003 - Dynamic Warp equation-group code generation (fused kernels). +- ADR-0004 - Grid-direct neighbour traversal for the WCSPH continuity hot path. +- ADR-0005 - Additive 3D WCSPH physics for the Warp dam-break benchmark. +- ADR-0006 - Additive rigid-body coupling for the Warp floating-body benchmark. +- Backend default precision is fp32 (`compyle.config.use_double=False`); f64 + opt-in. Rigid reduction accumulates in f64 regardless (atomic_add + non-associativity). +- Render: Blender Cycles via splashsurf; **Omniverse rejected** for this setup. + +## Aspects advanced this week + +warp-backend, gpu-nnps, particle-memory, cython-boundary, validation-benchmarks, +host-integration (all six -- the whole backend was built this week). + +## Experiments completed + +- 2D elliptical-drop runner (resolved CPU-vs-Warp + 1M perf). +- 3D Lobovsky dam-break runner + 2-tier CPU-parity harness + cross-GPU sweep. +- Plus the foundational sub-experiments (NNPS baselines, summation density, EOS + + continuity, pressure gradient, WCSPH Euler step). +- **In-flight:** floating / rigid body (`2026-06-19-warp-floating-body-rigid`), + P0 + P1 done. + +## Open questions resolved / still open + +- **Resolved:** "blazing fast" is now measurable (19-491x vs 1 CPU core across + GPUs); Warp version pinned (1.14, RTX 4060 dev); first benchmark case chosen + and shipped (elliptical drop -> dam-break). +- **Still open:** the ~1.43e8 particle-steps/s throughput plateau is + occupancy/launch/grid-build bound (NOT bandwidth -- roofline shows <12% of + peak); next perf win is occupancy/launch tuning. Super-linear per-step knee is + non-monotonic with VRAM (algorithmic, to profile). + +## Risks + +- **compyle 0.9.1 + Python 3.14:** `ast.Str` removed -> the CPU rigid-body + reference Application can't run, so floating-body tier-2 parity is deferred + behind a compyle upgrade (the GPU path is unaffected). See memory + `compyle-py314-aststr-blocker`. +- Floating-body coupling fidelity (P3): must implement the real Liu force, not a + wall mirror; validate the first-plunge transient (draft is coupling-blind). +- Cold full-suite compile is ~20 min (Wendland inflated every kernel's PTX); + use targeted subsets + warm cache. + +## Carry-over tasks / recommended focus next week + +- **Floating body (ADR-0006):** P2 host 6-DOF integrate + device transform; P3 + Liu coupling group + `NumberDensity` pre-pass + sibling driver + `wc_sph_dam_break_rigid_step` (resolve `arho` double-count); P4 EPEC fidelity; + assemble the 3D surge-tosses-a-box case. +- **Render the animation:** splashsurf -> Blender Cycles/OptiX -> cloud finals -> + ffmpeg (install ffmpeg; Z-up->Y-up; shadow-caustics vs volume-absorption bug). +- **Dam-break follow-ups:** RTX 5090 + B300 sweep (boxes were busy); SPHERIC/ + Kleefsman obstacle case; full `tf=2.5` run + probe-pressure vs Lobovsky data. +- **@prabhu review** of the dam-break PR #435 pending. +- Optional: compyle upgrade for tier-2 CPU rigid parity. + +## Memory health + +1. **Re-derived:** none. +2. **Stale or wrong:** the prior `~30x` 1M figure (mixed fresh-Warp / stale-CPU) + was superseded by same-session ratios; `current.md`'s dam-break "not yet + committed" tail predates the commits (corrected this closeout). +3. **Unread:** n/a. + +## Curation performed + +- Decision graph/index regenerated (ADR-0006 registered); `validate-memory` PASS. +- New session memories: `warp-floating-body-benchmark`, `compyle-py314-aststr-blocker`. diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 000000000..699e14355 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1 @@ +See `.ai/AGENTS.md` for the implementation router. For `blast-from-the-past`, use `.ai/implementations/blast-from-the-past/AGENTS.md`. diff --git a/BUILD.md b/BUILD.md new file mode 100644 index 000000000..4f1a984e3 --- /dev/null +++ b/BUILD.md @@ -0,0 +1,338 @@ +# Building PySPH (with the Warp GPU backend) + +This document describes how to build and run this repository the way it was set +up on the development machine used for the Warp-backend work, so you can +reproduce it elsewhere. + +PySPH is a Python package with **Cython/C++ extension modules** that must be +compiled. The experimental **Warp GPU backend** (`pysph/base/warp_*.py`) is an +*extra* on top of upstream PySPH: it is not in the repo's requirements and needs +`warp-lang` plus an NVIDIA GPU. + +There are two tiers you can build: + +- **Core PySPH** — the CPU package (Cython extensions + the standard solver). + Enough to run `pysph/examples/...` and the non-GPU tests. +- **Warp GPU backend** — adds `warp-lang` and a CUDA-capable GPU, needed to run + `pysph/base/warp_*.py`, `pysph/base/tests/test_warp_*.py`, and the + elliptical-drop GPU benchmarks under + `.ai/implementations/blast-from-the-past/experiments/`. + +--- + +## 1. Prerequisites + +| Component | What's needed | Version used here (known-good) | +|---|---|---| +| OS | Linux (incl. WSL2); macOS/Windows also supported by PySPH upstream | Linux 6.6 (WSL2) | +| C/C++ compiler | A working `gcc`/`g++` (OpenMP optional but recommended) | gcc/g++ 15.2.0 | +| Python | CPython 3.x with `venv` | 3.14.4 | +| Core Python build deps | numpy, Cython, compyle, cyarray, mako, pytools, Beaker, setuptools, wheel | see table in §6 | +| **GPU extras** | NVIDIA GPU + driver, `warp-lang` | RTX 4060 Laptop (sm_89), driver 595.79, CUDA 13.2 | + +Notes: + +- The base scientific stack on the dev machine (numpy, h5py, mpi4py, pybind11) + was provided by **Spack** and a pip venv was layered on top. That is **not + required** — plain `pip` works for the core build. `h5py`/`mpi4py` are only + needed for HDF5 output and MPI/Zoltan parallel runs respectively; skip them + for a minimal build. MPI/Zoltan/PyZoltan setup is covered in §5a. +- Warp ships its own CUDA runtime support; you do **not** need a separately + installed CUDA Toolkit, only a recent NVIDIA driver and a supported GPU. + +--- + +## 2. Get the source + +```bash +git clone pysph +cd pysph +``` + +--- + +## 3. Create an isolated environment + +Any Python 3.10+ works; 3.14 was used here. A plain venv is sufficient. + +```bash +python3 -m venv .venv +source .venv/bin/activate +python -m pip install --upgrade pip setuptools wheel +``` + +(Conda/mamba work too; this repo does not require them. The dev machine used a +plain venv at `~/.pqt_venv_/`, no conda.) + +--- + +## 4. Build core PySPH (Cython extensions) + +Install the build/runtime dependencies, then compile the extension modules +in place. The canonical build command (also `make build`) is: + +```bash +pip install -r requirements.txt # numpy, Cython, compyle, cyarray, mako, pytools, Beaker, setuptools +python setup.py build_ext --inplace # compile the Cython/C++ extensions +``` + +Or do both in one step with an editable install (uses `pyproject.toml`'s +build backend and builds the extensions): + +```bash +pip install -e . +``` + +This produces ~22 compiled modules under `pysph/` (e.g. +`pysph/base/nnps_base.*.so`, `linked_list_nnps`, `octree`, `c_kernels`, +`gpu_nnps_base`, ...). If `build_ext` reports it cannot import Cython it +disables OpenMP and continues; install `Cython>=0.20` (3.2.5 here) to keep +OpenMP and the threaded NNPS paths. + +Optional CPU extras: + +```bash +pip install -r requirements-test.txt # pytest, mock, h5py, vtk (tests + HDF5 + viz) +``` + +--- + +## 5. Add the Warp GPU backend (extra) + +Only needed to run the Warp code and GPU benchmarks. Requires an NVIDIA GPU and +a recent driver. + +```bash +pip install warp-lang # 1.14.0 used here +``` + +The Warp device path runs in **single precision (fp32)** because compyle's +configuration has `use_double = False` on this machine. Verify with: + +```bash +python -c "from compyle.config import get_config; print('use_double =', get_config().use_double)" +# -> use_double = False +``` + +Double precision is governed by compyle's configuration; see the compyle docs if +you need fp64 on the GPU path. + +For specific data-center and Blackwell GPUs (V100, A100, H100, RTX 5090, +RTX PRO 6000 Blackwell), see [§10](#10-gpu-architecture-compatibility-v100-a100-h100-rtx-5090-rtx-pro-6000-blackwell). + +--- + +## 5a. MPI + Zoltan + PyZoltan (optional -- distributed parallel runs) + +**Not needed** for the Warp single-GPU backend or serial CPU runs. Required only +for PySPH's MPI/Zoltan distributed mode (multi-rank domain decomposition; the +`slow or parallel` tests). `setup.py` builds the +`pysph.parallel.parallel_manager` extension only when **mpi4py**, the **Zoltan** +library, and the separate **`pyzoltan`** package are all present; if PyZoltan is +absent the build prints a notice and skips that extension (so a plain +`build_ext` works for serial/GPU use even on a box that has mpi4py but no +PyZoltan). + +Canonical instructions are in `docs/source/installation.rst` and the PyZoltan +docs (). The flow: + +1. Install and verify **mpi4py**, configured for your MPI/hardware. +2. Install the **Zoltan** library (from Trilinos, your package manager, or + Spack). Note its `include/` (has `zoltan.h`) and `lib/` (has `libzoltan.a`). +3. Point the build at Zoltan via an env var (or `~/.compyle/config.py`): + ```bash + export ZOLTAN=/path/to/zoltan # expects $ZOLTAN/include and $ZOLTAN/lib + # or, instead of ZOLTAN: + export ZOLTAN_INCLUDE=/path/include ZOLTAN_LIBRARY=/path/lib + # if Zoltan came from Trilinos, also: export USE_TRILINOS=1 # links -ltrilinos_zoltan + ``` +4. Install **PyZoltan** (not vendored in this repo), then PySPH, with build + isolation **off** so they use your mpi4py: + ```bash + pip install pyzoltan --no-build-isolation + pip install -e . --no-build-isolation # or: python setup.py build_ext --inplace + ``` +5. Custom MPI compile/link flags go in `~/.compyle/config.py` (`MPI_CFLAGS`, + `MPI_LINK`); the Zoltan options above can live there too. + +Verify and run the parallel suite: + +```bash +python -c "import mpi4py, pyzoltan; print('pyzoltan', pyzoltan.__version__)" +python -m pytest -v -m "slow or parallel" # the CI parallel/Zoltan command +``` + +On the dev machine here, mpi4py + Zoltan + PyZoltan (1.1.1) come from the Spack +stack and `pysph/parallel/parallel_manager` is built; a plain +`pip install pyzoltan --no-build-isolation` against a system/Spack Zoltan also +works. + +--- + +## 6. Verify the build + +Core imports and the Warp backend: + +```bash +python -c "import pysph, compyle; print('pysph + compyle import OK')" +python -c "import warp; warp.init()" # GPU path: should list your CUDA device +``` + +Run the test suites: + +```bash +# Standard PySPH CPU tests (excludes slow tests by default; see setup.cfg/tox.ini) +python -m pytest -m "not slow" pysph + +# Warp GPU backend focused suite (requires GPU + warp-lang) -- 50 tests here +python -m pytest -q \ + pysph/base/tests/test_warp_codegen.py \ + pysph/base/tests/test_warp_sph.py \ + pysph/base/tests/test_warp_nnps.py +``` + +The first Warp run JIT-compiles kernels (slow, a few minutes cold); compiled +kernels are cached under `~/.cache/warp//`, so reruns are fast. + +Known-good toolchain on the dev machine (from `pip freeze`): + +| Package | Version | +|---|---| +| Python | 3.14.4 | +| numpy | 2.4.6 | +| Cython | 3.2.5 | +| compyle | 0.9.1 | +| cyarray | 1.2 | +| mako | 1.3.12 | +| pytools | (per requirements) | +| warp-lang | 1.14.0 (GPU extra) | +| matplotlib | 3.10.9 (benchmark plots) | +| h5py | 3.16.0 (HDF5 output, optional) | +| mpi4py | 4.1.1 (parallel, optional) | + +--- + +## 7. Running the Warp elliptical-drop benchmarks (optional) + +These live under the implementation tree and import the runner by path, so set +`PYTHONPATH` to the experiment directory: + +```bash +R=.ai/implementations/blast-from-the-past/experiments/2026-06-16_warp-elliptical-drop-runner + +# Warp-only fixed-step run +PYTHONPATH=$R python $R/warp_elliptical_drop_runner.py \ + --nx 565 --steps 100 --dt 3.732778967800475e-07 \ + --c0 1400.0 --alpha 0.1 --eos tait --gamma 7.0 \ + --kernel gaussian --xsph-eps 0.5 --density-mode continuity + +# CPU (PySPH Application) vs Warp headline, million particles, 100 fixed steps +PYTHONPATH=$R python $R/headline_million_100step.py --nx 565 --steps 100 + +# Segmented per-step profile of the grid-direct neighbor path (ADR-0004) +PYTHONPATH=$R python $R/profile_grid_direct_neighbors.py --nx 565 --steps 12 --warmup 2 +``` + +The CPU baseline in the headline/comparison scripts is the real PySPH Cython +Application (`pysph/examples/elliptical_drop_no_scheme.py`), run single-threaded +with `--no-adaptive-timestep --n-damp 0` for an apples-to-apples fixed-step +comparison. + +--- + +## 8. Rebuilding after editing Cython (`.pyx`/`.pxd`) + +Re-run the in-place build; only changed modules recompile: + +```bash +python setup.py build_ext --inplace # or: make build +``` + +Editing the pure-Python Warp files (`pysph/base/warp_*.py`) needs no rebuild. + +--- + +## 9. Gotchas + +- **`python` vs `python3`**: this repo's tooling and the Makefile call bare + `python`. Inside an activated venv `python` resolves correctly. In a + non-interactive shell where only `python3` is on `PATH`, invoke the venv + interpreter explicitly (e.g. `/bin/python`). +- **Pre-commit hook**: `.git/hooks/pre-commit` runs + `python .ai/implementations/blast-from-the-past/scripts/validate-memory.py`. + If bare `python` is not on `PATH` the hook fails; run the validator manually + with the venv interpreter and commit with `git commit --no-verify`. +- **OpenMP**: if Cython is missing at build time, `setup.py` disables OpenMP and + falls back to `no_omp_threads`. Install Cython before building for threaded + NNPS. +- **GPU not found**: `warp.init()` prints the detected devices. If it shows only + `cpu`, check the NVIDIA driver (`nvidia-smi`) and that `warp-lang` matches your + driver's capability. + +--- + +## 10. GPU architecture compatibility (V100, A100, H100, RTX 5090, RTX PRO 6000 Blackwell) + +**The PySPH build is GPU-architecture-independent.** The compiled Cython +extensions are CPU code, and the Warp backend JIT-compiles its kernels to your +GPU's architecture at runtime (cached under `~/.cache/warp//`, keyed by +architecture). So **no build or source changes are needed for any of these +GPUs** — moving to a different card only requires: + +1. an NVIDIA driver new enough for that architecture, and +2. a `warp-lang` whose bundled CUDA toolkit supports it. + +The `warp-lang 1.14.0` used here bundles **CUDA Toolkit 12.9**, which targets +every architecture below (sm_70 through sm_120). Confirmed live via +`warp.init()`: `CUDA Toolkit 12.9, Driver 13.2`. So the same +`pip install warp-lang` step from §5 works on all of them. + +| GPU | Architecture | Compute capability | VRAM | Min NVIDIA driver branch | fp64 throughput | +|---|---|---|---|---|---| +| Tesla V100 | Volta | `sm_70` | 16 / 32 GiB | R384+ (any current) | strong (~1:2) | +| A100 | Ampere | `sm_80` | 40 / 80 GiB | R450+ | strong (~1:2) | +| H100 | Hopper | `sm_90` | 80 / 94 GiB | R525+ | strong (~1:2) | +| RTX 5090 | Blackwell (consumer) | `sm_120` | 32 GiB | **R570+** | weak (~1:64) | +| RTX PRO 6000 Blackwell | Blackwell (workstation) | `sm_120` | 96 GiB | **R570+** | weak | +| RTX 4060 (dev machine) | Ada Lovelace | `sm_89` | 8 GiB | — | weak | + +Per-architecture notes: + +- **Drivers.** Blackwell (`sm_120`: RTX 5090, RTX PRO 6000 Blackwell) requires an + **R570 or newer** driver; older drivers will not enumerate the GPU. V100 / + A100 / H100 work with any reasonably current driver. The dev machine's driver + reports CUDA 13.2 (newer than R570), so it already covers Blackwell. +- **warp-lang version.** 1.14.0 (CUDA Toolkit 12.9) covers all of these. + **CUDA 12.8 was the first toolkit with Blackwell (`sm_100`/`sm_120`) support**, + so if you are on an older `warp-lang` whose bundled CUDA predates 12.8, the + RTX 5090 / RTX PRO 6000 will fail to compile kernels — run + `pip install -U warp-lang` to get a CUDA ≥ 12.8 build. V100/A100/H100 are fine + on much older warp-lang. +- **First run recompiles, no code change.** Because the kernel cache is keyed by + architecture, the first run on a new GPU JIT-compiles (a few minutes) and then + caches. Nothing in `pysph/base/warp_*.py` changes per GPU. +- **Precision.** This code runs **fp32** (compyle `use_double = False`). On + V100 / A100 / H100 (full-rate fp64, ~1:2) you can enable double precision via + compyle's config with little throughput penalty if you need it. On the + RTX 5090 and RTX PRO 6000 Blackwell, fp64 is heavily rate-limited (~1:64) — + **keep fp32** there. +- **Memory / problem size.** The elliptical-drop benchmarks use a small fraction + of the 4060's 8 GiB at 1M particles. With 32–96 GiB (Blackwell) or 80 GiB + (A100) / 80–94 GiB (H100) you can scale particle counts up by one to two orders + of magnitude. Note the initial particle layout is built with `numpy.mgrid` on + the **CPU** before upload, so size host RAM accordingly. +- **Multi-GPU.** The current Warp NNPS/SPH path targets a **single device** + (`cuda:0` by default). On a multi-GPU node (e.g. 8×A100 / 8×H100) it uses one + GPU; pin a specific one with `CUDA_VISIBLE_DEVICES=` or by constructing the + NNPS with the desired `wp` device. Multi-GPU domain decomposition is not + implemented in this backend. + +Verify on the target machine before running: + +```bash +python -c "import warp as wp; wp.init(); print([(d.name, 'sm_%s' % d.arch, round(d.total_memory/2**30, 1)) for d in wp.get_cuda_devices()])" +``` + +If each device shows the expected `sm_XX` it will run. An "unsupported +architecture" / PTX / NVRTC error means the `warp-lang` or driver is too old for +that GPU — update both (newest driver for the card, `pip install -U warp-lang`). diff --git a/CODEBASE_UNDERSTANDING.md b/CODEBASE_UNDERSTANDING.md new file mode 100644 index 000000000..55736a3b1 --- /dev/null +++ b/CODEBASE_UNDERSTANDING.md @@ -0,0 +1,428 @@ +# PySPH Codebase Understanding + +Generated for the repository rooted at `/home/kunalp/work/particles/pysph`. + +This report uses these labels: + +- `[Confirmed]` means the statement is directly supported by cited source code, docs, or command output. +- `[Inferred]` means the statement follows from multiple cited facts but is not stated verbatim in one location. +- `[Unverified]` means the code suggests the point, but this pass did not execute enough runtime paths to prove it. +- `[Unknown]` means the pass did not find enough evidence to make a claim. + +## 1. Executive Summary + +[Confirmed] PySPH is a Python framework for Smoothed Particle Hydrodynamics (SPH), with performance-critical code paths in Cython and optional PyOpenCL-backed acceleration; the README explicitly says high-level Python code is automatically converted to Cython or OpenCL and can use OpenMP, OpenCL, and MPI when available (`README.rst:7`, `README.rst:10`, `README.rst:13`, `README.rst:15`). [Confirmed] The package metadata calls it "A general purpose Smoothed Particle Hydrodynamics framework" and classifies the project as beta, console-oriented, and targeted at science/research and developers (`setup.py:732`, `setup.py:737`, `setup.py:762`, `setup.py:765`, `setup.py:766`, `setup.py:773`). + +[Confirmed] The repository is organized around five core subsystems: particle storage and neighbor search in `pysph/base`, equation/code generation and schemes in `pysph/sph`, solver/application orchestration in `pysph/solver`, MPI/Zoltan partitioning in `pysph/parallel`, and command-line/post-processing tools in `pysph/tools` (`pysph/base/particle_array.pyx:68`, `pysph/base/nnps.py:1`, `pysph/sph/sph_compiler.py:1`, `pysph/solver/application.py:52`, `pysph/parallel/parallel_manager.pyx:1`, `pysph/tools/cli.py:11`). [Confirmed] Example applications live under `pysph/examples`, are discoverable through `pysph run`, and can be executed by module/file name (`pysph/examples/run.py:49`, `pysph/examples/run.py:97`, `pysph/examples/run.py:137`, `pysph/examples/run.py:153`). + +[Inferred] The intended product shape is a research and simulation framework rather than a single-purpose solver binary: users create particle arrays, choose or write SPH equations/schemes, run through `Application`, and receive particle-output files that can be viewed or converted (`pysph/solver/application.py:52`, `pysph/solver/application.py:1625`, `pysph/sph/equation.py:392`, `pysph/solver/output.py:306`, `pysph/tools/cli.py:58`). [Confirmed] It includes many published formulations, including WCSPH, transport-velocity variants, EDAC, delta-SPH, ISPH/IISPH/SISPH, GSPH, CRKSPH, AGSPH, ADKE, and Akinci-style rigid/fluid support (`README.rst:50`, `README.rst:60`, `README.rst:68`, `README.rst:74`, `README.rst:78`, `README.rst:82`, `README.rst:86`, `README.rst:90`, `README.rst:94`). + +[Confirmed] The central execution path is `pysph` console script -> `pysph.tools.cli.main` -> `pysph run`/example execution -> `Application.run()` -> `Application.setup()` -> solver/equation/particle creation -> `Solver.setup()` compilation -> `Solver.solve()` timestepping (`setup.py:758`, `pysph/tools/cli.py:58`, `pysph/examples/run.py:137`, `pysph/solver/application.py:1525`, `pysph/solver/application.py:1543`, `pysph/solver/solver.py:186`, `pysph/solver/solver.py:425`). [Confirmed] Generated SPH code is compiled through `SPHCompiler`, which wires acceleration evaluations into the integrator, selects Cython/OpenCL/CUDA helper paths, compiles generated modules, and attaches compiled objects back to runtime evaluators (`pysph/sph/sph_compiler.py:1`, `pysph/sph/sph_compiler.py:26`, `pysph/sph/sph_compiler.py:61`). + +[Confirmed] The build is hybrid Python/Cython and optionally MPI/Zoltan/OpenMP/GPU-enabled: build requirements are declared in `pyproject.toml`, Cython extension modules are built in `setup.py`, OpenMP/MPI/Zoltan detection is handled by setup-time probes and configuration files, and extras expose `mpi`, `opencl`, `ui`, `tests`, `docs`, `dev`, and `all` dependency sets (`pyproject.toml:1`, `setup.py:1`, `setup.py:117`, `setup.py:217`, `setup.py:266`, `setup.py:318`, `setup.py:596`, `setup.py:701`). [Confirmed] CI tests Linux, macOS, and Windows for Python 3.11 and 3.12, with a separate Zoltan/MPI workflow on Ubuntu (`.github/workflows/tests.yml:15`, `.github/workflows/tests.yml:55`, `.github/workflows/tests.yml:70`, `.github/workflows/zoltan-tests.yml:17`, `.github/workflows/zoltan-tests.yml:64`). + +## 2. Repository Map + +### 2.1 Top-Level Shape + +[Confirmed] A Phase 1 file survey found top-level project files including `README.rst`, `CHANGES.rst`, `LICENSE.txt`, `pyproject.toml`, `setup.py`, `setup.cfg`, `tox.ini`, `Makefile`, `.github`, `docs`, `docker`, `pysph`, and `starcluster` (command: `ls -la`, repository root). [Confirmed] The source tree contains major directories `pysph/base`, `pysph/sph`, `pysph/solver`, `pysph/parallel`, `pysph/tools`, and `pysph/examples` (command: `tree -L 2 -d`, repository root). [Confirmed] A Phase 1 extension histogram found 296 Python files, 22 Cython `.pyx` files, 20 `.pxd` files, 10 Mako templates, 33 reStructuredText files, 15 PNGs, and 13 CSVs outside `.git` (command: `find . -path ./.git -prune -o -type f ...`, repository root). + +### 2.2 Core Directories + +| Path | Role | Confidence and Evidence | +| --- | --- | --- | +| `pysph/base` | Particle arrays, low-level typed arrays, NNPS, kernels, domain managers, GPU NNPS exports. | [Confirmed] `ParticleArray` is documented as a collection of particles with properties and constants (`pysph/base/particle_array.pyx:68`, `pysph/base/particle_array.pyx:83`). [Confirmed] `pysph/base/nnps.py` exports linked-list, box-sort, spatial-hash, cell-indexing, z-order, stratified, octree, and compressed NNPS variants (`pysph/base/nnps.py:1`). | +| `pysph/sph` | SPH equation abstraction, schemes, integrators, compiler helpers, backend templates. | [Confirmed] `Equation` is the base abstraction for SPH equations and stores destination/source particle arrays (`pysph/sph/equation.py:392`). [Confirmed] `SPHCompiler` compiles acceleration evaluators and integrators for Cython/OpenCL/CUDA helper paths (`pysph/sph/sph_compiler.py:1`, `pysph/sph/sph_compiler.py:61`). | +| `pysph/solver` | Application lifecycle, solver loop, output, visualization helpers, command interfaces. | [Confirmed] `Application` documents the simulation lifecycle and the methods subclasses usually override (`pysph/solver/application.py:52`, `pysph/solver/application.py:1543`). [Confirmed] `Solver.solve()` performs the timestep loop (`pysph/solver/solver.py:425`). | +| `pysph/parallel` | MPI/Zoltan distributed particle exchange and load balancing. | [Confirmed] `parallel_manager.pyx` imports `mpi4py.MPI` and PyZoltan modules, and implements `ParticleArrayExchange`, `ParallelManager`, and Zoltan partition managers (`pysph/parallel/parallel_manager.pyx:1`, `pysph/parallel/parallel_manager.pyx:47`, `pysph/parallel/parallel_manager.pyx:343`, `pysph/parallel/parallel_manager.pyx:1040`). | +| `pysph/tools` | CLI dispatch and post-processing utilities. | [Confirmed] `pysph.tools.cli` dispatches `view`, `run`, `dump_vtk`, and `dump_xdmf`, plus utility subcommands such as `test`, `binder`, `cull`, and `cache` (`pysph/tools/cli.py:11`, `pysph/tools/cli.py:58`). | +| `pysph/examples` | Runnable examples and example-discovery CLI support. | [Confirmed] The example runner scans example modules recursively, maps names to modules, and executes examples by file/module name (`pysph/examples/run.py:49`, `pysph/examples/run.py:97`, `pysph/examples/run.py:137`). | +| `docs` | User and developer documentation. | [Confirmed] Installation docs describe pip, dev installs, MPI/PyZoltan setup, compiler requirements, and optional dependencies (`docs/source/installation.rst:39`, `docs/source/installation.rst:71`, `docs/source/installation.rst:170`, `docs/source/installation.rst:219`). | +| `docker` | Container support. | [Confirmed] Phase 1 found `docker/base/Dockerfile` and `docker/README.md` (command: `find . -maxdepth 3 ...`, repository root). | +| `starcluster` | Legacy/deployment-adjacent assets. | [Inferred] Phase 1 found a small `starcluster` top-level directory, but this pass did not trace it into the main build or runtime paths (command: `tree -L 2 -d`, repository root). | + +### 2.3 Source, Test, and Documentation Inventory + +[Confirmed] A Phase 1 count found 315 code/template files under `pysph` when counting `.py`, `.pyx`, `.pxd`, and `.mako` files (command: `find pysph -maxdepth 3 -type f ... | wc -l`). [Confirmed] A Phase 1 test count found 37 `test*.py` files below `pysph/*/tests` (command: `find pysph -path '*/tests/*' -name 'test*.py' | wc -l`). [Confirmed] Default pytest configuration excludes the `slow` marker (`setup.cfg:1`, `tox.ini:5`), while `Makefile` provides separate `test` and `testall` targets for non-slow and all tests respectively (`Makefile:52`, `Makefile:55`). + +### 2.4 Peripheral, Generated, and Legacy Areas + +[Confirmed] Runtime-generated code is intentionally cached outside the repository under `~/.pysph/source`, according to the README (`README.rst:208`). [Confirmed] CI caches both `~/.pysph` and `~/.compyle`, matching the runtime-generation and compiler-configuration design (`.github/workflows/tests.yml:70`, `.github/workflows/tests.yml:73`). [Inferred] `tox.ini` appears stale relative to current CI because it lists Python 2.7 and 3.5-3.7 environments, while GitHub Actions currently tests Python 3.11 and 3.12 (`tox.ini:1`, `.github/workflows/tests.yml:15`). [Inferred] `pysph/tools/pysph_to_vtk.py` looks older or peripheral because newer CLI paths expose `dump_vtk` through `pysph.tools.cli` and `pysph.solver.vtk_output` (`pysph/tools/cli.py:19`, `pysph/solver/vtk_output.py:152`, `pysph/tools/pysph_to_vtk.py:1`). + +### 2.5 Dependency Boundaries + +[Confirmed] This pass did not find top-level directories named `third_party`, `vendor`, `extern`, or `deps` in the repository survey (command: `find . -maxdepth 3 -type d ...`, repository root). [Confirmed] External packages are instead declared through build-system requirements, install requirements, extras, and CI install steps (`pyproject.toml:1`, `requirements.txt:1`, `setup.py:685`, `.github/workflows/tests.yml:55`). [Confirmed] Optional MPI/Zoltan integration depends on `mpi4py`, PyZoltan, and system Zoltan/Trilinos-style library paths (`setup.py:81`, `setup.py:266`, `setup.py:701`, `docs/source/installation.rst:71`, `.github/workflows/zoltan-tests.yml:30`). + +## 3. Build, Install, and Configuration + +### 3.1 Normal Python Build + +[Confirmed] The PEP 517 build-system dependencies include Beaker, Cython, compyle, cyarray, mako, numpy, pytools, setuptools, and wheel (`pyproject.toml:1`, `pyproject.toml:3`, `pyproject.toml:5`, `pyproject.toml:7`, `pyproject.toml:9`). [Confirmed] Runtime requirements in `requirements.txt` mirror the core package set: numpy, setuptools, Cython, cyarray, compyle, mako, pytools, and Beaker (`requirements.txt:1`, `requirements.txt:2`, `requirements.txt:3`, `requirements.txt:4`, `requirements.txt:5`, `requirements.txt:6`, `requirements.txt:7`, `requirements.txt:8`). [Confirmed] The `Makefile` build target uses `python setup.py build_ext --inplace`, so a developer build compiles extension modules into the working tree (`Makefile:18`). + +```bash +python -m pip install -r requirements.txt +python setup.py build_ext --inplace +``` + +[Confirmed] The installation docs also support `pip install PySPH`, installing from a GitHub archive, or cloning and using `python setup.py develop` for a development install (`docs/source/installation.rst:39`, `docs/source/installation.rst:48`, `docs/source/installation.rst:55`, `docs/source/installation.rst:61`). [Confirmed] The docs say a C/C++ compiler is needed not only for installation but also for runtime code generation (`docs/source/installation.rst:170`, `docs/source/installation.rst:202`). + +### 3.2 Cython Extension Matrix + +[Confirmed] `setup.py` builds low-level Cython extensions for particle arrays, NNPS, tools, and GPU NNPS wrappers through `get_basic_extensions()` (`setup.py:318`, `setup.py:362`, `setup.py:393`, `setup.py:539`). [Confirmed] OpenMP support is enabled only if setup-time compilation tests succeed or configuration overrides provide flags (`setup.py:117`, `setup.py:156`, `setup.py:340`). [Confirmed] Parallel extensions are added only when MPI is available; otherwise `get_parallel_extensions()` returns an empty list (`setup.py:596`, `setup.py:598`, `setup.py:620`). [Confirmed] Cythonization is performed by `Cython.Build.cythonize` unless setup is running in info mode or the default cythonization check says otherwise (`setup.py:720`, `setup.py:725`). + +### 3.3 Optional MPI/Zoltan Build + +[Confirmed] MPI detection tries to import `mpi4py` and PyZoltan, then probes MPI compiler/linker flags through config or `mpic++ --showme:*` commands (`setup.py:81`, `setup.py:217`, `setup.py:232`, `setup.py:239`). [Confirmed] Zoltan detection checks `ZOLTAN`, `ZOLTAN_INCLUDE`, `ZOLTAN_LIBRARY`, `USE_TRILINOS`, `sys.prefix`, and PyZoltan include paths (`setup.py:266`, `setup.py:274`, `setup.py:288`, `setup.py:305`). [Confirmed] The installation guide says MPI support should be installed by first installing `mpi4py`, then installing PyZoltan with Zoltan available, and then installing PySPH with `--no-build-isolation` when needed (`docs/source/installation.rst:71`, `docs/source/installation.rst:79`, `docs/source/installation.rst:105`). + +```bash +python -m pip install mpi4py +python -m pip install pyzoltan --no-build-isolation +python -m pip install -e . --no-build-isolation +``` + +[Confirmed] The separate Zoltan CI workflow installs `openmpi-bin`, `libopenmpi-dev`, and Trilinos/Zoltan packages, then installs `mpi4py<4`, PyZoltan, and PySPH before running slow/parallel tests (`.github/workflows/zoltan-tests.yml:30`, `.github/workflows/zoltan-tests.yml:43`, `.github/workflows/zoltan-tests.yml:46`, `.github/workflows/zoltan-tests.yml:49`, `.github/workflows/zoltan-tests.yml:64`). + +### 3.4 Optional GPU Build and Runtime + +[Confirmed] The package exposes an `opencl` extra requiring `pyopencl`, while CUDA support is present in runtime/compiler helper code rather than as a named setup extra (`setup.py:701`, `pysph/sph/acceleration_eval_gpu_helper.py:173`, `pysph/sph/integrator_gpu_helper.py:117`). [Confirmed] Application options include `--opencl`, `--cuda`, `--use-local-memory`, and `--use-double`, so GPU backend selection happens at runtime after command-line processing (`pysph/solver/application.py:421`, `pysph/solver/application.py:429`, `pysph/solver/application.py:434`, `pysph/solver/application.py:922`). [Confirmed] Installation docs show OpenCL example runs with `pysph run elliptical_drop --opencl` and `--use-double` (`docs/source/installation.rst:1062`, `docs/source/installation.rst:1074`). + +### 3.5 Local Configuration Files + +[Confirmed] `setup.py` reads `~/.compyle/config.py` for compiler and linker flags, including `CC`, `CXX`, OpenMP flags, MPI flags, Zoltan paths, and `USE_TRILINOS` (`setup.py:32`, `setup.py:35`, `setup.py:64`, `setup.py:117`, `setup.py:217`, `setup.py:266`). [Confirmed] The installation guide documents the same `~/.compyle/config.py` knobs with examples for compilers, OpenMP, MPI, and Zoltan (`docs/source/installation.rst:119`, `docs/source/installation.rst:126`, `docs/source/installation.rst:133`, `docs/source/installation.rst:146`, `docs/source/installation.rst:154`). + +### 3.6 CI and Test Configuration + +[Confirmed] CI runs on Ubuntu, macOS, and Windows with Python 3.11 and 3.12 (`.github/workflows/tests.yml:15`, `.github/workflows/tests.yml:18`). [Confirmed] Non-Windows CI installs `pocl` and `pyopencl`, suggesting OpenCL code paths are expected to be exercised where available (`.github/workflows/tests.yml:35`, `.github/workflows/tests.yml:38`). [Confirmed] CI installs current `cyarray` and `compyle` from GitHub main branches before installing PySPH editable with no build isolation (`.github/workflows/tests.yml:55`, `.github/workflows/tests.yml:58`, `.github/workflows/tests.yml:62`). [Confirmed] CI runs `pytest -m 'slow or not slow' pysph`, which is effectively all tests not filtered by marker expression (`.github/workflows/tests.yml:79`). + +## 4. Running the Code + +### 4.1 CLI Entry + +[Confirmed] The installed console script is `pysph = pysph.tools.cli:main` (`setup.py:758`). [Confirmed] The CLI contains subcommands for `view`, `run`, `dump_vtk`, `dump_xdmf`, `test`, `binder`, `cull`, and `cache` (`pysph/tools/cli.py:58`, `pysph/tools/cli.py:70`, `pysph/tools/cli.py:87`, `pysph/tools/cli.py:96`, `pysph/tools/cli.py:99`, `pysph/tools/cli.py:105`, `pysph/tools/cli.py:110`, `pysph/tools/cli.py:120`). [Confirmed] The README demonstrates `pysph run elliptical_drop`, `pysph run dam_break_2d`, `pysph view`, and `pysph run cavity` (`README.rst:149`, `README.rst:160`, `README.rst:176`, `README.rst:186`). + +### 4.2 Common Commands + +| Task | Command | Confidence and Evidence | +| --- | --- | --- | +| List examples | `pysph run --list` | [Confirmed] `pysph.examples.run` has list and run modes, and README says examples can be listed with `pysph run` (`pysph/examples/run.py:153`, `README.rst:212`). | +| Run a basic example | `pysph run elliptical_drop` | [Confirmed] README uses this to verify installation (`README.rst:149`). | +| Run a dam break example | `pysph run dam_break_2d` | [Confirmed] README documents this command (`README.rst:160`). | +| Run a cavity example | `pysph run cavity` | [Confirmed] README documents this command and says it uses transport velocity formulation (`README.rst:186`, `README.rst:188`). | +| View output | `pysph view` | [Confirmed] README and CLI expose the viewer command (`README.rst:176`, `pysph/tools/cli.py:70`). | +| Convert to VTK | `pysph dump_vtk ` | [Confirmed] CLI dispatches `dump_vtk`, and `vtk_output.main` loads input files/directories and writes VTK files (`pysph/tools/cli.py:87`, `pysph/solver/vtk_output.py:152`). | +| Generate XDMF | `pysph dump_xdmf ` | [Confirmed] CLI dispatches `dump_xdmf`, and the tool generates XDMF for HDF5 outputs or time series directories (`pysph/tools/cli.py:96`, `pysph/tools/dump_xdmf.py:1`, `pysph/tools/dump_xdmf.py:81`). | +| Run MPI example | `mpirun -np 4 pysph run elliptical_drop` | [Confirmed] The runner preserves `sys.argv` and comments specifically mention this MPI invocation (`pysph/examples/run.py:137`, `pysph/examples/run.py:146`). | +| Run OpenCL | `pysph run elliptical_drop --opencl` | [Confirmed] Installation docs document the command (`docs/source/installation.rst:1062`). | + +### 4.3 Example Anatomy + +[Confirmed] A typical example subclasses `Application`, implements particle creation and scheme creation, optionally adds command-line options, configures the scheme, then calls `app.run()` in `if __name__ == '__main__'` (`pysph/examples/elliptical_drop.py:82`, `pysph/examples/elliptical_drop.py:90`, `pysph/examples/elliptical_drop.py:99`, `pysph/examples/elliptical_drop.py:110`, `pysph/examples/elliptical_drop.py:129`, `pysph/examples/elliptical_drop.py:223`). [Confirmed] `elliptical_drop` can switch between WCSPH and IISPH schemes via `SchemeChooser`, configures kernel/timestep/final time/output times, creates a circular patch of particles, and then post-processes results (`pysph/examples/elliptical_drop.py:99`, `pysph/examples/elliptical_drop.py:110`, `pysph/examples/elliptical_drop.py:129`, `pysph/examples/elliptical_drop.py:164`). [Confirmed] `cavity` offers TVF and EDAC schemes and creates fluid and solid particle arrays with a moving lid velocity (`pysph/examples/cavity.py:58`, `pysph/examples/cavity.py:78`, `pysph/examples/cavity.py:114`). [Confirmed] `sod_shocktube` offers multiple gas-dynamics schemes, uses a mirror-domain manager, and configures density/energy/pressure states for left and right regions (`pysph/examples/gas_dynamics/sod_shocktube.py:22`, `pysph/examples/gas_dynamics/sod_shocktube.py:70`, `pysph/examples/gas_dynamics/sod_shocktube.py:81`, `pysph/examples/gas_dynamics/sod_shocktube.py:95`). + +## 5. End-to-End Execution Flow + +[Confirmed] `Application` documents its lifecycle explicitly: constructor calls `initialize`, `create_scheme`, scheme option registration, and user option registration; `run()` parses args, consumes options, configures the scheme, creates solver/equations/particles/inlet/domain/NNPS/tools, customizes output, and then enters callbacks and timestepping (`pysph/solver/application.py:52`, `pysph/solver/application.py:78`, `pysph/solver/application.py:117`). [Confirmed] The implemented `setup()` method follows that lifecycle: parse/process/log/config, create solver/equations/particles, create inlet/domain/NNPS, configure solver, configure callbacks/tools/output, log info, write info (`pysph/solver/application.py:1543`, `pysph/solver/application.py:1547`, `pysph/solver/application.py:1555`, `pysph/solver/application.py:1566`, `pysph/solver/application.py:1574`, `pysph/solver/application.py:1583`, `pysph/solver/application.py:1598`). + +```mermaid +flowchart TD + A[pysph console script] --> B[pysph.tools.cli.main] + B --> C[pysph run] + C --> D[example Application subclass] + D --> E[Application.run] + E --> F[Application.setup] + F --> G[create particles, scheme, solver, equations] + G --> H[create domain and NNPS] + H --> I[configure Solver] + I --> J[SPHCompiler.compile] + J --> K[Solver.solve timestep loop] + K --> L[dump npz or hdf5 output] +``` + +[Confirmed] The console-script mapping is in package metadata, CLI dispatch is in `pysph.tools.cli`, and example execution occurs through `pysph.examples.run` (`setup.py:758`, `pysph/tools/cli.py:58`, `pysph/examples/run.py:137`). [Confirmed] `Application.run()` calls `setup()` and then `solve()` (`pysph/solver/application.py:1525`, `pysph/solver/application.py:1534`). [Confirmed] `Application.configure_solver()` eventually invokes `solver.setup(...)`, which compiles generated code and wires NNPS, integrator, callbacks, and outputs (`pysph/solver/application.py:1192`, `pysph/solver/solver.py:186`). [Confirmed] Solver output is dumped through `dump()`, which chooses HDF5 or NPZ based on extension and h5py availability (`pysph/solver/solver.py:520`, `pysph/solver/output.py:306`). + +### 5.1 Solver Loop + +```mermaid +flowchart TD + A[initial output and barrier] --> B[optional spatial reorder] + B --> C[initial acceleration] + C --> D[compute dt] + D --> E{time < tf and count < max_steps} + E -->|yes| F[pre_step callbacks] + F --> G[integrator.step] + G --> H[post_step callbacks] + H --> I[advance time/count] + I --> J[compute next dt] + J --> K[periodic output/progress/reorder/commands] + K --> E + E -->|no| L[final output] +``` + +[Confirmed] `Solver.solve()` dumps initial output, optionally reorders particles, computes initial accelerations, computes/adapts `dt`, loops through callbacks and `integrator.step`, updates time and count, dumps output, executes commands, and writes a final dump (`pysph/solver/solver.py:425`, `pysph/solver/solver.py:441`, `pysph/solver/solver.py:449`, `pysph/solver/solver.py:455`, `pysph/solver/solver.py:465`, `pysph/solver/solver.py:471`, `pysph/solver/solver.py:481`, `pysph/solver/solver.py:496`, `pysph/solver/solver.py:514`). [Confirmed] Adaptive timestepping asks the integrator for a timestep and performs an MPI minimum reduction when a parallel manager is present (`pysph/solver/solver.py:647`, `pysph/solver/solver.py:656`). + +## 6. Entry Points and Call Graph + +### 6.1 Public Entry Points + +[Confirmed] The most important public entry point is the `pysph` console script (`setup.py:758`). [Confirmed] Users can also run examples as Python modules because the tutorial notes `python -m pysph.examples.elliptical_drop`, and the example runner ultimately executes the selected file in the `__main__` namespace (`docs/tutorial/1_getting_started.ipynb:75`, `pysph/examples/run.py:17`, `pysph/examples/run.py:137`). [Confirmed] The `Application` class is the main extension point for simulations, with overridable methods such as `add_user_options`, `configure_scheme`, `consume_user_options`, `create_domain`, `create_inlet_outlet`, `create_equations`, `create_particles`, `create_scheme`, `create_solver`, `pre_step`, `post_stage`, `post_step`, and `post_process` (`pysph/solver/application.py:1625`, `pysph/solver/application.py:1632`, `pysph/solver/application.py:1648`, `pysph/solver/application.py:1660`, `pysph/solver/application.py:1680`, `pysph/solver/application.py:1694`, `pysph/solver/application.py:1706`, `pysph/solver/application.py:1716`, `pysph/solver/application.py:1722`, `pysph/solver/application.py:1739`, `pysph/solver/application.py:1745`, `pysph/solver/application.py:1751`, `pysph/solver/application.py:1757`). + +### 6.2 Main Call Graph + +```mermaid +flowchart TD + CLI[cli.main] --> Run[examples.run.main] + Run --> Exec[exec example file] + Exec --> App[Application subclass] + App --> Setup[Application.setup] + Setup --> Scheme[Scheme or SchemeChooser] + Setup --> Particles[ParticleArray list] + Setup --> Equations[Equation groups] + Setup --> Solver[Solver] + Solver --> Compiler[SPHCompiler] + Compiler --> AE[AccelerationEval] + Compiler --> Integrator[Integrator] + Solver --> Output[Output.dump] +``` + +[Confirmed] `SchemeChooser` wraps several schemes and delegates setup, option consumption, solver creation, and equation creation to the selected scheme (`pysph/sph/scheme.py:141`, `pysph/sph/scheme.py:165`, `pysph/sph/scheme.py:177`, `pysph/sph/scheme.py:185`). [Confirmed] `AccelerationEval` validates particle arrays and equation property requirements, selects a backend, creates backend group objects, and delegates compute calls to a compiled object (`pysph/sph/acceleration_eval.py:32`, `pysph/sph/acceleration_eval.py:166`, `pysph/sph/acceleration_eval.py:189`, `pysph/sph/acceleration_eval.py:228`). [Confirmed] `Integrator` stores per-particle-array steppers, receives compiled acceleration evaluations, and delegates stage execution to compiled integrator objects after setup (`pysph/sph/integrator.py:20`, `pysph/sph/integrator.py:122`, `pysph/sph/integrator.py:266`). + +## 7. Major Workflows + +### 7.1 Serial SPH Example + +[Confirmed] In a serial example, a subclass creates particles, chooses a scheme, lets `Application` create the solver and equations, uses a CPU NNPS by default, compiles Cython evaluator/integrator code, and runs the solver loop (`pysph/examples/elliptical_drop.py:99`, `pysph/examples/elliptical_drop.py:129`, `pysph/solver/application.py:1543`, `pysph/solver/application.py:1007`, `pysph/sph/sph_compiler.py:26`, `pysph/solver/solver.py:425`). [Confirmed] Default CPU NNPS choices include box sort, linked list, spatial hash variants, cell indexing, z-order, stratified SFC, compressed octree, and tree implementations exposed through command-line options (`pysph/solver/application.py:464`, `pysph/solver/application.py:1007`, `pysph/solver/application.py:1115`). + +### 7.2 MPI/Zoltan Workflow + +[Confirmed] If more than one process is detected, `Application` requires MPI and Zoltan support, creates a Zoltan geometric parallel manager, performs an initial partition update, and attaches the parallel manager to the solver (`pysph/solver/application.py:1275`, `pysph/solver/application.py:1286`, `pysph/solver/application.py:1302`, `pysph/solver/application.py:1344`, `pysph/solver/application.py:1356`). [Confirmed] The parallel manager removes remote particles, repartitions or migrates local particles, computes remote particles, exchanges them, and updates local/remote cell maps (`pysph/parallel/parallel_manager.pyx:512`, `pysph/parallel/parallel_manager.pyx:580`, `pysph/parallel/parallel_manager.pyx:615`, `pysph/parallel/parallel_manager.pyx:622`). [Confirmed] Parallel tests compare serial and parallel example outputs by final time and particle coordinates keyed by global id (`pysph/parallel/tests/example_test_case.py:24`, `pysph/parallel/tests/example_test_case.py:62`, `pysph/parallel/tests/example_test_case.py:144`). + +### 7.3 OpenCL/CUDA Workflow + +[Confirmed] Runtime GPU selection is command-line driven; `Application` sets `config.use_opencl`, `config.use_cuda`, `config.use_local_memory`, and `config.use_double` from parsed options (`pysph/solver/application.py:421`, `pysph/solver/application.py:922`, `pysph/solver/application.py:930`, `pysph/solver/application.py:937`). [Confirmed] `AccelerationEval` chooses `opencl` or `cuda` backend when those config flags are set and creates OpenCL/CUDA group implementations instead of Cython groups (`pysph/sph/acceleration_eval.py:166`, `pysph/sph/acceleration_eval.py:184`, `pysph/sph/equation.py:895`). [Confirmed] GPU helper code obtains OpenCL contexts/queues through Compyle and CUDA contexts through PyCUDA/Compyle, then launches generated kernels from Mako templates (`pysph/sph/acceleration_eval_gpu_helper.py:173`, `pysph/sph/acceleration_eval_gpu_helper.py:204`, `pysph/sph/acceleration_eval_gpu.mako:1`). + +### 7.4 Output/Post-Processing Workflow + +[Confirmed] Solver output files are named with `fname`, rank, and iteration count and contain solver metadata plus particle arrays (`pysph/solver/solver.py:520`, `pysph/solver/solver.py:532`, `pysph/solver/solver.py:747`). [Confirmed] `dump()` chooses HDF5 when requested and available, otherwise NPZ, and MPI collection mode writes only from rank 0 when an MPI communicator is supplied (`pysph/solver/output.py:306`, `pysph/solver/output.py:340`, `pysph/solver/output.py:53`). [Confirmed] VTK conversion and XDMF generation are separate tools built on loaded PySPH output files (`pysph/solver/vtk_output.py:15`, `pysph/solver/vtk_output.py:152`, `pysph/tools/dump_xdmf.py:130`). + +## 8. Numerics and Solver Architecture + +### 8.1 Governing Model + +[Confirmed] PySPH is not hard-wired to one PDE; it exposes arbitrary SPH equations operating on particle arrays and ships multiple published SPH formulations (`README.rst:40`, `README.rst:50`). [Confirmed] The base `Equation` class identifies a destination particle array and zero or more source arrays and exposes optional methods such as `initialize`, `loop`, `post_loop`, `reduce`, and convergence hooks through introspection (`pysph/sph/equation.py:392`, `pysph/sph/equation.py:582`, `pysph/sph/equation.py:630`). [Inferred] The governing equations are assembled as equation groups chosen by each scheme or by user code, rather than through one monolithic solver object, because `create_equations()` defaults to the scheme and `AccelerationEval` accepts grouped equations (`pysph/solver/application.py:1680`, `pysph/sph/acceleration_eval.py:14`, `pysph/sph/acceleration_eval.py:166`). + +### 8.2 Spatial Discretization + +[Confirmed] The dominant discretization model is meshfree SPH particle interaction over neighbor lists: equations are evaluated over destination/source particle arrays, NNPS returns nearest particles, and kernels are selected in solver/application setup (`pysph/sph/equation.py:392`, `pysph/base/nnps_base.pyx:1368`, `pysph/solver/application.py:950`). [Confirmed] NNPS implementations include brute-force fallback logic and acceleration structures such as linked-list cells and multiple hash/tree/SFC variants (`pysph/base/nnps_base.pyx:1325`, `pysph/base/linked_list_nnps.pyx:92`, `pysph/base/nnps.py:1`). [Confirmed] Mesh tools exist for generating/interpolating particles from triangle surfaces, but this pass found no evidence that the primary solvers are finite-volume or finite-element mesh solvers (`pysph/tools/mesh_tools.pyx:291`, `pysph/sph/equation.py:392`, `pysph/base/particle_array.pyx:68`). + +### 8.3 Equation Grouping and Generated Loops + +[Confirmed] Raw equations are normalized into `Group` objects, and group objects track iteration, NNPS updates, source/destination arrays, pre/post callbacks, and convergence conditions (`pysph/sph/acceleration_eval.py:14`, `pysph/sph/equation.py:448`, `pysph/sph/equation.py:630`). [Confirmed] `CythonGroup` generates Cython code for equation groups, array declarations, wrappers, kernel substitutions, and equation variables (`pysph/sph/equation.py:713`, `pysph/sph/equation.py:748`, `pysph/sph/equation.py:820`). [Confirmed] `OpenCLGroup` and `CUDAGroup` generate backend code by converting equation code to OpenCL/CUDA-compatible forms and respecting local-memory annotations (`pysph/sph/equation.py:895`, `pysph/sph/equation.py:937`, `pysph/sph/equation.py:959`). + +### 8.4 Representative Formulations + +| Formulation area | Implementation evidence | +| --- | --- | +| WCSPH | [Confirmed] `WCSPHScheme` configures density/equation of state, continuity, momentum, artificial viscosity, tensile correction, delta-SPH, XSPH, and smoothing-length options (`pysph/sph/scheme.py:218`, `pysph/sph/scheme.py:388`, `pysph/sph/scheme.py:508`). [Confirmed] Tait equation of state and WCSPH momentum equation live in `pysph/sph/wc/basic.py` (`pysph/sph/wc/basic.py:9`, `pysph/sph/wc/basic.py:129`). | +| Transport velocity | [Confirmed] `TVFScheme` creates a transport-velocity solver with QuinticSpline, `PECIntegrator`, and `TransportVelocityStep`, and assembles density, state equation, pressure-gradient, viscosity, no-slip, and artificial-stress equations (`pysph/sph/scheme.py:530`, `pysph/sph/scheme.py:577`, `pysph/sph/scheme.py:616`). | +| Gas dynamics | [Confirmed] `GasDScheme` supports Gaussian kernel setup, adaptive smoothing-length schemes, artificial viscosity parameters, density iterations, and gas dynamics equations (`pysph/sph/scheme.py:884`, `pysph/sph/scheme.py:985`, `pysph/sph/scheme.py:1026`). [Confirmed] Gas dynamics includes an ideal-gas EOS and Riemann solver dispatch with multiple solver variants (`pysph/sph/gas_dynamics/basic.py:222`, `pysph/sph/gas_dynamics/riemann_solver.py:19`). | +| Incompressible/pressure-correction variants | [Confirmed] README lists ISPH, SISPH, and IISPH as available formulations (`README.rst:78`, `README.rst:80`, `README.rst:82`). [Unverified] This pass did not fully trace all pressure-solve internals. | +| Solid mechanics and rigid/fluid examples | [Confirmed] README lists elastic dynamics and Akinci-style fluid/rigid coupling, and examples include solid mechanics and rigid body references (`README.rst:64`, `README.rst:94`, `README.rst:198`). [Unverified] This pass did not audit the solid-mechanics equations in detail. | + +### 8.5 Boundary Conditions + +[Confirmed] User-facing docs list generalized wall, do-nothing outlet, outlet mirror, method-of-characteristics inlet/outlet, and hybrid boundary conditions as implemented papers/formulations (`README.rst:98`, `README.rst:102`, `README.rst:106`, `README.rst:110`, `README.rst:114`). [Confirmed] Code contains classical Monaghan and Monaghan-Kajtar boundary force equations (`pysph/sph/boundary_equations.py:18`, `pysph/sph/boundary_equations.py:81`). [Confirmed] Domain managers implement periodic and mirror boundary support and create periodic/mirror ghost particles when not in parallel (`pysph/base/nnps_base.pyx:227`, `pysph/base/nnps_base.pyx:386`, `pysph/base/nnps_base.pyx:407`). [Confirmed] Inlet/outlet support is modeled through `InletInfo`, `OutletInfo`, and `InletOutletManager`, including optional ghost particle arrays (`pysph/sph/bc/inlet_outlet_manager.py:13`, `pysph/sph/bc/inlet_outlet_manager.py:53`, `pysph/sph/bc/inlet_outlet_manager.py:67`, `pysph/sph/bc/inlet_outlet_manager.py:105`). [Unknown] This pass found no evidence for structured-grid CFD concepts such as mixing planes, sliding mesh, overset mesh, or Chimera methods in `pysph`, `docs`, or `README.rst` (command: `rg -n -i "mixing.?plane|sliding.?mesh|overset|chimera" pysph docs README.rst` returned no matches). + +### 8.6 Time Integration + +[Confirmed] `Integrator` models ODE integration, stores steppers per particle array, computes timestep constraints from particle-level `dt_cfl`, `dt_force`, and `dt_visc`, and delegates runtime stages to compiled implementations (`pysph/sph/integrator.py:20`, `pysph/sph/integrator.py:62`, `pysph/sph/integrator.py:161`, `pysph/sph/integrator.py:266`). [Confirmed] Implemented integrator families include Euler, PEC, EPEC, TVDRK3, LeapFrog, and PEFRL (`pysph/sph/integrator.py:319`, `pysph/sph/integrator.py:330`, `pysph/sph/integrator.py:367`, `pysph/sph/integrator.py:426`, `pysph/sph/integrator.py:464`, `pysph/sph/integrator.py:481`). [Confirmed] The default PEC one-timestep sequence initializes, runs stage 1, updates the domain, runs a post-stage callback, computes accelerations, runs stage 2, updates the domain, and runs another post-stage callback (`pysph/sph/integrator.py:202`, `pysph/sph/integrator.py:243`). + +### 8.7 Nonlinear and Linear Solves + +[Confirmed] Equation groups support iterative evaluation with maximum/minimum iteration counts and convergence checks, so nonlinear or fixed-point-style loops can be expressed in the equation grouping layer (`pysph/sph/equation.py:448`, `pysph/sph/equation.py:630`). [Confirmed] Adaptive timestep reduction across MPI ranks is a scalar minimum reduction, not a linear-solver operation (`pysph/solver/solver.py:647`, `pysph/parallel/parallel_manager.pyx:454`). [Unknown] This pass did not identify a general-purpose sparse linear algebra subsystem comparable to PETSc/Trilinos solvers in the main runtime path; PyZoltan/Trilinos references are for partitioning/Zoltan detection (`setup.py:266`, `pysph/parallel/parallel_manager.pyx:1245`). + +### 8.8 Turbulence and Closures + +[Confirmed] The repository includes viscosity and artificial-viscosity terms in multiple schemes and equations, such as WCSPH momentum viscosity and TVF viscosity/no-slip/artificial stress (`pysph/sph/wc/basic.py:129`, `pysph/sph/scheme.py:616`). [Unknown] A focused search found no direct references to common RANS/LES model names such as Spalart, Smagorinsky, Vreman, WALE, RANS, large eddy, k-omega, or k-epsilon in `pysph`, `docs`, or `README.rst` (command: `rg -n -i "\b(spalart|smagorinsky|vreman|wale|rans|large eddy|k-omega|k-epsilon)\b" pysph docs README.rst` returned no matches). [Inferred] Turbulence modeling is not a prominent named subsystem in this codebase based on that search and the scheme survey, but this pass did not prove absence of all closure-like terms embedded under different names (`pysph/sph/scheme.py:616`, command above). + +### 8.9 Units, Nondimensionalization, and Precision + +[Unknown] This pass did not find a global unit system or nondimensionalization policy; examples set their own reference constants such as density, velocity, Mach-like speed of sound, Reynolds number, smoothing length, timestep, and final time (`pysph/examples/cavity.py:16`, `pysph/examples/cavity.py:27`, `pysph/examples/elliptical_drop.py:82`, `pysph/examples/gas_dynamics/sod_shocktube.py:12`). [Confirmed] CPU particle properties are typed C arrays such as double, long, float, int, and unsigned int, with default particle-array helper properties mostly double except integer tag/pid/gid (`pysph/base/particle_array.pyx:1020`, `pysph/base/utils.py:40`, `pysph/base/utils.py:47`). [Confirmed] GPU device arrays respect `config.use_double`, and application command-line options expose `--use-double` for OpenCL/CUDA code paths (`pysph/base/device_helper.py:47`, `pysph/solver/application.py:922`). + +## 9. Data Structures + +### 9.1 ParticleArray + +[Confirmed] `ParticleArray` is the core state container: it stores named properties, constants, output array names, stride metadata, and optional GPU helpers (`pysph/base/particle_array.pyx:68`, `pysph/base/particle_array.pyx:109`, `pysph/base/particle_array.pyx:159`). [Confirmed] Properties are dynamic and typed; `add_property()` creates typed C arrays, handles default values and stride, resizes arrays, and mirrors additions to GPU helpers when present (`pysph/base/particle_array.pyx:851`, `pysph/base/particle_array.pyx:1020`). [Confirmed] `get()` returns NumPy arrays, defaulting to real particles unless `only_real_particles=False` is supplied (`pysph/base/particle_array.pyx:704`). [Confirmed] Particle arrays can be pickled through state dictionaries of properties/constants and reconstructed from those states (`pysph/base/particle_array.pyx:179`, `pysph/base/particle_array.pyx:225`). + +### 9.2 Standard Particle Properties + +[Confirmed] `get_particle_array()` creates standard properties `x`, `y`, `z`, `u`, `v`, `w`, `m`, `h`, `rho`, `p`, `au`, `av`, `aw`, `gid`, `pid`, and `tag`, with integer-like storage for `tag`, `pid`, and unsigned `gid` (`pysph/base/utils.py:40`, `pysph/base/utils.py:47`). [Confirmed] `get_particle_array_wcsph()` adds WCSPH-oriented properties such as `cs`, acceleration components, density derivative, initial-position/state fields, divergence, and timestep constraints (`pysph/base/utils.py:152`). [Confirmed] MPI helper functions can create particle info and dummy particles for non-root ranks (`pysph/base/utils.py:466`). + +### 9.3 Domain and NNPS + +[Confirmed] `DomainManager` stores bounds, periodic and mirror axis flags, ghost-layer information, and cell-size state (`pysph/base/nnps_base.pyx:227`, `pysph/base/nnps_base.pyx:301`). [Confirmed] `NNPSBase` stores dimension, particle arrays, radius scale, ghost layers, domain manager, cache options, and sort-by-gid options (`pysph/base/nnps_base.pyx:1261`). [Confirmed] `NNPS.update()` refreshes domain/cell-size state, computes bounds, bins particles, and updates neighbor caches when enabled (`pysph/base/nnps_base.pyx:1471`). [Confirmed] Neighbor sorting can be by global id or local id (`pysph/base/nnps_base.pyx:1577`). + +### 9.4 Solver State and Output Data + +[Confirmed] `Solver` stores integrator, kernel, timestep/final-time settings, adaptivity flags, output frequency, reorder frequency, rank/fname/output directory, and callback lists (`pysph/solver/solver.py:21`, `pysph/solver/solver.py:62`, `pysph/solver/solver.py:105`, `pysph/solver/solver.py:130`). [Confirmed] Output stores solver metadata plus per-particle-array properties and output-array metadata (`pysph/solver/output.py:53`, `pysph/solver/output.py:117`, `pysph/solver/output.py:165`). + +## 10. MPI and Distributed Memory + +[Confirmed] MPI availability is gated through package-level `has_mpi`, `has_zoltan`, and `in_parallel()` helpers; `in_parallel()` returns true only when MPI and Zoltan are both available (`pysph/__init__.py:11`, `pysph/__init__.py:40`, `pysph/__init__.py:58`). [Confirmed] `Application` initializes MPI communicator/rank if `in_parallel()` is true (`pysph/solver/application.py:165`, `pysph/solver/application.py:189`). [Confirmed] Command-line parallel options expose Zoltan load-balancing method, ghost layers, load-balance frequency, debug flags, cell-size update behavior, scale factor, and parallel output mode (`pysph/solver/application.py:583`, `pysph/solver/application.py:664`). + +[Confirmed] `ParticleArrayExchange` performs load-balancing exchanges by removing exported particles, resizing arrays for imports, exchanging property buffers through Zoltan communication helpers, tagging local particles as local, and tagging remote received particles as remote (`pysph/parallel/parallel_manager.pyx:100`, `pysph/parallel/parallel_manager.pyx:159`, `pysph/parallel/parallel_manager.pyx:212`). [Confirmed] `ParallelManager.update()` removes stale remote particles, then either repartitions at load-balance frequency or migrates particles according to an existing partition (`pysph/parallel/parallel_manager.pyx:512`, `pysph/parallel/parallel_manager.pyx:580`, `pysph/parallel/parallel_manager.pyx:615`). [Confirmed] `ZoltanParallelManagerGeometric` supports RCB, RIB, and HSFC methods and builds geometric partition data from cell centroids and weights (`pysph/parallel/parallel_manager.pyx:1290`, `pysph/parallel/parallel_manager.pyx:1346`). + +[Confirmed] Remote-particle computation uses Zoltan box assignment over local cell extents and neighbor-process intersections (`pysph/parallel/parallel_manager.pyx:1159`). [Confirmed] Parallel timestep consistency uses `MPI.MIN` over local timestep candidates (`pysph/parallel/parallel_manager.pyx:454`). [Confirmed] Parallel output can be collected or distributed based on application options and solver settings (`pysph/solver/application.py:664`, `pysph/solver/solver.py:130`, `pysph/solver/output.py:24`). + +## 11. GPU and Accelerator Model + +[Confirmed] GPU acceleration is source-generated rather than handwritten CUDA C kernels in the repository: Mako templates and Python helper classes generate OpenCL/CUDA code, and a focused search for `__global__`, `cudaMalloc`, `cudaMemcpy`, `cudaStream`, `cudaEvent`, and `__device__` in `pysph` returned no matches (command: `rg -n "__global__|cudaMalloc|cudaMemcpy|cudaStream|cudaEvent|__device__" pysph`; `pysph/sph/acceleration_eval_gpu.mako:1`, `pysph/sph/acceleration_eval_gpu_helper.py:1`). [Confirmed] The GPU helper overview states OpenCL/CUDA code differs mainly in backend/NNPS handling, uses Mako templates and transpilation, stores structs/data on the GPU, and has `compute` call the generated acceleration evaluator (`pysph/sph/acceleration_eval_gpu_helper.py:1`, `pysph/sph/acceleration_eval_gpu_helper.py:14`, `pysph/sph/acceleration_eval_gpu_helper.py:31`, `pysph/sph/acceleration_eval_gpu_helper.py:70`). + +[Confirmed] `DeviceHelper` mirrors particle-array properties/constants onto `compyle.array.Array` objects, updates min/max state, and supports push/pull of device data (`pysph/base/device_helper.py:47`, `pysph/base/device_helper.py:67`, `pysph/base/device_helper.py:180`). [Confirmed] GPU integrator helpers generate kernels for steppers, select CUDA-specific launch geometry when needed, and attach `GPUIntegrator`/`CUDAIntegrator` objects to the runtime integrator (`pysph/sph/integrator_gpu_helper.py:19`, `pysph/sph/integrator_gpu_helper.py:117`, `pysph/sph/integrator_gpu_helper.py:149`). [Confirmed] GPU NNPS classes are exposed from `pysph/base/gpu_nnps.py`, and application setup chooses `OctreeGPUNNPS` for `gpu_octree` or `ZOrderGPUNNPS` for other GPU NNPS options (`pysph/base/gpu_nnps.py:1`, `pysph/solver/application.py:976`). + +[Unverified] This pass did not run OpenCL or CUDA examples, so runtime GPU correctness, device availability, and kernel compilation success are not verified here. [Confirmed] CI installs POCL and PyOpenCL on non-Windows jobs, which is evidence that OpenCL paths are intended to be exercised in automation (`.github/workflows/tests.yml:35`, `.github/workflows/tests.yml:38`). + +## 12. I/O, Restart, and Post-Processing + +[Confirmed] Output supports NPZ and HDF5 formats, with a configured compression level (`pysph/solver/output.py:13`). [Confirmed] NPZ output stores version metadata, particle metadata, solver data, and particle property arrays using `numpy.savez` or `numpy.savez_compressed` (`pysph/solver/output.py:117`). [Confirmed] HDF5 output stores solver data and particle groups through h5py (`pysph/solver/output.py:165`). [Confirmed] `load()` selects NPZ or HDF5 readers and reconstructs particle arrays and output-array metadata (`pysph/solver/output.py:270`, `pysph/solver/output.py:127`, `pysph/solver/output.py:195`). + +[Confirmed] Restart-like behavior is supported in `Application.create_particles_if_needed()`: rank 0 either creates particles or loads a restart file, then broadcasts particle-array metadata; non-root ranks create dummy particles from that metadata (`pysph/solver/application.py:859`, `pysph/solver/application.py:872`, `pysph/solver/application.py:905`). [Confirmed] `Application` writes an `.info` JSON file with solver/application metadata and completion status (`pysph/solver/application.py:1387`, `pysph/solver/application.py:1598`, `pysph/solver/application.py:1603`). [Confirmed] Profiling output can be written to `profile_info.csv`, with MPI gather support (`pysph/solver/application.py:1398`). + +[Confirmed] VTK output supports scalar and vector arrays from PySPH particle arrays and writes `.vtu` through either `pyvisfile` or `tvtk` (`pysph/solver/vtk_output.py:15`, `pysph/solver/vtk_output.py:89`, `pysph/solver/vtk_output.py:105`, `pysph/solver/vtk_output.py:123`). [Confirmed] XDMF output is generated from HDF5 files by reading solver/particle properties and rendering a Mako template (`pysph/tools/dump_xdmf.py:1`, `pysph/tools/dump_xdmf.py:130`). + +## 13. Testing and Quality Gates + +[Confirmed] Default pytest settings exclude tests marked `slow` and define `slow` and `parallel` markers (`setup.cfg:1`, `setup.cfg:3`, `setup.cfg:4`). [Confirmed] `Makefile` provides `test` for non-slow tests and `testall` for all tests under `pysph` (`Makefile:52`, `Makefile:55`). [Confirmed] Phase 1 found 37 test files under `pysph/*/tests` (command: `find pysph -path '*/tests/*' -name 'test*.py' | wc -l`). [Confirmed] CI installs test requirements and runs pytest over the full marker expression on all main OS/Python matrix jobs (`.github/workflows/tests.yml:55`, `.github/workflows/tests.yml:79`). + +[Confirmed] Parallel tests use `pytest.importorskip` for `mpi4py` and `pyzoltan`, so they are skipped when MPI/Zoltan dependencies are missing (`pysph/parallel/tests/test_parallel_run.py:17`, `pysph/parallel/tests/test_parallel_run.py:20`). [Confirmed] Parallel example tests run serial and parallel versions of examples, load their final outputs, sort by global id, and compare times and positions with tolerances (`pysph/parallel/tests/example_test_case.py:62`, `pysph/parallel/tests/example_test_case.py:128`, `pysph/parallel/tests/example_test_case.py:144`). [Confirmed] Zoltan CI explicitly runs tests marked `slow` or `parallel` after installing MPI/Zoltan dependencies (`.github/workflows/zoltan-tests.yml:64`). + +[Unverified] This report creation pass did not run the test suite. [Inferred] A meaningful verification run for code changes should include at least `make test` for CPU paths and the Zoltan workflow or local `mpirun` tests for parallel changes, because default local pytest excludes slow tests while Zoltan functionality is covered separately (`Makefile:52`, `.github/workflows/zoltan-tests.yml:64`). + +## 14. Performance and Scaling Model + +[Confirmed] Performance-sensitive loops are generated and compiled through Cython/OpenCL/CUDA backends, rather than interpreting equation loops directly in Python during production runs (`README.rst:10`, `pysph/sph/sph_compiler.py:26`, `pysph/sph/equation.py:713`, `pysph/sph/equation.py:895`). [Confirmed] OpenMP support is optional and detected at build time; application runtime options allow disabling OpenMP and selecting OpenMP scheduling behavior (`setup.py:117`, `setup.py:340`, `pysph/solver/application.py:421`). [Confirmed] Particle neighbor-search performance is configurable through multiple NNPS algorithms and optional spatial reordering (`pysph/solver/application.py:464`, `pysph/base/nnps_base.pyx:1618`, `pysph/solver/solver.py:295`). + +[Confirmed] Distributed-memory scaling relies on Zoltan cell/particle partitioning, load-balance frequency, remote ghost-particle exchange, and MPI reductions for global quantities such as timestep and bounds (`pysph/parallel/parallel_manager.pyx:512`, `pysph/parallel/parallel_manager.pyx:885`, `pysph/parallel/parallel_manager.pyx:1245`, `pysph/parallel/parallel_manager.pyx:454`). [Confirmed] Output scaling has a collected/distributed mode, and collected mode gathers arrays to rank 0 before writing (`pysph/solver/application.py:664`, `pysph/solver/output.py:24`, `pysph/solver/output.py:53`). [Inferred] Large simulations are likely sensitive to NNPS choice, output mode, load-balance frequency, ghost-layer count, and generated-code cache behavior because these are explicit runtime knobs around the hottest loops and communication paths (`pysph/solver/application.py:464`, `pysph/solver/application.py:583`, `pysph/solver/application.py:664`, `.github/workflows/tests.yml:70`). + +## 15. Configuration and Runtime Parameters + +| Scope | Parameters | Confidence and Evidence | +| --- | --- | --- | +| Time control | `--tf`, `--timestep`, `--max-steps`, `--n-damp`, adaptive/CFL options. | [Confirmed] Application parser exposes final time, timestep, max steps, damping steps, adaptive flags, and CFL (`pysph/solver/application.py:265`, `pysph/solver/application.py:304`). | +| Output | `--disable-output`, `--fname`, `--pfreq`, `--directory`, compression/detailed-output options. | [Confirmed] Application parser exposes output control and output directory options (`pysph/solver/application.py:313`, `pysph/solver/application.py:336`, `pysph/solver/application.py:398`). | +| Backend | `--no-openmp`, `--omp-schedule`, `--opencl`, `--cuda`, `--use-local-memory`, `--use-double`. | [Confirmed] Application parser exposes OpenMP, OpenCL, CUDA, local-memory, and precision options (`pysph/solver/application.py:421`, `pysph/solver/application.py:922`). | +| Kernel and NNPS | Kernel choice, NNPS choice, cache, sort GIDs, fixed smoothing length. | [Confirmed] Application parser exposes kernel choices and NNPS choices, and later applies cache/fixed-h/sort options (`pysph/solver/application.py:464`, `pysph/solver/application.py:560`). | +| Parallel | Zoltan method, ghost layers, load-balance frequency, debug, update cell sizes, scale factor, output mode. | [Confirmed] Application parser exposes these options under Zoltan and parallel groups (`pysph/solver/application.py:583`, `pysph/solver/application.py:664`). | +| Scheme-specific | WCSPH alpha/beta/delta/gamma/tensile/update-h, TVF and gas-dynamics options. | [Confirmed] Schemes add their own command-line options through `add_user_options` (`pysph/sph/scheme.py:301`, `pysph/sph/scheme.py:530`, `pysph/sph/scheme.py:940`). | +| Build | Compiler, OpenMP, MPI, Zoltan, Trilinos paths and flags. | [Confirmed] `~/.compyle/config.py` and environment variables configure these build paths (`setup.py:1`, `setup.py:32`, `docs/source/installation.rst:119`). | + +## 16. Mental Model for New Contributors + +[Inferred] The shortest useful mental model is: PySPH applications define particles and equations; schemes assemble common equation/integrator/solver choices; `Application` wires runtime options and data structures; `SPHCompiler` generates backend code; `Solver` advances time and writes particle outputs (`pysph/solver/application.py:52`, `pysph/sph/scheme.py:7`, `pysph/sph/sph_compiler.py:1`, `pysph/solver/solver.py:425`, `pysph/solver/output.py:306`). + +[Confirmed] Particle state lives in `ParticleArray` properties, and equations reference those properties by naming destination/source particle arrays and method argument symbols (`pysph/base/particle_array.pyx:68`, `pysph/sph/equation.py:392`, `pysph/sph/equation.py:582`). [Confirmed] Neighbor interactions are abstracted behind NNPS, so an equation usually does not choose the cell/hash/tree structure directly; `Application` or CLI configuration selects NNPS before solver setup (`pysph/base/nnps_base.pyx:1368`, `pysph/solver/application.py:464`, `pysph/solver/application.py:1007`). [Confirmed] Backend choice is late-bound through global config and helper classes, so the same equation source can be converted to Cython, OpenCL, or CUDA when supported (`pysph/sph/acceleration_eval.py:166`, `pysph/sph/equation.py:713`, `pysph/sph/equation.py:895`). + +## 17. Extension Guide + +### 17.1 Add a New Example + +[Confirmed] Add an `Application` subclass that implements at least `create_particles`; then create or choose a scheme, optionally add command-line options, configure the scheme, and call `app.run()` in the script main block (`pysph/solver/application.py:1706`, `pysph/examples/elliptical_drop.py:90`, `pysph/examples/elliptical_drop.py:99`, `pysph/examples/elliptical_drop.py:110`, `pysph/examples/elliptical_drop.py:223`). [Confirmed] To make it available through `pysph run`, place it under `pysph/examples` and ensure it is not one of the ignored helper/test files in the example discovery logic (`pysph/examples/run.py:49`, `pysph/examples/run.py:53`, `pysph/examples/run.py:61`). + +### 17.2 Add a New Equation + +[Confirmed] Subclass `Equation`, define destination/source usage, and implement equation methods such as `initialize`, `loop`, `post_loop`, `reduce`, or `converged`; PySPH introspects these methods and generates backend code from their signatures/bodies (`pysph/sph/equation.py:392`, `pysph/sph/equation.py:582`, `pysph/sph/equation.py:713`). [Confirmed] Ensure required particle properties exist before compilation because `AccelerationEval` validates properties referenced by equations against available particle arrays (`pysph/sph/acceleration_eval.py:32`, `pysph/sph/acceleration_eval.py:51`). + +### 17.3 Add a New Scheme + +[Confirmed] Implement the `Scheme` interface methods: `add_user_options`, `consume_user_options`, `get_equations`, `configure_solver`, `get_solver`, and `setup_properties` as appropriate (`pysph/sph/scheme.py:7`, `pysph/sph/scheme.py:23`, `pysph/sph/scheme.py:45`, `pysph/sph/scheme.py:50`, `pysph/sph/scheme.py:74`). [Confirmed] Existing schemes show the pattern: configure kernel/integrator/solver, define equation groups, and add scheme-specific particle properties (`pysph/sph/scheme.py:357`, `pysph/sph/scheme.py:388`, `pysph/sph/scheme.py:508`). + +### 17.4 Add New Output/Post-Processing + +[Confirmed] Use `pysph.solver.output.load` to read NPZ/HDF5 outputs and inspect `solver_data` plus particle arrays (`pysph/solver/output.py:270`). [Confirmed] Existing VTK and XDMF tools are good templates for converting output files to visualization formats (`pysph/solver/vtk_output.py:152`, `pysph/tools/dump_xdmf.py:81`). + +### 17.5 Add or Change MPI Behavior + +[Confirmed] MPI changes should be coordinated with `ParallelManager`, `ParticleArrayExchange`, Zoltan partition managers, and application-level parallel options (`pysph/parallel/parallel_manager.pyx:47`, `pysph/parallel/parallel_manager.pyx:343`, `pysph/parallel/parallel_manager.pyx:1040`, `pysph/solver/application.py:583`). [Confirmed] The relevant tests compare serial and parallel outputs and are skipped unless `mpi4py` and `pyzoltan` are available (`pysph/parallel/tests/example_test_case.py:24`, `pysph/parallel/tests/test_parallel_run.py:17`). + +## 18. Risks, Fragile Areas, and Open Questions + +### 18.1 Build and Dependency Risks + +[Confirmed] The build depends on Cython extension compilation, optional compiler probes, and runtime code generation, which means compiler availability and local `~/.compyle/config.py` can affect both install-time and run-time behavior (`setup.py:117`, `setup.py:720`, `docs/source/installation.rst:202`). [Confirmed] CI installs `cyarray` and `compyle` from GitHub main branches rather than only pinned released packages, which can make CI more current but also couples tests to upstream moving targets (`.github/workflows/tests.yml:58`, `.github/workflows/tests.yml:59`). [Inferred] `tox.ini` is probably not the authoritative current test matrix because it targets Python 2.7 and 3.5-3.7 while CI targets Python 3.11 and 3.12 (`tox.ini:1`, `.github/workflows/tests.yml:15`). + +### 18.2 Runtime Risks + +[Confirmed] GPU execution depends on OpenCL/CUDA context availability and generated kernels, while this pass did not execute those kernels (`pysph/sph/acceleration_eval_gpu_helper.py:173`, `pysph/sph/acceleration_eval_gpu_helper.py:223`). [Confirmed] MPI execution depends on both MPI and Zoltan availability, and `Application` raises an import error when multiple processes are used without those prerequisites (`pysph/solver/application.py:1286`). [Inferred] Changes to particle property names or equation signatures can fail at compile/setup time because `AccelerationEval` validates required properties before backend compilation (`pysph/sph/acceleration_eval.py:32`, `pysph/sph/acceleration_eval.py:51`). + +### 18.3 Design Unknowns + +[Unknown] This pass did not establish a project-wide policy for units, nondimensionalization, or reference variable naming; examples define local constants (`pysph/examples/cavity.py:16`, `pysph/examples/elliptical_drop.py:82`, `pysph/examples/gas_dynamics/sod_shocktube.py:12`). [Unknown] This pass did not audit every scheme family listed in the README, especially solid mechanics, rigid body coupling, incompressible pressure solvers, and shallow-water variants (`README.rst:64`, `README.rst:78`, `README.rst:82`, `README.rst:94`). [Unverified] This pass did not run tests, examples, MPI, OpenCL, or CUDA paths; it is a static code-and-docs understanding pass. + +### 18.4 Potential Code Smell to Recheck + +[Unverified] `Application._dump_code()` references `self.solver.sph_eval.ext_mod.code`, but `Solver.setup()` stores acceleration evaluations in `self.acceleration_evals`; this may be stale or only used in a code path not exercised in this pass (`pysph/solver/application.py:1477`, `pysph/solver/solver.py:186`, `pysph/solver/solver.py:203`). [Unverified] Package-level MPI fallback logic should be rechecked: `has_mpi()` imports `mpi4py` in one branch but the visible code path does not obviously set `_has_mpi = True` after that fallback import (`pysph/__init__.py:11`). These are review leads, not confirmed bugs. + +## 19. Recommended Reading Path + +1. [Confirmed] Start with the README for project scope, supported formulations, and basic CLI examples (`README.rst:7`, `README.rst:50`, `README.rst:149`). +2. [Confirmed] Read installation docs for compiler, MPI/Zoltan, OpenCL/CUDA, and runtime-generation requirements (`docs/source/installation.rst:39`, `docs/source/installation.rst:71`, `docs/source/installation.rst:170`, `docs/source/installation.rst:1062`). +3. [Confirmed] Read `pysph/solver/application.py` lifecycle docs and `setup()` implementation to understand orchestration (`pysph/solver/application.py:52`, `pysph/solver/application.py:1543`). +4. [Confirmed] Read a compact example such as `elliptical_drop.py`, then a boundary-heavy example such as `cavity.py`, then a gas-dynamics example such as `sod_shocktube.py` (`pysph/examples/elliptical_drop.py:82`, `pysph/examples/cavity.py:58`, `pysph/examples/gas_dynamics/sod_shocktube.py:95`). +5. [Confirmed] Read `pysph/sph/scheme.py` for how common formulations assemble particles, equations, integrators, kernels, and solver options (`pysph/sph/scheme.py:7`, `pysph/sph/scheme.py:218`, `pysph/sph/scheme.py:530`, `pysph/sph/scheme.py:884`). +6. [Confirmed] Read `pysph/sph/equation.py` and `pysph/sph/acceleration_eval.py` for the equation and group model (`pysph/sph/equation.py:392`, `pysph/sph/acceleration_eval.py:166`). +7. [Confirmed] Read `pysph/sph/sph_compiler.py` to understand generated-code compilation and backend helper selection (`pysph/sph/sph_compiler.py:1`, `pysph/sph/sph_compiler.py:61`). +8. [Confirmed] Read `pysph/sph/integrator.py` and `pysph/solver/solver.py` for timestepping and solver-loop mechanics (`pysph/sph/integrator.py:20`, `pysph/solver/solver.py:425`). +9. [Confirmed] Read `pysph/base/particle_array.pyx`, `pysph/base/utils.py`, and NNPS files for storage and neighbor-search data structures (`pysph/base/particle_array.pyx:68`, `pysph/base/utils.py:40`, `pysph/base/nnps_base.pyx:1261`). +10. [Confirmed] For MPI work, read `pysph/parallel/parallel_manager.pyx` and the parallel example tests (`pysph/parallel/parallel_manager.pyx:343`, `pysph/parallel/tests/example_test_case.py:24`). +11. [Confirmed] For GPU work, read `pysph/sph/acceleration_eval_gpu_helper.py`, `pysph/sph/integrator_gpu_helper.py`, and `pysph/sph/acceleration_eval_gpu.mako` (`pysph/sph/acceleration_eval_gpu_helper.py:1`, `pysph/sph/integrator_gpu_helper.py:19`, `pysph/sph/acceleration_eval_gpu.mako:1`). +12. [Confirmed] For output work, read `pysph/solver/output.py`, `vtk_output.py`, and `dump_xdmf.py` (`pysph/solver/output.py:306`, `pysph/solver/vtk_output.py:152`, `pysph/tools/dump_xdmf.py:81`). + +## 20. Appendices + +### 20.1 Glossary + +| Term | Meaning in this repository | +| --- | --- | +| `ParticleArray` | [Confirmed] A named collection of particles with typed properties and constants (`pysph/base/particle_array.pyx:68`). | +| `Equation` | [Confirmed] A code-generatable SPH operation with destination/source particle arrays (`pysph/sph/equation.py:392`). | +| `Group` | [Confirmed] A set of equations with real-particle, update, iteration, and convergence controls (`pysph/sph/equation.py:448`). | +| `Scheme` | [Confirmed] A reusable formulation package that supplies options, solver, equations, and particle properties (`pysph/sph/scheme.py:7`). | +| `Application` | [Confirmed] The simulation orchestration class that parses options, creates objects, configures solver state, and runs/post-processes (`pysph/solver/application.py:52`). | +| `NNPS` | [Confirmed] Nearest-neighbor particle search infrastructure (`pysph/base/nnps_base.pyx:1261`, `pysph/base/nnps_base.pyx:1368`). | +| `SPHCompiler` | [Confirmed] The compiler coordinator for acceleration evaluators and integrators (`pysph/sph/sph_compiler.py:1`). | +| `ParallelManager` | [Confirmed] MPI/Zoltan particle distribution, migration, and remote-particle exchange manager (`pysph/parallel/parallel_manager.pyx:343`). | + +### 20.2 Symbol and Property Inventory + +[Confirmed] Core particle properties are `x`, `y`, `z`, `u`, `v`, `w`, `m`, `h`, `rho`, `p`, `au`, `av`, `aw`, `gid`, `pid`, and `tag` (`pysph/base/utils.py:40`). [Confirmed] WCSPH particle arrays add `cs`, `ax`, `ay`, `az`, `arho`, `x0`, `y0`, `z0`, `u0`, `v0`, `w0`, `rho0`, `div`, `dt_cfl`, and `dt_force`, and output pressure by default (`pysph/base/utils.py:152`). [Confirmed] Integrator timestep constraints look at `dt_cfl`, `dt_force`, `dt_visc`, and optional `dt_adapt` (`pysph/sph/integrator.py:62`, `pysph/sph/integrator.py:83`). + +### 20.3 Build and Runtime Command Reference + +```bash +# [Confirmed] Developer build path (`Makefile:18`) +python setup.py build_ext --inplace + +# [Confirmed] Default local test target (`Makefile:52`) +python -m pytest -m "not slow" pysph + +# [Confirmed] Full local test target (`Makefile:55`) +python -m pytest pysph + +# [Confirmed] Example commands (`README.rst:149`, `README.rst:160`, `README.rst:186`) +pysph run elliptical_drop +pysph run dam_break_2d +pysph run cavity + +# [Confirmed] OpenCL example (`docs/source/installation.rst:1062`) +pysph run elliptical_drop --opencl + +# [Confirmed] MPI example form (`pysph/examples/run.py:146`) +mpirun -np 4 pysph run elliptical_drop +``` + +### 20.4 Phase 1 Survey Commands + +[Confirmed] The following commands were used to establish repository shape and metrics: `pwd`, `ls -la`, `tree -L 2 -d`, `du -sh */`, `find . -path ./.git -prune -o -type f ...`, `find pysph -maxdepth 3 -type f ... | wc -l`, `find pysph -path '*/tests/*' -name 'test*.py' | wc -l`, `git log -1`, `git log --reverse --max-count=1`, `git log --since='1 year ago' --name-only --pretty=format:`, and targeted `rg` searches for build files, entry points, GPU markers, mesh-interface terms, and turbulence-model names. [Confirmed] The latest commit observed in this pass was `69be1c30` dated 2025-10-26, and the first commit observed was `2f68a8da` dated 2013-02-21 (commands: `git log -1 --format=...`, `git log --reverse --max-count=1 --format=...`). + +### 20.5 Verification Notes + +[Confirmed] This document was produced after a two-phase static survey: broad repository inventory first, then targeted reading of build, CLI, application, solver, equation, integrator, particle, NNPS, MPI, GPU, I/O, scheme, example, and test files. [Unverified] No runtime tests, examples, MPI jobs, OpenCL jobs, or CUDA jobs were executed during this report creation. [Unknown] Any behavior depending on local compiler configuration, installed GPU drivers, MPI launcher environment, or Zoltan library layout should be validated on the target machine before changing production workflows (`docs/source/installation.rst:119`, `docs/source/installation.rst:170`, `docs/source/installation.rst:219`). diff --git a/docs/source/index.rst b/docs/source/index.rst index 3b32a21df..976a20614 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -49,6 +49,7 @@ Installation and getting started installation.rst tutorial/circular_patch_simple.rst tutorial/circular_patch.rst + tutorial/warp_particle_array.rst *************************** diff --git a/docs/source/tutorial/warp_particle_array.rst b/docs/source/tutorial/warp_particle_array.rst new file mode 100644 index 000000000..f602a36c2 --- /dev/null +++ b/docs/source/tutorial/warp_particle_array.rst @@ -0,0 +1,169 @@ +Using Warp-backed particle arrays +================================= + +PySPH can create a ParticleArray with a NVIDIA Warp-backed device mirror by +passing ``backend='warp'`` to :func:`pysph.base.utils.get_particle_array`. +The host ParticleArray remains the public object you use from Python and +Cython. The Warp mirror stores property and constant arrays on the selected +Warp device and is available as ``pa.gpu``. + +This is an experimental backend intended for ParticleArray storage and mutation +work. It does not migrate SPH equations, integrators, or NNPS kernels to Warp. + +Creating a Warp-backed ParticleArray +------------------------------------ + +Create the array as usual, adding ``backend='warp'``: + +.. code-block:: python + + from pysph.base.utils import get_particle_array + + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0, 2.0], + y=[0.0, 0.0, 0.0], + h=0.1, + m=1.0, + rho=1000.0, + backend='warp', + ) + + print(pa.backend) # warp + print(pa.gpu.x.get()) # Warp device mirror copied back to NumPy + +The object returned by ``get_particle_array`` is still a regular +ParticleArray. The Warp-specific part is the device helper attached to +``pa.gpu``. + +Synchronizing host and device data +---------------------------------- + +The host arrays and the Warp mirror are intentionally separate. If you mutate +host arrays directly, push the changed properties to the Warp mirror: + +.. code-block:: python + + pa.x[:] = [3.0, 4.0, 5.0] + pa.tag[:] = [0, 1, 0] + + pa.gpu.push('x', 'tag') + +Use ``push()`` with no arguments to copy all mirrored properties and constants. + +If you mutate the Warp mirror directly, pull the changed arrays back to the +host: + +.. code-block:: python + + pa.gpu.x[:] = 10.0 + pa.gpu.pull('x') + + assert (pa.x == [10.0, 10.0, 10.0]).all() + +Use ``pull()`` with no arguments to copy all mirrored properties and constants +back to the host ParticleArray. + +Working with local, remote, and ghost particles +----------------------------------------------- + +The ``tag`` property has the same meaning as in a normal ParticleArray: +``0`` is local, ``1`` is remote, and ``2`` is ghost. Calling +``align_particles`` partitions local particles first and updates the real +particle count: + +.. code-block:: python + + pa.tag[:] = [1, 0, 2, 0] + pa.gpu.push('tag') + + pa.align_particles() + + print(pa.get_number_of_particles(real=True)) # 2 + print(pa.get('x')) # local particles only + +The Warp backend supports strided properties during alignment: + +.. code-block:: python + + pa.add_property( + 'force', + data=[0.0, 0.1, 0.2, + 1.0, 1.1, 1.2, + 2.0, 2.1, 2.2], + stride=3, + ) + + pa.align_particles() + +Particle mutations +------------------ + +The Warp mirror supports the same first-level ParticleArray mutation methods +for particle-table work: + +.. code-block:: python + + pa.add_particles(x=[6.0, 7.0], tag=[1, 0]) + pa.remove_tagged_particles(1) + pa.remove_particles([0], align=False) + +Appending and extracting ParticleArrays also works: + +.. code-block:: python + + wall = get_particle_array(name='wall', x=[8.0, 9.0], backend='warp') + pa.append_parray(wall) + + subset = pa.extract_particles([0, 2], props=['x']) + +When you request specific properties during extraction, PySPH still preserves +its baseline system properties such as ``tag``, ``pid``, and ``gid``. + +Constants and additional properties +----------------------------------- + +Constants are mirrored to Warp and remain fixed-size arrays: + +.. code-block:: python + + pa.add_constant('gravity', [0.0, -9.81, 0.0]) + print(pa.gpu.gravity.get()) + +New properties are mirrored when they are added: + +.. code-block:: python + + pa.add_property('temperature', data=[300.0] * pa.get_number_of_particles()) + print(pa.gpu.temperature.get()) + +Precision +--------- + +Floating-point properties follow Compyle's ``use_double`` configuration when +the Warp mirror is created. With ``use_double=True``, float properties are +mirrored as ``float64``. With ``use_double=False``, they are mirrored as +``float32``. + +Example: + +.. code-block:: python + + from compyle.config import get_config + + cfg = get_config() + cfg.use_double = False + + pa = get_particle_array(name='fluid', x=[0.0, 1.0], backend='warp') + print(pa.gpu.x.dtype) # float32 + +Current limitations +------------------- + +The Warp backend is currently a ParticleArray device mirror. Host +``BaseArray`` storage remains authoritative for the public ParticleArray API. +The first implementation focuses on correctness for property storage, +synchronization, alignment, constants, and particle-table mutations. + +Equation evaluation, integrators, NNPS kernels, and full solver execution are +not migrated to Warp by this backend. diff --git a/pysph/base/particle_array.pyx b/pysph/base/particle_array.pyx index b19a96111..16c2af9c5 100644 --- a/pysph/base/particle_array.pyx +++ b/pysph/base/particle_array.pyx @@ -33,6 +33,12 @@ cdef extern from "limits.h": _UINT_MAX = UINT_MAX + +def _get_particle_array_backend(backend): + if backend == 'warp': + return 'warp' + return get_backend(backend) + # Declares various tags for particles, and functions to check them. # Note that these tags are the ones set in the 'tag' property of the @@ -129,7 +135,7 @@ cdef class ParticleArray: for each property. """ - self.backend = get_backend(backend) + self.backend = _get_particle_array_backend(backend) self.time = 0.0 self.name = name @@ -150,7 +156,11 @@ cdef class ParticleArray: # list of output property arrays self.output_property_arrays = [] - if self.backend is not 'cython': + if self.backend == 'warp': + from pysph.base.warp_device_helper import WarpDeviceHelper + h = WarpDeviceHelper(self, backend=self.backend) + self.set_device_helper(h) + elif self.backend is not 'cython': h = DeviceHelper(self, backend=self.backend) self.set_device_helper(h) else: @@ -296,7 +306,7 @@ cdef class ParticleArray: # `Public` interface ###################################################################### def update_backend(self, backend=None): - self.backend = get_backend(backend) + self.backend = _get_particle_array_backend(backend) def set_output_arrays(self, list props): """Set the list of output arrays for this ParticleArray @@ -423,7 +433,7 @@ cdef class ParticleArray: cpdef int get_number_of_particles(self, bint real=False): """ Return the number of particles """ if self.gpu is not None and self.backend is not 'cython': - return self.gpu.get_number_of_particles() + return self.gpu.get_number_of_particles(real) if real: return self.num_real_particles else: @@ -465,6 +475,10 @@ cdef class ParticleArray: """ if self.gpu is not None and self.backend is not 'cython': + if self.backend == 'warp': + if isinstance(indices, BaseArray): + indices = indices.get_npy_array() + return self.gpu.remove_particles(indices, align=align) if type(indices) != Array: if isinstance(indices, BaseArray): indices = indices.get_npy_array() @@ -559,6 +573,8 @@ cdef class ParticleArray: self._check_property(prop) if self.gpu is not None and self.backend is not 'cython': + if self.backend == 'warp': + return self.gpu.add_particles(align=align, **particle_props) gpu_particle_props = {} for prop, ary in particle_props.items(): if prop in self.gpu.properties: @@ -1269,6 +1285,9 @@ cdef class ParticleArray: """ if self.gpu is not None and self.backend is not 'cython': + if self.backend == 'warp': + return self.gpu.extract_particles(indices, dest_array=dest_array, + align=align, props=props) if type(indices) != Array: indices = to_device( numpy.array(indices, dtype=numpy.uint32), diff --git a/pysph/base/tests/test_warp_codegen.py b/pysph/base/tests/test_warp_codegen.py new file mode 100644 index 000000000..a933aac75 --- /dev/null +++ b/pysph/base/tests/test_warp_codegen.py @@ -0,0 +1,355 @@ +"""Focused tests for the dynamic Warp equation-group code generator (ADR-0003). + +These exercise the generator mechanism in isolation: that a kernel templated +from equation-block snippets compiles, runs, and is cached, that a generated +kernel resolves and calls the existing device ``wp.func`` helpers, and that the +unioned signature/metadata is correct. Physics parity of the real SPH blocks +against the hand-written kernels lives in ``test_warp_sph.py``. +""" + +import numpy as np +import pytest + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +pytest.importorskip('warp') + +import warp as wp + +from pysph.base.kernels import CubicSpline +from pysph.base.warp_codegen import ( + WarpEquation, build_group_kernel, clear_kernel_cache, + generate_group_source, +) +import pysph.base.warp_sph as ws + + +def _device(): + return wp.get_device() + + +def _arr(values, dtype): + return wp.array(np.asarray(values, dtype=dtype), device=_device()) + + +class _SumMass(WarpEquation): + """Toy block: accumulate neighbor mass (no geometry, no wp.func calls).""" + src_arrays = ('m',) + out_arrays = ('total',) + + def loop(self): + return " _acc_total += s_m[j]" + + +class _KernelSum(WarpEquation): + """Toy block: accumulate the SPH kernel value over neighbors. + + Exercises shared-geometry emission and a generated call into the existing + device ``_kernel_value_*`` ``wp.func``. + """ + out_arrays = ('wsum',) + requires = ('rij', 'hij', 'wij') + + def loop(self): + return " _acc_wsum += wij" + + +def _launch_manual(group, arrays, dim, kernel_id, scalars=None): + """Bind device arrays/scalars in the generator's canonical order.""" + scalars = scalars or {} + inputs = [arrays['s_' + n] for n in group.src_names] + inputs += [arrays['d_' + n] for n in group.dst_names] + inputs += [arrays['starts'], arrays['lengths'], arrays['neighbors']] + inputs += [np.int32(dim), np.int32(kernel_id)] + inputs += [group.dtype(scalars[n]) for n in group.scalar_names] + inputs += [arrays['d_' + n] for n in group.out_names] + n = arrays['_n'] + wp.launch(group.kernel, dim=n, inputs=inputs, device=_device()) + wp.synchronize_device(_device()) + + +def _launch_grid_manual(group, arrays, dim, kernel_id, scalars=None): + """Bind device arrays/scalars for a ``grid`` kernel in canonical order.""" + scalars = scalars or {} + inputs = [arrays['s_' + n] for n in group.src_names] + inputs += [arrays['d_' + n] for n in group.dst_names] + inputs += [ + arrays['cell_starts'], arrays['cell_counts'], arrays['cell_particles'], + group.dtype(arrays['xmin']), group.dtype(arrays['ymin']), + group.dtype(arrays['zmin']), group.dtype(arrays['cell_size']), + np.int32(arrays['nx']), np.int32(arrays['ny']), np.int32(arrays['nz']), + np.int32(arrays['ncells']), group.dtype(arrays['radius_scale']), + ] + inputs += [np.int32(dim), np.int32(kernel_id)] + inputs += [group.dtype(scalars[n]) for n in group.scalar_names] + inputs += [arrays['d_' + n] for n in group.out_names] + wp.launch(group.kernel, dim=arrays['_n'], inputs=inputs, device=_device()) + wp.synchronize_device(_device()) + + +def test_generated_group_kernel_compiles_runs_and_is_cached(): + clear_kernel_cache() + group = build_group_kernel([_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS) + assert group.src_names == ['m'] + assert group.out_names == ['total'] + assert group.scalar_names == [] + + # 3 particles, each neighboring all three. + arrays = { + 's_m': _arr([1.0, 2.0, 3.0], np.float32), + 'starts': _arr([0, 3, 6], np.int32), + 'lengths': _arr([3, 3, 3], np.int32), + 'neighbors': _arr([0, 1, 2, 0, 1, 2, 0, 1, 2], np.uint32), + 'd_total': wp.zeros(3, dtype=wp.float32, device=_device()), + '_n': 3, + } + _launch_manual(group, arrays, dim=2, kernel_id=0) + total = arrays['d_total'].numpy() + assert np.allclose(total, [6.0, 6.0, 6.0]) + + # Building the same group again returns the identical cached object. + group2 = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS + ) + assert group2 is group + + +def test_generated_kernel_calls_existing_device_wp_func(): + clear_kernel_cache() + group = build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS + ) + # Geometry auto-adds x, y, z, h to both source and destination signature. + assert group.src_names == ['x', 'y', 'z', 'h'] + assert group.dst_names == ['x', 'y', 'z', 'h'] + + r = 0.3 + h = 0.35 + arrays = { + 's_x': _arr([0.0, r], np.float32), 's_y': _arr([0.0, 0.0], np.float32), + 's_z': _arr([0.0, 0.0], np.float32), + 's_h': _arr([h, h], np.float32), + 'd_x': _arr([0.0, r], np.float32), 'd_y': _arr([0.0, 0.0], np.float32), + 'd_z': _arr([0.0, 0.0], np.float32), + 'd_h': _arr([h, h], np.float32), + 'starts': _arr([0, 1], np.int32), + 'lengths': _arr([1, 1], np.int32), + 'neighbors': _arr([1, 0], np.uint32), + 'd_wsum': wp.zeros(2, dtype=wp.float32, device=_device()), + '_n': 2, + } + _launch_manual(group, arrays, dim=2, kernel_id=0) + wsum = arrays['d_wsum'].numpy() + + cpu = CubicSpline(dim=2) + expected = cpu.kernel([r, 0.0, 0.0], r, 0.5 * (h + h)) + assert np.allclose(wsum, [expected, expected], rtol=1e-5, atol=1e-6) + + +def test_generator_unions_signature_and_scalars_in_order(): + group = build_group_kernel( + ws._WCSPH_CONTINUITY_BLOCKS, np.float64, ws._WARP_DEVICE_FUNCS + ) + # Distinct outputs from all four blocks, in block order. + assert group.out_names == ['au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'] + # Scalars collected across blocks in block order, de-duplicated. + assert group.scalar_names == ['alpha', 'beta', 'eps'] + # Velocity + geometry arrays are auto-added and shared (no duplicates). + for name in ('x', 'y', 'z', 'h', 'u', 'v', 'w', 'm', 'rho', 'p', 'cs'): + assert name in group.src_names + assert len(group.src_names) == len(set(group.src_names)) + + +def test_grid_neighbor_mode_caches_distinctly_and_forces_geometry(): + # ADR-0004: grid mode is part of the structural cache key (distinct from + # flat) and forces positions + h into the signature even for a block that + # declares no geometry, because the support cutoff needs them. + clear_kernel_cache() + flat = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS, neighbor_mode='flat' + ) + grid = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS, neighbor_mode='grid' + ) + assert flat.neighbor_mode == 'flat' + assert grid.neighbor_mode == 'grid' + assert grid is not flat + for n in ('x', 'y', 'z', 'h'): + assert n in grid.src_names and n in grid.dst_names + assert 'x' not in flat.src_names # flat keeps the minimal signature + grid2 = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS, neighbor_mode='grid' + ) + assert grid2 is grid + + +def test_grid_mode_kernel_sum_matches_reference_single_cell(): + # A grid kernel over a single cell holding both particles must visit each + # particle's self-pair and its in-cutoff neighbor, calling the device + # kernel-value wp.func through the post-cutoff geometry. + clear_kernel_cache() + group = build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS, neighbor_mode='grid' + ) + r = 0.3 + h = 0.35 + arrays = { + 's_x': _arr([0.0, r], np.float32), 's_y': _arr([0.0, 0.0], np.float32), + 's_z': _arr([0.0, 0.0], np.float32), 's_h': _arr([h, h], np.float32), + 'd_x': _arr([0.0, r], np.float32), 'd_y': _arr([0.0, 0.0], np.float32), + 'd_z': _arr([0.0, 0.0], np.float32), 'd_h': _arr([h, h], np.float32), + 'cell_starts': _arr([0], np.int32), + 'cell_counts': _arr([2], np.int32), + 'cell_particles': _arr([0, 1], np.uint32), + 'xmin': -1.0, 'ymin': -1.0, 'zmin': -1.0, 'cell_size': 10.0, + 'nx': 1, 'ny': 1, 'nz': 1, 'ncells': 1, 'radius_scale': 2.0, + 'd_wsum': wp.zeros(2, dtype=wp.float32, device=_device()), + '_n': 2, + } + _launch_grid_manual(group, arrays, dim=2, kernel_id=0) + wsum = arrays['d_wsum'].numpy() + + cpu = CubicSpline(dim=2) + w_self = cpu.kernel([0.0, 0.0, 0.0], 0.0, h) + w_pair = cpu.kernel([r, 0.0, 0.0], r, h) + expected = w_self + w_pair + assert np.allclose(wsum, [expected, expected], rtol=1e-5, atol=1e-6) + + +def test_grid_mode_kernel_sum_matches_reference_in_3d_across_z_cells(): + # dim=3 coverage of the generated grid path (ADR-0005 pin): a 1x1x2 cell + # grid (nz=2) with one particle per z-layer forces the 27-cell triple loop + # to reach an adjacent cell via dzc=+/-1 and to form the 3D linear cell id + # cid = ix + iy*nx + iz*nx*ny -- the iz*nx*ny term is exercised only in 3D. + clear_kernel_cache() + group = build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS, neighbor_mode='grid' + ) + h = 0.35 + cs = 0.7 # cell_size = radius_scale*h, so the z-neighbor one + z1 = 0.5 # cell away (dz=0.5 < cutoff 0.7) is found by the walk + arrays = { + 's_x': _arr([0.0, 0.0], np.float32), 's_y': _arr([0.0, 0.0], np.float32), + 's_z': _arr([0.0, z1], np.float32), 's_h': _arr([h, h], np.float32), + 'd_x': _arr([0.0, 0.0], np.float32), 'd_y': _arr([0.0, 0.0], np.float32), + 'd_z': _arr([0.0, z1], np.float32), 'd_h': _arr([h, h], np.float32), + # particle 0 -> cell (0,0,0)=cid 0; particle 1 -> cell (0,0,1)=cid 1 + 'cell_starts': _arr([0, 1], np.int32), + 'cell_counts': _arr([1, 1], np.int32), + 'cell_particles': _arr([0, 1], np.uint32), + 'xmin': -0.35, 'ymin': -0.35, 'zmin': -0.35, 'cell_size': cs, + 'nx': 1, 'ny': 1, 'nz': 2, 'ncells': 2, 'radius_scale': 2.0, + 'd_wsum': wp.zeros(2, dtype=wp.float32, device=_device()), + '_n': 2, + } + _launch_grid_manual(group, arrays, dim=3, kernel_id=0) + wsum = arrays['d_wsum'].numpy() + + cpu = CubicSpline(dim=3) + w_self = cpu.kernel([0.0, 0.0, 0.0], 0.0, h) + w_pair = cpu.kernel([0.0, 0.0, z1], z1, h) + expected = w_self + w_pair + assert np.allclose(wsum, [expected, expected], rtol=1e-5, atol=1e-6) + + +def test_accumulate_outputs_adds_to_existing_output(): + # accumulate_outputs=True seeds _acc from the existing d_[i] so the + # group adds to (read-modify-writes) the destination arrays instead of + # overwriting; it is a distinct cached kernel from the overwrite variant. + clear_kernel_cache() + overwrite = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS, + accumulate_outputs=False, + ) + accumulate = build_group_kernel( + [_SumMass()], np.float32, ws._WARP_DEVICE_FUNCS, + accumulate_outputs=True, + ) + assert accumulate is not overwrite + + # 3 particles, each neighboring all three; sum of masses is 6. Seed the + # output with 10 so accumulate yields 16 (overwrite would yield 6). + arrays = { + 's_m': _arr([1.0, 2.0, 3.0], np.float32), + 'starts': _arr([0, 3, 6], np.int32), + 'lengths': _arr([3, 3, 3], np.int32), + 'neighbors': _arr([0, 1, 2, 0, 1, 2, 0, 1, 2], np.uint32), + 'd_total': _arr([10.0, 10.0, 10.0], np.float32), + '_n': 3, + } + _launch_manual(accumulate, arrays, dim=2, kernel_id=0) + assert np.allclose(arrays['d_total'].numpy(), [16.0, 16.0, 16.0]) + + +def test_periodic_grid_kernel_compiles_and_is_distinct(): + # The periodic (minimum-image) variant is a distinct cached kernel from the + # non-periodic one and only valid in grid mode. + clear_kernel_cache() + nonper = build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS, + neighbor_mode='grid', periodic=False, + ) + per = build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS, + neighbor_mode='grid', periodic=True, + ) + assert per is not nonper + assert 'box_lx' in per.source and 'box_lx' not in nonper.source + with pytest.raises(ValueError): + build_group_kernel( + [_KernelSum()], np.float32, ws._WARP_DEVICE_FUNCS, + neighbor_mode='flat', periodic=True, + ) + + +def test_unknown_shared_quantity_is_rejected(): + class _Bad(WarpEquation): + out_arrays = ('q',) + requires = ('bogus',) + + def loop(self): + return " _acc_q += TYPE(1.0)" + + with pytest.raises(ValueError): + build_group_kernel([_Bad()], np.float32, ws._WARP_DEVICE_FUNCS) + + +def test_2d_path_generated_source_is_byte_identical_to_golden(): + # ADR-0005 cache-stability guard. Adding the WendlandQuintic kernel id (2) + # must NOT perturb the generated source for the 2D elliptical-drop kernels: + # the kernel choice is a *runtime* kernel_id, so the SummationDensity and + # the fused WCSPH-continuity group each emit one shared source string, + # independent of which kernel is used. Warp's on-disk cache for those + # kernels is keyed by that source, so a stable source == stable 2D baseline + # and cache. These md5s were captured pre-Wendland; any future edit that + # would change the 2D-path emitted source (and bust its cache / perturb the + # committed elliptical-drop baseline) fails here. func_name is pinned so the + # digest reflects only the equation snippets + geometry emission. + import hashlib + + golden = { + ('summation', 'flat'): 'c473f1a544a6bbf31eb70ebafe7a3399', + ('summation', 'grid'): 'edd7cbd57050ca10cbfe4064f5ff7de7', + ('wcsph_continuity', 'flat'): '9a568b201d9faac1e41bda0350a2e2fb', + ('wcsph_continuity', 'grid'): 'eff64c63fbab32768d3bd14a7eb0928d', + } + groups = { + 'summation': [ws.SummationDensity()], + 'wcsph_continuity': list(ws._WCSPH_CONTINUITY_BLOCKS), + } + for name, eqs in groups.items(): + for mode in ('flat', 'grid'): + src, *_ = generate_group_source( + eqs, np.float32, func_name='_golden', neighbor_mode=mode + ) + digest = hashlib.md5(src.encode()).hexdigest() + assert digest == golden[(name, mode)], ( + "%s/%s 2D-path source changed (cache-bust / baseline risk): " + "%s != %s" % (name, mode, digest, golden[(name, mode)]) + ) + # The 2D path never mentions Wendland; the kernel router resolves + # ids at runtime, so the emitted source must stay kernel-agnostic. + assert 'wendland' not in src diff --git a/pysph/base/tests/test_warp_device_helper.py b/pysph/base/tests/test_warp_device_helper.py new file mode 100644 index 000000000..e7da99203 --- /dev/null +++ b/pysph/base/tests/test_warp_device_helper.py @@ -0,0 +1,299 @@ +import pytest +import numpy as np +import setuptools # noqa: F401 + +pytest.importorskip('warp') +pytest.importorskip('pysph.base.particle_array') + +from compyle.config import get_config # noqa: E402 +from pysph.base.utils import get_particle_array # noqa: E402 +from pysph.base.warp_device_helper import WarpDeviceHelper # noqa: E402 + + +class TestWarpDeviceHelper(object): + def setup_method(self): + self.pa = get_particle_array(name='f', x=[0.0, 1.0], m=1.0, rho=2.0) + + def test_simple(self): + pa = self.pa + h = WarpDeviceHelper(pa) + pa.set_device_helper(h) + + assert np.allclose(pa.x, h.x.get()) + assert np.allclose(pa.y, h.y.get()) + assert np.allclose(pa.m, h.m.get()) + assert np.allclose(pa.rho, h.rho.get()) + assert np.allclose(pa.tag, h.tag.get()) + + def test_push_and_pull_selected_properties(self): + pa = self.pa + h = WarpDeviceHelper(pa) + pa.set_device_helper(h) + + pa.x[:] = [2.0, 3.0] + pa.rho[0] = 1.0 + pa.tag[:] = 1 + h.push('x', 'rho', 'tag') + + assert np.allclose(pa.x, h.x.get()) + assert np.allclose(pa.rho, h.rho.get()) + assert np.allclose(pa.tag, h.tag.get()) + + h.x.set(np.array([4.0, 5.0], h.x.dtype)) + h.rho[1] = 7.0 + h.tag[:] = np.array([0, 1], h.tag.dtype) + h.pull('x', 'rho', 'tag') + + assert np.allclose(pa.x, [4.0, 5.0]) + assert np.allclose(pa.rho, [1.0, 7.0]) + assert np.allclose(pa.tag, [0, 1]) + + def test_push_and_pull_all_properties(self): + pa = self.pa + h = WarpDeviceHelper(pa) + pa.set_device_helper(h) + + pa.x[:] = [2.0, 3.0] + pa.y[:] = [4.0, 5.0] + pa.rho[:] = [6.0, 7.0] + pa.tag[:] = [1, 0] + h.push() + + assert np.allclose(h.x.get(), [2.0, 3.0]) + assert np.allclose(h.y.get(), [4.0, 5.0]) + assert np.allclose(h.rho.get(), [6.0, 7.0]) + assert np.allclose(h.tag.get(), [1, 0]) + + h.x[:] = 8.0 + h.y[:] = 9.0 + h.rho[:] = 10.0 + h.tag[:] = np.array([0, 1], h.tag.dtype) + h.pull() + + assert np.allclose(pa.x, [8.0, 8.0]) + assert np.allclose(pa.y, [9.0, 9.0]) + assert np.allclose(pa.rho, [10.0, 10.0]) + assert np.allclose(pa.tag, [0, 1]) + + def test_float_precision_follows_config(self): + cfg = get_config() + old_use_double = cfg.use_double + try: + cfg.use_double = False + pa = get_particle_array(name='f', x=[0.0, 1.0], + backend='warp') + + assert pa.gpu.x.dtype == np.dtype(np.float32) + finally: + cfg.use_double = old_use_double + + def test_align_particles(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + y=[10.0, 11.0, 12.0, 13.0]) + pa.add_property('force', data=[0.0, 0.1, 0.2, + 1.0, 1.1, 1.2, + 2.0, 2.1, 2.2, + 3.0, 3.1, 3.2], + stride=3) + pa.tag[:] = [1, 0, 2, 0] + + h = WarpDeviceHelper(pa) + pa.set_device_helper(h) + h.align_particles() + h.pull() + + assert pa.get_number_of_particles(real=True) == 2 + assert np.allclose(pa.x, [1.0, 3.0]) + assert np.allclose(pa.y, [11.0, 13.0]) + assert np.allclose(pa.get('force'), [1.0, 1.1, 1.2, + 3.0, 3.1, 3.2]) + + def test_particle_array_can_create_warp_backend(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + + assert isinstance(pa.gpu, WarpDeviceHelper) + assert np.allclose(pa.x, pa.gpu.x.get()) + + def test_property_and_constant_updates_are_mirrored(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + + pa.add_property('test', data=[3.0, 4.0]) + assert np.allclose(pa.test, pa.gpu.test.get()) + + pa.add_constant('alpha', [0.25, 0.5]) + assert np.allclose(pa.constants['alpha'].get_npy_array(), + pa.gpu.alpha.get()) + + pa.remove_property('test') + assert not hasattr(pa.gpu, 'test') + assert 'test' not in pa.gpu._data + assert 'test' not in pa.gpu.properties + + def test_particle_array_align_and_property_readback(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + y=[10.0, 11.0, 12.0, 13.0], + backend='warp') + pa.add_property('force', data=[0.0, 0.1, 0.2, + 1.0, 1.1, 1.2, + 2.0, 2.1, 2.2, + 3.0, 3.1, 3.2], + stride=3) + pa.tag[:] = [1, 0, 2, 0] + pa.gpu.push('tag') + + pa.align_particles() + props = pa.get_property_arrays(all=True, only_real=False) + + assert pa.get_number_of_particles(real=True) == 2 + assert np.allclose(props['x'], [1.0, 3.0, 0.0, 2.0]) + assert np.allclose(props['y'], [11.0, 13.0, 10.0, 12.0]) + assert np.allclose(props['force'], [1.0, 1.1, 1.2, + 3.0, 3.1, 3.2, + 0.0, 0.1, 0.2, + 2.0, 2.1, 2.2]) + + def test_particle_array_only_real_readback(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + backend='warp') + pa.tag[:] = [1, 0, 2, 0] + pa.gpu.push('tag') + + pa.align_particles() + props = pa.get_property_arrays(all=True, only_real=True) + + assert np.allclose(props['x'], [1.0, 3.0]) + assert np.allclose(props['tag'], [0, 0]) + + def test_particle_array_remove_particles(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + backend='warp') + pa.tag[:] = [0, 1, 2, 0] + pa.gpu.push('tag') + + pa.remove_particles([1], align=False) + props = pa.get_property_arrays(all=True, only_real=False) + + assert np.allclose(props['x'], [0.0, 2.0, 3.0]) + assert np.allclose(props['tag'], [0, 2, 0]) + + def test_remove_particles_raises_for_too_many_indices(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + + with pytest.raises(ValueError): + pa.remove_particles([0, 1, 2]) + + def test_particle_array_remove_tagged_particles(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + backend='warp') + pa.tag[:] = [0, 1, 2, 0] + pa.gpu.push('tag') + + pa.remove_tagged_particles(2, align=True) + props = pa.get_property_arrays(all=True, only_real=False) + + assert pa.get_number_of_particles(real=True) == 2 + assert np.allclose(props['x'][:2], [0.0, 3.0]) + assert np.allclose(props['tag'][:2], [0, 0]) + + def test_particle_array_add_particles(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], y=[5.0, 6.0], + backend='warp') + + pa.add_particles(x=[2.0, 3.0], tag=[1, 0], align=True) + props = pa.get_property_arrays(all=True, only_real=False) + + assert pa.get_number_of_particles(real=True) == 3 + assert np.allclose(props['x'], [0.0, 1.0, 3.0, 2.0]) + assert np.allclose(props['y'], [5.0, 6.0, 0.0, 0.0]) + assert np.allclose(props['tag'], [0, 0, 0, 1]) + + def test_particle_array_extend_and_resize(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + + pa.extend(2) + assert pa.gpu.get_number_of_particles() == 4 + assert np.allclose(pa.gpu.x.get(), [0.0, 1.0, 0.0, 0.0]) + assert np.allclose(pa.gpu.tag.get(), [0, 0, 0, 0]) + + pa.gpu.resize(2) + assert pa.gpu.get_number_of_particles() == 2 + assert np.allclose(pa.gpu.x.get(), [0.0, 1.0]) + + def test_particle_array_append_parray(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + other = get_particle_array(name='g', x=[2.0, 3.0]) + other.tag[:] = [1, 0] + + pa.append_parray(other, align=True) + props = pa.get_property_arrays(all=True, only_real=False) + + assert pa.get_number_of_particles(real=True) == 3 + assert np.allclose(props['x'], [0.0, 1.0, 3.0, 2.0]) + assert np.allclose(props['tag'], [0, 0, 0, 1]) + + def test_append_parray_adds_missing_properties_and_constants(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp') + other = get_particle_array(name='g', x=[2.0, 3.0], + temp=[20.0, 30.0], + constants={'alpha': [0.25, 0.5]}) + + pa.append_parray(other, align=False, update_constants=True) + props = pa.get_property_arrays(all=True, only_real=False) + + assert np.allclose(props['x'], [0.0, 1.0, 2.0, 3.0]) + assert np.allclose(props['temp'], [0.0, 0.0, 20.0, 30.0]) + assert np.allclose(pa.gpu.alpha.get(), [0.25, 0.5]) + + def test_empty_clone_preserves_schema_and_constants(self): + pa = get_particle_array(name='f', x=[0.0, 1.0], backend='warp', + constants={'alpha': [0.25, 0.5]}) + pa.add_property('force', stride=3) + pa.set_output_arrays(['x', 'force']) + + clone = pa.gpu.empty_clone() + + assert clone.name == 'f' + assert clone.gpu.get_number_of_particles() == 0 + assert clone.stride['force'] == 3 + assert 'alpha' in clone.gpu.constants + assert np.allclose(clone.gpu.alpha.get(), [0.25, 0.5]) + assert set(clone.output_property_arrays) == set(['x', 'force']) + + def test_particle_array_extract_particles(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + backend='warp') + pa.tag[:] = [1, 0, 2, 0] + pa.gpu.push('tag') + + result = pa.extract_particles([1, 3], align=True) + props = result.get_property_arrays(all=True, only_real=False) + + assert result.get_number_of_particles(real=True) == 2 + assert np.allclose(props['x'], [1.0, 3.0]) + assert np.allclose(props['tag'], [0, 0]) + + def test_extract_particles_with_specific_strided_props(self): + pa = get_particle_array(name='f', x=[0.0, 1.0, 2.0, 3.0], + backend='warp') + pa.add_property('force', data=[0.0, 0.1, 0.2, + 1.0, 1.1, 1.2, + 2.0, 2.1, 2.2, + 3.0, 3.1, 3.2], + stride=3) + + result = pa.extract_particles([1, 3], align=False, + props=['x', 'force']) + props = result.get_property_arrays(all=True, only_real=False) + + assert 'y' not in props + assert 'x' in props + assert 'force' in props + assert np.allclose(props['x'], [1.0, 3.0]) + assert np.allclose(props['force'], [1.0, 1.1, 1.2, + 3.0, 3.1, 3.2]) + + def test_max_reports_device_value(self): + pa = get_particle_array(name='f', x=[0.0, 3.0, 2.0], + backend='warp') + + assert pa.gpu.max('x') == 3.0 diff --git a/pysph/base/tests/test_warp_nnps.py b/pysph/base/tests/test_warp_nnps.py new file mode 100644 index 000000000..ded7276b0 --- /dev/null +++ b/pysph/base/tests/test_warp_nnps.py @@ -0,0 +1,1014 @@ +import numpy as np +import pytest + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +pytest.importorskip('warp') + +from cyarray.carray import UIntArray + +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import ( + BruteForceWarpNNPS, UniformGridWarpNNPS, + assign_particle_levels, brute_force_neighbor_sets, + accepted_level_pair_counts, +) +from pysph.base.warp_multilevel_nnps import MultilevelGridWarpNNPS + + +def _neighbors(nnps, src_index, dst_index, d_idx): + nbrs = UIntArray() + nnps.get_nearest_particles(src_index, dst_index, d_idx, nbrs) + return np.sort(nbrs.get_npy_array()[:nbrs.length]) + + +def _assert_all_neighbors_match(cpu, warp, particles, pairs): + for src_index, dst_index in pairs: + warp.set_context(src_index, dst_index) + dst_count = particles[dst_index].get_number_of_particles() + for d_idx in range(dst_count): + expected = _neighbors(cpu, src_index, dst_index, d_idx) + actual = _neighbors(warp, src_index, dst_index, d_idx) + assert np.array_equal(actual, expected) + + +def _neighbor_sum(cpu, particles, src_index, dst_index, prop): + values = particles[src_index].properties[prop].get_npy_array() + dst_count = particles[dst_index].get_number_of_particles() + result = np.zeros(dst_count, dtype=values.dtype) + for d_idx in range(dst_count): + result[d_idx] = np.sum(values[_neighbors(cpu, src_index, dst_index, + d_idx)]) + return result + + +def _assert_device_cache_neighbors_match(cpu, grid, particles, pairs): + for src_index, dst_index in pairs: + cache = grid.build_neighbor_cache_gpu(src_index, dst_index) + starts = cache['starts_dev'].numpy() + lengths = cache['lengths'] + neighbors = cache['neighbors_dev'].numpy() + dst_count = particles[dst_index].get_number_of_particles() + + assert len(starts) == dst_count + assert len(lengths) == dst_count + assert len(neighbors) == cache['total_neighbors'] + + for d_idx in range(dst_count): + expected = _neighbors(cpu, src_index, dst_index, d_idx) + start = int(starts[d_idx]) + stop = start + int(lengths[d_idx]) + actual = np.sort(neighbors[start:stop]) + assert np.array_equal(actual, expected), ( + src_index, dst_index, d_idx, actual, expected + ) + + +def test_brute_force_warp_nnps_matches_cpu_linked_list_in_2d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.25, 0.25], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + warp = BruteForceWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + _assert_all_neighbors_match(cpu, warp, particles, [(0, 0)]) + + +def test_brute_force_warp_nnps_matches_cpu_for_multiple_arrays(): + pa1 = get_particle_array( + name='fluid', + x=[0.0, 0.25, 0.5], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[0.2, 0.2, 0.2], + backend='warp', + ) + pa2 = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + particles = [pa1, pa2] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + warp = BruteForceWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + _assert_all_neighbors_match( + cpu, warp, particles, [(0, 0), (1, 1), (0, 1), (1, 0)] + ) + + +def test_brute_force_warp_nnps_uses_source_and_destination_h(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0, 2.0], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[0.1, 1.1, 0.1], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=1, particles=particles, radius_scale=1.0) + warp = BruteForceWarpNNPS(dim=1, particles=particles, radius_scale=1.0) + + _assert_all_neighbors_match(cpu, warp, particles, [(0, 0)]) + + +def test_brute_force_warp_nnps_can_sort_neighbors_by_gid(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.1, 0.2], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[1.0, 1.0, 1.0], + gid=np.array([30, 10, 20], dtype=np.uint32), + backend='warp', + ) + warp = BruteForceWarpNNPS( + dim=1, particles=[pa], radius_scale=1.0, sort_gids=True + ) + + nbrs = UIntArray() + warp.get_nearest_particles(0, 0, 0, nbrs) + + assert np.array_equal(nbrs.get_npy_array()[:nbrs.length], + np.array([1, 2, 0], dtype=np.uint32)) + + +def test_brute_force_warp_nnps_update_after_particle_mutation(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + particles = [pa] + warp = BruteForceWarpNNPS(dim=1, particles=particles, radius_scale=1.0) + + assert np.array_equal(_neighbors(warp, 0, 0, 0), + np.array([0], dtype=np.uint32)) + + pa.x[1] = 0.1 + warp.update() + + assert np.array_equal(_neighbors(warp, 0, 0, 0), + np.array([0, 1], dtype=np.uint32)) + + +def test_cached_brute_force_warp_nnps_matches_uncached_path(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.25, 0.25], + backend='warp', + ) + particles = [pa] + cached = BruteForceWarpNNPS( + dim=2, particles=particles, radius_scale=2.0, cache=True + ) + uncached = BruteForceWarpNNPS( + dim=2, particles=particles, radius_scale=2.0, cache=False + ) + + _assert_all_neighbors_match(uncached, cached, particles, [(0, 0)]) + + +def test_cached_brute_force_warp_nnps_rebuilds_after_update(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + warp = BruteForceWarpNNPS( + dim=1, particles=[pa], radius_scale=1.0, cache=True + ) + + assert np.array_equal(_neighbors(warp, 0, 0, 0), + np.array([0], dtype=np.uint32)) + + pa.x[1] = 0.1 + warp.update() + + assert np.array_equal(_neighbors(warp, 0, 0, 0), + np.array([0, 1], dtype=np.uint32)) + + +def test_uniform_grid_warp_nnps_matches_cpu_linked_list_in_2d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.25, 0.25], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + _assert_all_neighbors_match(cpu, grid, particles, [(0, 0)]) + + +def test_uniform_grid_warp_nnps_matches_cpu_for_multiple_arrays(): + pa1 = get_particle_array( + name='fluid', + x=[0.0, 0.25, 0.5], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[0.2, 0.2, 0.2], + backend='warp', + ) + pa2 = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + particles = [pa1, pa2] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + _assert_all_neighbors_match( + cpu, grid, particles, [(0, 0), (1, 1), (0, 1), (1, 0)] + ) + + +def test_uniform_grid_warp_nnps_matches_cpu_in_3d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5, 0.25], + y=[0.0, 0.0, 0.1, 1.5, 0.25], + z=[0.0, 0.1, 0.0, 1.5, 0.2], + h=[0.25, 0.25, 0.25, 0.25, 0.25], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=3, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + _assert_all_neighbors_match(cpu, grid, particles, [(0, 0)]) + + +def test_uniform_grid_warp_nnps_device_cache_matches_cpu_indices_in_random_2d(): + rng = np.random.default_rng(1729) + n = 96 + pa = get_particle_array( + name='fluid', + x=rng.random(n), + y=rng.random(n), + z=np.zeros(n), + h=0.055 + 0.035 * rng.random(n), + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + _assert_device_cache_neighbors_match(cpu, grid, particles, [(0, 0)]) + + +def test_uniform_grid_warp_nnps_device_cache_matches_cpu_indices_cross_3d(): + rng = np.random.default_rng(2718) + nsrc = 64 + ndst = 41 + fluid = get_particle_array( + name='fluid', + x=rng.random(nsrc), + y=rng.random(nsrc), + z=rng.random(nsrc), + h=0.08 + 0.04 * rng.random(nsrc), + backend='warp', + ) + solid = get_particle_array( + name='solid', + x=rng.random(ndst), + y=rng.random(ndst), + z=rng.random(ndst), + h=0.08 + 0.04 * rng.random(ndst), + backend='warp', + ) + particles = [fluid, solid] + cpu = LinkedListNNPS(dim=3, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + _assert_device_cache_neighbors_match( + cpu, grid, particles, [(0, 1), (1, 0)] + ) + + +def test_uniform_grid_warp_nnps_matches_bruteforce_for_variable_h(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0, 2.0], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[0.1, 1.1, 0.1], + backend='warp', + ) + particles = [pa] + brute = BruteForceWarpNNPS( + dim=1, particles=particles, radius_scale=1.0, cache=True + ) + grid = UniformGridWarpNNPS(dim=1, particles=particles, radius_scale=1.0) + + _assert_all_neighbors_match(brute, grid, particles, [(0, 0)]) + + +def test_uniform_grid_warp_nnps_rebuilds_after_update(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + grid = UniformGridWarpNNPS(dim=1, particles=[pa], radius_scale=1.0) + + assert np.array_equal(_neighbors(grid, 0, 0, 0), + np.array([0], dtype=np.uint32)) + + pa.x[1] = 0.1 + grid.update() + + assert np.array_equal(_neighbors(grid, 0, 0, 0), + np.array([0, 1], dtype=np.uint32)) + + +def test_uniform_grid_warp_nnps_can_rebuild_from_device_positions(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + backend='warp', + ) + grid = UniformGridWarpNNPS(dim=1, particles=[pa], radius_scale=1.0) + + assert np.array_equal(_neighbors(grid, 0, 0, 0), + np.array([0], dtype=np.uint32)) + + pa.gpu.x.set(np.asarray([0.0, 0.1])) + grid.update(push=False) + + assert np.array_equal(_neighbors(grid, 0, 0, 0), + np.array([0, 1], dtype=np.uint32)) + + grid.update() + + assert np.array_equal(_neighbors(grid, 0, 0, 0), + np.array([0], dtype=np.uint32)) + + +def test_uniform_grid_warp_nnps_computes_neighbor_sum_on_device(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.25, 0.25], + m=[1.0, 2.0, 3.0, 4.0], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + expected = _neighbor_sum(cpu, particles, 0, 0, 'm') + actual = grid.compute_neighbor_sum(0, 0, 'm').numpy() + + assert np.allclose(actual, expected) + + +def test_uniform_grid_warp_nnps_computes_cross_array_neighbor_sum_on_device(): + fluid = get_particle_array( + name='fluid', + x=[0.0, 0.25, 0.5], + y=[0.0, 0.0, 0.0], + z=[0.0, 0.0, 0.0], + h=[0.2, 0.2, 0.2], + m=[2.0, 4.0, 8.0], + backend='warp', + ) + solid = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + m=[1.0, 1.0], + backend='warp', + ) + particles = [fluid, solid] + cpu = LinkedListNNPS(dim=2, particles=particles, radius_scale=2.0) + grid = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + expected = _neighbor_sum(cpu, particles, 0, 1, 'm') + actual = grid.compute_neighbor_sum(0, 1, 'm').numpy() + + assert np.allclose(actual, expected) + + +def test_uniform_grid_warp_nnps_neighbor_sum_rebuilds_after_update(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.2, 0.2], + m=[2.0, 3.0], + backend='warp', + ) + particles = [pa] + cpu = LinkedListNNPS(dim=1, particles=particles, radius_scale=1.0) + grid = UniformGridWarpNNPS(dim=1, particles=particles, radius_scale=1.0) + + expected = _neighbor_sum(cpu, particles, 0, 0, 'm') + assert np.allclose(grid.compute_neighbor_sum(0, 0, 'm').numpy(), + expected) + + pa.x[1] = 0.1 + cpu.update() + grid.update() + + expected = _neighbor_sum(cpu, particles, 0, 0, 'm') + assert np.allclose(grid.compute_neighbor_sum(0, 0, 'm').numpy(), + expected) + + +# --- Multilevel GPU NNPS: level-assignment contract (kill gate, step 1) --- +# +# Range-bin, half-open convention frozen for the multilevel NNPS: +# level k covers h in [h_ref*ratio**k, h_ref*ratio**(k+1)); level 0 is finest. +# The top edge (h == h_ref*ratio**nlevels) is inclusive -> top level. +# h strictly outside [h_ref, h_ref*ratio**nlevels] fails loudly. +# Per-level support bound = radius_scale * max(assigned h) (conservative). + +def test_assign_particle_levels_bins_by_half_open_ranges(): + # h_ref=0.1, ratio=2, nlevels=4 -> edges [0.1, 0.2, 0.4, 0.8, 1.6] + # level 0: [0.1, 0.2) level 1: [0.2, 0.4) + # level 2: [0.4, 0.8) level 3: [0.8, 1.6] + h = np.array([0.1, 0.15, 0.2, 0.5, 0.8, 1.6], dtype=np.float64) + levels, support = assign_particle_levels( + h, h_ref=0.1, level_ratio=2.0, nlevels=4, radius_scale=2.0 + ) + # 0.2 and 0.8 sit on lower-closed boundaries; 1.6 is the inclusive top edge. + assert list(levels) == [0, 0, 1, 2, 3, 3] + + +def test_assign_particle_levels_rejects_invalid_level_parameters(): + # Level edges h_ref*ratio**k are only strictly ascending for h_ref > 0 and + # level_ratio > 1; otherwise binning silently inverts. Fail loudly instead. + h = np.array([1.0]) + with pytest.raises(ValueError): # ratio == 1 collapses all edges + assign_particle_levels(h, h_ref=1.0, level_ratio=1.0, nlevels=3, + radius_scale=2.0) + with pytest.raises(ValueError): # ratio < 1 -> descending edges + assign_particle_levels(h, h_ref=1.0, level_ratio=0.5, nlevels=3, + radius_scale=2.0) + with pytest.raises(ValueError): # non-positive h_ref + assign_particle_levels(h, h_ref=0.0, level_ratio=2.0, nlevels=3, + radius_scale=2.0) + with pytest.raises(ValueError): # nlevels must be >= 1 + assign_particle_levels(h, h_ref=1.0, level_ratio=2.0, nlevels=0, + radius_scale=2.0) + + +def test_assign_particle_levels_rejects_out_of_range_h(): + # No silent clipping: h below the finest edge or above the top edge fails. + with pytest.raises(ValueError): + assign_particle_levels( + np.array([0.05]), h_ref=0.1, level_ratio=2.0, nlevels=4, + radius_scale=2.0, + ) + with pytest.raises(ValueError): + assign_particle_levels( + np.array([2.0]), h_ref=0.1, level_ratio=2.0, nlevels=4, + radius_scale=2.0, + ) + + +def test_assign_particle_levels_support_bound_is_conservative_max(): + # support[k] = radius_scale * max(h in level k); empty levels stay 0. + h = np.array([0.1, 0.15, 0.5, 0.7], dtype=np.float64) + # edges [0.1,0.2,0.4,0.8,1.6] -> levels [0,0,2,2]; levels 1 and 3 empty. + levels, support = assign_particle_levels( + h, h_ref=0.1, level_ratio=2.0, nlevels=4, radius_scale=2.0 + ) + assert list(levels) == [0, 0, 2, 2] + assert np.allclose(support, [2.0 * 0.15, 0.0, 2.0 * 0.7, 0.0]) + + +# --- Multilevel GPU NNPS: host brute-force neighbor oracle (kill gate) --- +# +# Independent pure-numpy reference for the exact symmetric pair contract +# rij^2 < (radius_scale*h_i)^2 OR rij^2 < (radius_scale*h_j)^2 +# matching _neighbor_flags in warp_nnps (self is included: an array vs itself +# has rij=0 < support). Cross-checked against BruteForceWarpNNPS below. + +def test_brute_force_neighbor_sets_matches_symmetric_cutoff_1d(): + # positions [0.0, 0.3, 1.0], h=0.2, radius_scale=2 -> support radius 0.4. + # dst 0: self + 0.3<0.4 -> [0,1]; dst 1: 0.3<0.4 + self -> [0,1] + # dst 2: 0.7 and 1.0 both > 0.4 -> [2] (self only) + zeros = np.zeros(3) + pa = (np.array([0.0, 0.3, 1.0]), zeros, zeros, np.full(3, 0.2)) + sets = brute_force_neighbor_sets(pa, pa, radius_scale=2.0, dim=1) + assert [list(s) for s in sets] == [[0, 1], [0, 1], [2]] + + +def test_brute_force_oracle_agrees_with_brute_force_warp_nnps_2d(): + # Variable h exercises the asymmetric OR in the symmetric cutoff; the + # host oracle must reproduce the trusted GPU BruteForceWarpNNPS exactly. + x = [0.0, 0.2, 0.5, 0.55, 1.2] + y = [0.0, 0.1, 0.5, 0.5, 0.0] + h = [0.3, 0.1, 0.2, 0.05, 0.4] + pa = get_particle_array( + name='fluid', x=x, y=y, z=[0.0] * 5, h=h, backend='warp' + ) + warp = BruteForceWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + warp.set_context(0, 0) + tup = (np.array(x), np.array(y), np.zeros(5), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=2) + for d_idx in range(5): + actual = _neighbors(warp, 0, 0, d_idx) + assert np.array_equal(actual, oracle[d_idx]), (d_idx, actual, + oracle[d_idx]) + + +def test_accepted_level_pair_counts_bins_pairs_by_level(): + # 2 destinations at levels [0, 1]; 3 sources at levels [0, 0, 1]. + # dst 0 (level 0) -> src {0(l0), 2(l1)}: (0,0)+1, (0,1)+1 + # dst 1 (level 1) -> src {1(l0)}: (1,0)+1 + neighbor_sets = [np.array([0, 2]), np.array([1])] + d_levels = np.array([0, 1]) + s_levels = np.array([0, 0, 1]) + counts = accepted_level_pair_counts( + neighbor_sets, d_levels, s_levels, nlevels=2 + ) + assert counts.tolist() == [[1, 1], [1, 0]] + + +# --- Multilevel GPU NNPS: MultilevelGridWarpNNPS (kill gate, step 2) --- + +def test_multilevel_single_level_matches_brute_force_2d(): + # nlevels=1: the multilevel class must degenerate to exact uniform-grid / + # brute-force behavior (cheapest kill gate). Reuses the trusted 2D config. + x = [0.0, 0.2, 0.4, 1.5] + y = [0.0, 0.0, 0.1, 1.5] + h = [0.25, 0.25, 0.25, 0.25] + pa = get_particle_array( + name='fluid', x=x, y=y, z=[0.0] * 4, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=2, particles=[pa], radius_scale=2.0, + h_ref=0.25, level_ratio=2.0, nlevels=1, + ) + bf = BruteForceWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + # Also pin against the independent numpy oracle. + tup = (np.array(x), np.array(y), np.zeros(4), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=2) + ml.set_context(0, 0) + for d_idx in range(4): + assert np.array_equal(_neighbors(ml, 0, 0, d_idx), oracle[d_idx]) + + +def test_multilevel_four_levels_h16_cross_level_parity_3d(): + # Four discrete levels spanning h_max/h_min = 16 with genuine cross-level + # pairs (coarse dst <-> fine src). Exact-set parity here is the core + # correctness kill gate; the variable stencil must find neighbors whose + # support far exceeds a fine level's cell size. + x = [0.0, 0.8, 0.5, 1.0, 0.2, 2.0, 0.1, 5.0] + h = [1.6, 0.8, 0.6, 0.4, 0.3, 0.2, 0.15, 0.1] + zeros = [0.0] * 8 + pa = get_particle_array( + name='fluid', x=x, y=zeros, z=zeros, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=3, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=4, + ) + # Level assignment / support contract. + levels, support = assign_particle_levels( + np.array(h), h_ref=0.1, level_ratio=2.0, nlevels=4, radius_scale=2.0 + ) + assert list(levels) == [3, 3, 2, 2, 1, 1, 0, 0] + assert np.allclose(support, [0.3, 0.6, 1.2, 3.2]) + + # Exact-set parity vs the verified numpy oracle and the (uncached, robust) + # brute-force GPU NNPS. + tup = (np.array(x), np.array(zeros), np.array(zeros), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=3) + bf = BruteForceWarpNNPS(dim=3, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + ml.set_context(0, 0) + ml_sets = [_neighbors(ml, 0, 0, i) for i in range(8)] + for i in range(8): + assert np.array_equal(ml_sets[i], oracle[i]), (i, ml_sets[i], oracle[i]) + assert len(ml_sets[i]) == len(set(ml_sets[i].tolist())), i # no dupes + + # The isolated fine particle (x=5.0) has only itself. + assert list(ml_sets[7]) == [7] + + # Accepted (dst-level, src-level) matrix has real cross-level (off-diagonal) + # mass, and matches the oracle-derived matrix. + counts_ml = accepted_level_pair_counts(ml_sets, levels, levels, nlevels=4) + counts_oracle = accepted_level_pair_counts(oracle, levels, levels, nlevels=4) + assert np.array_equal(counts_ml, counts_oracle) + off_diagonal = counts_ml.sum() - np.trace(counts_ml) + assert off_diagonal > 0 + + +def test_multilevel_fp32_per_level_grid_boundary_padding_1d(): + # P0 silent-omission guard: a fine particle sitting exactly on its level's + # far edge must floor to a valid cell in [0, nx) via per-level PADDING, not + # via the binning kernel's clamp (which would mask a padding defect). + # h_ref=0.1, level_ratio=4, nlevels=2 -> edges [0.1, 0.4, 1.6]. + # Fine level 0 (h=0.1 -> cell_size 0.2) spans x=[0.1..0.9], extent + # 0.8 == 4*cell_size exactly, so WITHOUT padding x=0.9 floors to cell 4==nx. + # Spacings are deliberately off the 0.2 support so no PAIR sits on the + # neighbor cutoff (which would make fp32 and the fp64 oracle disagree); this + # fixture isolates grid-cell-boundary padding, not cutoff rounding. The + # coarse particle sits at 0.5 so its 0.8 support clearly covers every fine + # particle (max dist 0.4), again avoiding a cutoff-boundary pair. + x = [0.1, 0.25, 0.55, 0.72, 0.9, 0.5] + h = [0.1, 0.1, 0.1, 0.1, 0.1, 0.4] # last is the coarse (level 1) particle + zeros = [0.0] * 6 + pa = get_particle_array( + name='fluid', x=x, y=zeros, z=zeros, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=1, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=4.0, nlevels=2, + ) + + info = ml.level_grid_info(0) + lv = info['levels'] + ox = info['origin_x'] + cs = info['cell_size'] + nxs = info['nx'] + assert list(lv) == [0, 0, 0, 0, 0, 1] + # Pre-clamp cell index (computed in the device fp32 precision) is in range + # for every particle -- especially the far-edge fine particle at x=0.9. + xf = np.asarray(x, dtype=ox.dtype) + for i in range(6): + k = int(lv[i]) + ix = int(np.floor((xf[i] - ox[k]) / cs[k])) + assert 0 <= ix < nxs[k], (i, ix, nxs[k]) + # Regression witness: WITHOUT padding the far fine particle would land on + # cell nx (out of range) -- documents why the per-level origin is padded. + unpadded_nx = int(np.ceil((0.9 - 0.1) / 0.2)) + assert int(np.floor((0.9 - 0.1) / 0.2)) == unpadded_nx # == nx => OOB + + # Full neighbor-set parity (fp32 GPU) vs oracle and uncached brute force. + tup = (np.array(x), np.array(zeros), np.array(zeros), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=1) + bf = BruteForceWarpNNPS(dim=1, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + ml.set_context(0, 0) + ml_sets = [_neighbors(ml, 0, 0, i) for i in range(6)] + for i in range(6): + assert np.array_equal(ml_sets[i], oracle[i]), (i, ml_sets[i], oracle[i]) + # Cross-level pair reaching the far-edge fine particle (idx4 at x=0.9) from + # the coarse particle (idx5): its 0.8 support spans the fine AABB. + assert 4 in ml_sets[5].tolist() and 5 in ml_sets[4].tolist() + + +def test_multilevel_empty_interior_levels_are_well_formed_2d(): + # Levels 1 and 3 are unpopulated: their per-level metadata must be + # degenerate-safe (no cells, no NaN origin) and traversal must skip them, + # while cross-level (level 0 <-> level 2) neighbors stay exact. + x = [0.0, 0.15, 0.2, 0.8] + h = [0.1, 0.15, 0.5, 0.7] # edges [0.1,0.2,0.4,0.8,1.6] -> levels [0,0,2,2] + zeros = [0.0] * 4 + pa = get_particle_array( + name='fluid', x=x, y=zeros, z=zeros, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=2, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=4, + ) + levels, support = assign_particle_levels( + np.array(h), h_ref=0.1, level_ratio=2.0, nlevels=4, radius_scale=2.0 + ) + assert list(levels) == [0, 0, 2, 2] + assert np.allclose(support, [0.3, 0.0, 1.4, 0.0]) + + # Empty levels 1 and 3 allocate no cells (nx == 0), not a degenerate grid. + info = ml.level_grid_info(0) + assert info['nx'][1] == 0 and info['nx'][3] == 0 + assert info['nx'][0] > 0 and info['nx'][2] > 0 + + tup = (np.array(x), np.array(zeros), np.array(zeros), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=2) + bf = BruteForceWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + ml.set_context(0, 0) + ml_sets = [_neighbors(ml, 0, 0, i) for i in range(4)] + for i in range(4): + assert np.array_equal(ml_sets[i], oracle[i]), (i, ml_sets[i], oracle[i]) + assert len(ml_sets[i]) == len(set(ml_sets[i].tolist())), i + + # Accepted level-pair matrix: empty levels 1,3 have all-zero rows/cols; + # only the (0,0),(0,2),(2,0),(2,2) blocks are populated. + counts = accepted_level_pair_counts(ml_sets, levels, levels, nlevels=4) + assert counts[1].sum() == 0 and counts[3].sum() == 0 + assert counts[:, 1].sum() == 0 and counts[:, 3].sum() == 0 + assert counts[0, 2] > 0 and counts[2, 0] > 0 + + # Repeated update() rebuilds empty-level metadata cleanly (idempotent sets). + ml.update() + ml.set_context(0, 0) + for i in range(4): + assert np.array_equal(_neighbors(ml, 0, 0, i), oracle[i]), i + + +def test_multilevel_gradual_ratio_1_2_adjacent_levels_2d(): + # Four closely-spaced levels (ratio 1.2) with near-equal per-level cell + # sizes; guards adjacent-level edge binning and the coarse-into-finer + # query-cell-range rounding. h are strictly interior to their bins so the + # fp32 device path bins identically to the fp64 oracle. p5 is isolated to + # exercise exclusion, not just connectivity. + x = [0.0, 0.1, 0.2, 0.3, 0.15, 2.0] + y = [0.0, 0.0, 0.0, 0.0, 0.15, 0.0] + h = [0.11, 0.13, 0.15, 0.19, 0.19, 0.11] + zeros = [0.0] * 6 + pa = get_particle_array( + name='fluid', x=x, y=y, z=zeros, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=2, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=1.2, nlevels=4, + ) + levels, support = assign_particle_levels( + np.array(h), h_ref=0.1, level_ratio=1.2, nlevels=4, radius_scale=2.0 + ) + assert list(levels) == [0, 1, 2, 3, 3, 0] + assert np.allclose(support, [0.22, 0.26, 0.30, 0.38]) + + tup = (np.array(x), np.array(y), np.array(zeros), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=2) + bf = BruteForceWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + ml.set_context(0, 0) + ml_sets = [_neighbors(ml, 0, 0, i) for i in range(6)] + for i in range(6): + assert np.array_equal(ml_sets[i], oracle[i]), (i, ml_sets[i], oracle[i]) + assert len(ml_sets[i]) == len(set(ml_sets[i].tolist())), i + # p5 is isolated (only itself); adjacent-level pairs are found. + assert list(ml_sets[5]) == [5] + counts = accepted_level_pair_counts(ml_sets, levels, levels, nlevels=4) + for a, b in [(0, 1), (1, 0), (1, 2), (2, 1), (2, 3), (3, 2)]: + assert counts[a, b] > 0, (a, b) + + +def test_multilevel_cross_array_traversal_and_ownership_2d(): + # Two independently-leveled arrays; every source/destination context must + # match brute force, neighbor indices stay in the SOURCE array's own 0-based + # space, and each source array owns one cached multilevel structure. + fx, fy = [0.0, 0.2, 0.5], [0.0, 0.0, 0.0] + fh = [0.1, 0.15, 0.4] # edges [0.1,0.2,0.4,0.8] -> [0,0,2] + sx, sy = [0.1, 0.6], [0.0, 0.0] + sh = [0.2, 0.6] # -> [1,2] + fluid = get_particle_array( + name='fluid', x=fx, y=fy, z=[0.0] * 3, h=fh, backend='warp' + ) + solid = get_particle_array( + name='solid', x=sx, y=sy, z=[0.0] * 2, h=sh, backend='warp' + ) + particles = [fluid, solid] + ml = MultilevelGridWarpNNPS( + dim=2, particles=particles, radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=3, + ) + bf = BruteForceWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + # Per-array level assignment is independent. + fl, _ = assign_particle_levels(np.array(fh), 0.1, 2.0, 3, 2.0) + sl, _ = assign_particle_levels(np.array(sh), 0.1, 2.0, 3, 2.0) + assert list(fl) == [0, 0, 2] and list(sl) == [1, 2] + + arrays = {0: (np.array(fx), np.array(fy), np.zeros(3), np.array(fh)), + 1: (np.array(sx), np.array(sy), np.zeros(2), np.array(sh))} + contexts = [(0, 0), (1, 1), (0, 1), (1, 0)] + _assert_all_neighbors_match(bf, ml, particles, contexts) + for src_index, dst_index in contexts: + oracle = brute_force_neighbor_sets( + arrays[dst_index], arrays[src_index], radius_scale=2.0, dim=2 + ) + ml.set_context(src_index, dst_index) + ndst = particles[dst_index].get_number_of_particles() + for d_idx in range(ndst): + got = _neighbors(ml, src_index, dst_index, d_idx) + assert np.array_equal(got, oracle[d_idx]), (src_index, dst_index, + d_idx, got, oracle[d_idx]) + # Indices are in the source array's own 0-based space. + nsrc = particles[src_index].get_number_of_particles() + assert got.size == 0 or int(got.max()) < nsrc + + # Each source array owns a distinct cached multilevel structure. + assert set(ml._ml.keys()) == {0, 1} + assert ml._ml[0] is not ml._ml[1] + + +def test_multilevel_particles_at_spatial_bounds_3d(): + # Particles at the geometric min/max corners of each level's occupied + # region (all axes) must bin to valid cells via per-level padding, and keep + # their colocated cross-level neighbors. edges [0.1,0.2,0.4]. + corners = [(0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1), + (1, 1, 0), (1, 0, 1), (0, 1, 1), (1, 1, 1)] + fx = [c[0] for c in corners] + [0.5] + fy = [c[1] for c in corners] + [0.5] + fz = [c[2] for c in corners] + [0.5] + fh = [0.1] * 9 # fine, level 0 + cx, cy, cz, ch = [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.4, 0.4] # coarse l1 + x = fx + cx + y = fy + cy + z = fz + cz + h = fh + ch + pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, backend='warp') + ml = MultilevelGridWarpNNPS( + dim=3, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=2, + ) + levels, support = assign_particle_levels( + np.array(h), 0.1, 2.0, 2, 2.0 + ) + assert list(levels) == [0] * 9 + [1, 1] + assert np.allclose(support, [0.2, 0.8]) + + # Every particle -- including the 8 corner extremes -- floors to a valid + # in-range cell on every axis (pre-clamp), via per-level origin padding. + info = ml.level_grid_info(0) + ox, oy, oz = info['origin_x'], info['origin_y'], info['origin_z'] + cs, nxs, nys, nzs = (info['cell_size'], info['nx'], info['ny'], info['nz']) + xa = np.asarray(x, dtype=ox.dtype) + ya = np.asarray(y, dtype=ox.dtype) + za = np.asarray(z, dtype=ox.dtype) + for i in range(len(x)): + k = int(levels[i]) + ix = int(np.floor((xa[i] - ox[k]) / cs[k])) + iy = int(np.floor((ya[i] - oy[k]) / cs[k])) + iz = int(np.floor((za[i] - oz[k]) / cs[k])) + assert 0 <= ix < nxs[k] and 0 <= iy < nys[k] and 0 <= iz < nzs[k], ( + i, ix, iy, iz, nxs[k], nys[k], nzs[k]) + + tup = (np.array(x), np.array(y), np.array(z), np.array(h)) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=3) + bf = BruteForceWarpNNPS(dim=3, particles=[pa], radius_scale=2.0) + _assert_all_neighbors_match(bf, ml, [pa], [(0, 0)]) + ml.set_context(0, 0) + for i in range(len(x)): + got = _neighbors(ml, 0, 0, i) + assert np.array_equal(got, oracle[i]), (i, got, oracle[i]) + assert len(got) == len(set(got.tolist())), i + # Corner colocated cross-level pairs retained: coarse idx9 at (0,0,0) <-> + # fine idx0 at (0,0,0); coarse idx10 at (1,1,1) <-> fine idx7 at (1,1,1). + assert 0 in _neighbors(ml, 0, 0, 9).tolist() + assert 7 in _neighbors(ml, 0, 0, 10).tolist() + + +def test_multilevel_no_coordinate_host_readback_on_warm_update_3d(): + # Device residency: a warm update(push=False) followed by a query must NOT + # pull per-particle x/y/z/h back to the host. Only O(nlevels) scalar + # metadata readback is permitted (and is not on the coordinate arrays). + x = [0.0, 0.8, 0.5, 1.0, 0.2, 2.0, 0.1, 5.0] + h = [1.6, 0.8, 0.6, 0.4, 0.3, 0.2, 0.15, 0.1] + zeros = [0.0] * 8 + pa = get_particle_array( + name='fluid', x=x, y=zeros, z=zeros, h=h, backend='warp' + ) + ml = MultilevelGridWarpNNPS( + dim=3, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=4, + ) + + reads = [] + + def _spy(name, orig): + def wrapped(): + reads.append(name) + return orig() + return wrapped + + patched = [n for n in ('x', 'y', 'z', 'h')] + for name in patched: + arr = getattr(pa.gpu, name) + arr.get = _spy(name, arr.get) + try: + ml.update(push=False) # warm rebuild + ml.set_context(0, 0) + for i in range(8): # traversal / neighbor build + _neighbors(ml, 0, 0, i) + finally: + for name in patched: + arr = getattr(pa.gpu, name) + if 'get' in arr.__dict__: + del arr.__dict__['get'] + + assert reads == [], ( + "warm update/query pulled coordinates to host: %r" % reads + ) + + +def _uniform_candidate_pairs(uniform, src_index, dst_index): + # Total source particles the uniform grid's fixed 3x3x3 stencil scans, + # summed over destinations -- the candidate work to beat. + grid = uniform._build_grid(src_index) + counts = grid['counts'].numpy() + b = uniform._bounds + cs = uniform.cell_size + nx, ny, nz = b['nx'], b['ny'], b['nz'] + dim = uniform.dim + dst = uniform.particles[dst_index].gpu + dx, dy, dz = dst.x.get(), dst.y.get(), dst.z.get() + + def cell0(c, cmin, n): + return min(max(int(np.floor((c - cmin) / cs)), 0), n - 1) + + total = 0 + for i in range(len(dx)): + ix0 = cell0(dx[i], b['xmin'], nx) + iy0 = cell0(dy[i], b['ymin'], ny) if dim > 1 else 0 + iz0 = cell0(dz[i], b['zmin'], nz) if dim > 2 else 0 + for dzc in (-1, 0, 1): + for dyc in (-1, 0, 1): + for dxc in (-1, 0, 1): + ix, iy, iz = ix0 + dxc, iy0 + dyc, iz0 + dzc + if 0 <= ix < nx and 0 <= iy < ny and 0 <= iz < nz: + total += int(counts[ix + iy * nx + iz * nx * ny]) + return total + + +def test_multilevel_clustered_refinement_candidate_scaling_3d(): + # Localized refinement: a few coarse particles (h=0.8) far enough to inflate + # the global hmax plus a dense fine cluster (h=0.05, spacing 0.08 != the + # 0.1 support so no pair sits exactly on the cutoff). The uniform grid's + # global cell (rs*hmax=1.6) lumps the whole fine cluster into ~one cell, so + # every fine destination scans it entirely; the multilevel grid confines + # fine-fine scanning to local fine cells. Accepted sets stay identical; + # candidate work drops >=4x. + g = np.linspace(0.0, 0.8, 11) # spacing 0.08 + FX, FY, FZ = np.meshgrid(g, g, g, indexing='ij') + fx, fy, fz = FX.ravel(), FY.ravel(), FZ.ravel() # 1331 fine + fh = np.full(fx.size, 0.05) + corners = np.array([(a, b_, c) for a in (0.0, 4.0) + for b_ in (0.0, 4.0) for c in (0.0, 4.0)]) + cx, cy, cz = corners[:, 0], corners[:, 1], corners[:, 2] # 8 coarse + ch = np.full(cx.size, 0.8) + x = np.concatenate([fx, cx]) + y = np.concatenate([fy, cy]) + z = np.concatenate([fz, cz]) + h = np.concatenate([fh, ch]) + pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, backend='warp') + + ml = MultilevelGridWarpNNPS( + dim=3, particles=[pa], radius_scale=2.0, + h_ref=0.05, level_ratio=2.0, nlevels=4, + ) + nfine = fx.size + levels, _ = assign_particle_levels(h, 0.05, 2.0, 4, 2.0) + assert set(levels[:nfine].tolist()) == {0} + assert set(levels[nfine:].tolist()) == {3} + + # Accepted-set parity: multilevel == numpy oracle == uniform grid. + tup = (x, y, z, h) + oracle = brute_force_neighbor_sets(tup, tup, radius_scale=2.0, dim=3) + uniform = UniformGridWarpNNPS(dim=3, particles=[pa], radius_scale=2.0) + ml.set_context(0, 0) + uniform.set_context(0, 0) + accepted = 0 + for i in range(len(x)): + ml_i = _neighbors(ml, 0, 0, i) + assert np.array_equal(ml_i, oracle[i]), i + assert np.array_equal(_neighbors(uniform, 0, 0, i), oracle[i]), i + assert len(ml_i) == len(set(ml_i.tolist())), i + accepted += len(oracle[i]) + + # Candidate work: multilevel <= 0.25 * uniform (>= 4x reduction). + ml_cand = ml.candidate_pairs(0, 0) + uniform_cand = _uniform_candidate_pairs(uniform, 0, 0) + assert ml_cand >= accepted # candidates are a superset of accepted + assert ml_cand * 4 <= uniform_cand, (ml_cand, uniform_cand, accepted) diff --git a/pysph/base/tests/test_warp_sph.py b/pysph/base/tests/test_warp_sph.py new file mode 100644 index 000000000..fca1f898a --- /dev/null +++ b/pysph/base/tests/test_warp_sph.py @@ -0,0 +1,2466 @@ +import numpy as np +import pytest + +try: + import setuptools # noqa: F401 - keeps distutils importable on Python 3.14. +except Exception: + pass + +wp = pytest.importorskip('warp') + +from cyarray.carray import UIntArray + +from pysph.base.kernels import CubicSpline, Gaussian, WendlandQuintic +from pysph.base.nnps import LinkedListNNPS +from pysph.base.utils import get_particle_array +from pysph.base.warp_nnps import UniformGridWarpNNPS +from pysph.base.warp_multilevel_nnps import MultilevelGridWarpNNPS +import pysph.base.warp_sph as warp_sph +from pysph.base.warp_sph import ( + apply_body_force, compute_artificial_viscosity, compute_continuity, + compute_isothermal_eos, compute_pressure_gradient, compute_summation_density, + compute_liu_fluid_rigid_coupling, compute_rigid_body_moments, + compute_rigid_body_moments_device, compute_rigid_number_density, + compute_tait_eos, compute_tait_eos_hg_correction, create_rigid_body_state, + compute_wcsph_accel_continuity, compute_wcsph_adaptive_timestep, + compute_xsph_correction, euler_step, leapfrog_drift, leapfrog_kick, + initialize_rigid_body_force, rigid_body_density_stage, + rigid_body_rk2_stage, save_rigid_body_density, save_rigid_body_state, + save_wcsph_state, wc_sph_dam_break_rigid_step, wc_sph_dam_break_step, + wc_sph_euler_step, wc_sph_leapfrog_step, wcsph_pec_stage, wrap_periodic +) + + +def _neighbors(nnps, src_index, dst_index, d_idx): + nbrs = UIntArray() + nnps.get_nearest_particles(src_index, dst_index, d_idx, nbrs) + return nbrs.get_npy_array()[:nbrs.length] + + +def _cpu_kernel(dim, kernel='cubic'): + if kernel == 'gaussian': + return Gaussian(dim=dim) + if kernel == 'wendland': + return WendlandQuintic(dim=dim) + return CubicSpline(dim=dim) + + +def _cpu_summation_density(particles, src_index, dst_index, dim, + radius_scale=2.0, kernel='cubic'): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + kernel_obj = _cpu_kernel(dim, kernel) + src = particles[src_index] + dst = particles[dst_index] + result = np.zeros(dst.get_number_of_particles()) + + for d_idx in range(dst.get_number_of_particles()): + total = 0.0 + for s_idx in _neighbors(nnps, src_index, dst_index, d_idx): + xij = [ + dst.x[d_idx] - src.x[s_idx], + 0.0, + 0.0, + ] + if dim > 1: + xij[1] = dst.y[d_idx] - src.y[s_idx] + if dim > 2: + xij[2] = dst.z[d_idx] - src.z[s_idx] + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d_idx] + src.h[s_idx]) + total += src.m[s_idx] * kernel_obj.kernel( + xij=xij, rij=rij, h=hij + ) + result[d_idx] = total + return result + + +def _cpu_continuity(particles, src_index, dst_index, dim, radius_scale=2.0, + kernel='cubic'): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + kernel_obj = _cpu_kernel(dim, kernel) + src = particles[src_index] + dst = particles[dst_index] + result = np.zeros(dst.get_number_of_particles()) + + for d_idx in range(dst.get_number_of_particles()): + total = 0.0 + for s_idx in _neighbors(nnps, src_index, dst_index, d_idx): + xij = [ + dst.x[d_idx] - src.x[s_idx], + 0.0, + 0.0, + ] + vij = [ + dst.u[d_idx] - src.u[s_idx], + 0.0, + 0.0, + ] + if dim > 1: + xij[1] = dst.y[d_idx] - src.y[s_idx] + vij[1] = dst.v[d_idx] - src.v[s_idx] + if dim > 2: + xij[2] = dst.z[d_idx] - src.z[s_idx] + vij[2] = dst.w[d_idx] - src.w[s_idx] + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d_idx] + src.h[s_idx]) + dwij = [0.0, 0.0, 0.0] + kernel_obj.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + total += src.m[s_idx] * ( + vij[0]*dwij[0] + vij[1]*dwij[1] + vij[2]*dwij[2] + ) + result[d_idx] = total + return result + + +def _cpu_pressure_gradient(particles, src_index, dst_index, dim, + radius_scale=2.0, kernel='cubic'): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + kernel_obj = _cpu_kernel(dim, kernel) + src = particles[src_index] + dst = particles[dst_index] + result = np.zeros((dst.get_number_of_particles(), 3)) + + for d_idx in range(dst.get_number_of_particles()): + acc = np.zeros(3) + rhoi21 = 1.0/(dst.rho[d_idx]*dst.rho[d_idx]) + tmpi = dst.p[d_idx]*rhoi21 + for s_idx in _neighbors(nnps, src_index, dst_index, d_idx): + xij = [ + dst.x[d_idx] - src.x[s_idx], + 0.0, + 0.0, + ] + if dim > 1: + xij[1] = dst.y[d_idx] - src.y[s_idx] + if dim > 2: + xij[2] = dst.z[d_idx] - src.z[s_idx] + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d_idx] + src.h[s_idx]) + dwij = [0.0, 0.0, 0.0] + kernel_obj.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + rhoj21 = 1.0/(src.rho[s_idx]*src.rho[s_idx]) + tmp = tmpi + src.p[s_idx]*rhoj21 + acc += -src.m[s_idx] * tmp * np.asarray(dwij) + result[d_idx, :] = acc + return result + + +def _cpu_tait_eos(rho, rho0, c0, gamma=7.0, p0=0.0): + ratio = rho / rho0 + p = p0 + (rho0*c0*c0/gamma) * (ratio**gamma - 1.0) + cs = c0 * ratio**(0.5 * (gamma - 1.0)) + return p, cs + + +def _cpu_artificial_viscosity(particles, src_index, dst_index, dim, + alpha, beta, c0, radius_scale=2.0, + kernel='cubic'): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + kernel_obj = _cpu_kernel(dim, kernel) + src = particles[src_index] + dst = particles[dst_index] + result = np.zeros((dst.get_number_of_particles(), 3)) + src_has_cs = 'cs' in src.properties + dst_has_cs = 'cs' in dst.properties + + for d_idx in range(dst.get_number_of_particles()): + acc = np.zeros(3) + for s_idx in _neighbors(nnps, src_index, dst_index, d_idx): + xij = [ + dst.x[d_idx] - src.x[s_idx], + 0.0, + 0.0, + ] + vij = [ + dst.u[d_idx] - src.u[s_idx], + 0.0, + 0.0, + ] + if dim > 1: + xij[1] = dst.y[d_idx] - src.y[s_idx] + vij[1] = dst.v[d_idx] - src.v[s_idx] + if dim > 2: + xij[2] = dst.z[d_idx] - src.z[s_idx] + vij[2] = dst.w[d_idx] - src.w[s_idx] + vdotx = vij[0]*xij[0] + vij[1]*xij[1] + vij[2]*xij[2] + if vdotx < 0.0: + rij2 = xij[0]**2 + xij[1]**2 + xij[2]**2 + rij = np.sqrt(rij2) + hij = 0.5 * (dst.h[d_idx] + src.h[s_idx]) + mu = hij * vdotx / (rij2 + 0.01*hij*hij) + rhoij1 = 2.0 / (dst.rho[d_idx] + src.rho[s_idx]) + csi = dst.cs[d_idx] if dst_has_cs else c0 + csj = src.cs[s_idx] if src_has_cs else c0 + cij = 0.5 * (csi + csj) + piij = (-alpha*cij*mu + beta*mu*mu) * rhoij1 + dwij = [0.0, 0.0, 0.0] + kernel_obj.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + acc += -src.m[s_idx] * piij * np.asarray(dwij) + result[d_idx, :] = acc + return result + + +def _cpu_xsph_correction(particles, src_index, dst_index, dim, eps=0.5, + radius_scale=2.0, kernel='cubic'): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + kernel_obj = _cpu_kernel(dim, kernel) + src = particles[src_index] + dst = particles[dst_index] + result = np.zeros((dst.get_number_of_particles(), 3)) + + for d_idx in range(dst.get_number_of_particles()): + acc = np.zeros(3) + for s_idx in _neighbors(nnps, src_index, dst_index, d_idx): + xij = [ + dst.x[d_idx] - src.x[s_idx], + 0.0, + 0.0, + ] + vij = [ + dst.u[d_idx] - src.u[s_idx], + 0.0, + 0.0, + ] + if dim > 1: + xij[1] = dst.y[d_idx] - src.y[s_idx] + vij[1] = dst.v[d_idx] - src.v[s_idx] + if dim > 2: + xij[2] = dst.z[d_idx] - src.z[s_idx] + vij[2] = dst.w[d_idx] - src.w[s_idx] + rij = np.sqrt(xij[0]**2 + xij[1]**2 + xij[2]**2) + hij = 0.5 * (dst.h[d_idx] + src.h[s_idx]) + wij = kernel_obj.kernel(xij=xij, rij=rij, h=hij) + rhoij1 = 2.0 / (dst.rho[d_idx] + src.rho[s_idx]) + tmp = -eps * src.m[s_idx] * wij * rhoij1 + acc += tmp * np.asarray(vij) + result[d_idx, :] = acc + return result + + +def _cpu_wcsph_dt(particles, pa_index, dim, c0, cfl, dt_min, dt_max, + radius_scale=2.0): + nnps = LinkedListNNPS( + dim=dim, particles=particles, radius_scale=radius_scale + ) + pa = particles[pa_index] + max_cfl = 0.0 + max_force = 0.0 + hmin = np.min(pa.h) + for i in range(pa.get_number_of_particles()): + for j in _neighbors(nnps, pa_index, pa_index, i): + xij = [pa.x[i] - pa.x[j], 0.0, 0.0] + vij = [pa.u[i] - pa.u[j], 0.0, 0.0] + if dim > 1: + xij[1] = pa.y[i] - pa.y[j] + vij[1] = pa.v[i] - pa.v[j] + if dim > 2: + xij[2] = pa.z[i] - pa.z[j] + vij[2] = pa.w[i] - pa.w[j] + rij2 = xij[0]**2 + xij[1]**2 + xij[2]**2 + if rij2 > 1.0e-12: + hij = 0.5 * (pa.h[i] + pa.h[j]) + vdotx = vij[0]*xij[0] + vij[1]*xij[1] + vij[2]*xij[2] + max_cfl = max(max_cfl, abs(hij * vdotx / rij2) + c0) + max_force = max( + max_force, + pa.au[i]*pa.au[i] + pa.av[i]*pa.av[i] + pa.aw[i]*pa.aw[i] + ) + result = dt_max + if max_cfl > 0.0: + result = min(result, cfl * hmin / max_cfl) + if max_force > 0.0: + result = min(result, cfl * np.sqrt(hmin / np.sqrt(max_force))) + return min(max(result, dt_min), dt_max) + + +def test_warp_isothermal_eos_matches_cpu_and_pulls_pressure(): + pa = get_particle_array( + name='fluid', + rho=[900.0, 1000.0, 1100.0], + p=[0.0, 0.0, 0.0], + backend='warp', + ) + expected = 5.0 + 20.0*20.0*(pa.rho - 1000.0) + + actual = compute_isothermal_eos(pa, rho0=1000.0, c0=20.0, + p0=5.0).get() + pa.gpu.pull('p') + + assert np.allclose(actual, expected) + assert np.allclose(pa.p, expected) + + +def test_warp_tait_eos_matches_cpu_and_pulls_pressure_and_sound_speed(): + rho = np.asarray([0.9, 1.0, 1.1, 1.25]) + pa = get_particle_array( + name='fluid', + rho=rho, + p=np.zeros_like(rho), + cs=np.zeros_like(rho), + backend='warp', + ) + expected_p, expected_cs = _cpu_tait_eos( + rho, rho0=1.0, c0=20.0, gamma=7.0, p0=0.5 + ) + + p, cs = compute_tait_eos( + pa, rho0=1.0, c0=20.0, gamma=7.0, p0=0.5 + ) + pa.gpu.pull('p', 'cs') + + assert np.allclose(p.get(), expected_p) + assert np.allclose(cs.get(), expected_cs) + assert np.allclose(pa.p, expected_p) + assert np.allclose(pa.cs, expected_cs) + + +def test_warp_summation_density_matches_cpu_in_2d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_summation_density(particles, 0, 0, dim=2) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + actual = compute_summation_density(nnps, 0, 0).get() + + assert np.allclose(actual, expected) + + +def test_warp_gaussian_summation_density_matches_pysph_kernel(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 0.62], + y=[0.0, 0.0, 0.1, -0.05], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.35, 0.3], + m=[1.0, 2.0, 1.5, 1.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_summation_density( + particles, 0, 0, dim=2, radius_scale=3.0, kernel='gaussian' + ) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=3.0) + + actual = compute_summation_density( + nnps, 0, 0, kernel='gaussian' + ).get() + + assert np.allclose(actual, expected) + + +def test_warp_wendland_summation_density_matches_pysph_kernel_in_3d(): + # ADR-0005: the new WendlandQuintic kernel id (2) must match PySPH's + # WendlandQuintic(dim=3) device-side. Wendland C2 support is q < 2, so + # radius_scale=2.0. Genuine 3D coords exercise the dim==3 normalization + # (21/(16 pi h^3)). + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 0.15], + y=[0.0, 0.05, 0.1, -0.08], + z=[0.0, 0.1, -0.05, 0.12], + h=[0.25, 0.25, 0.35, 0.3], + m=[1.0, 2.0, 1.5, 1.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_summation_density( + particles, 0, 0, dim=3, radius_scale=2.0, kernel='wendland' + ) + nnps = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + actual = compute_summation_density(nnps, 0, 0, kernel='wendland').get() + + assert np.allclose(actual, expected, rtol=1e-5, atol=1e-6) + + +def test_warp_summation_density_matches_cpu_cross_array_in_3d_and_pulls_rho(): + fluid = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.1, 0.0, 1.5], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + backend='warp', + ) + solid = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.25, 0.25], + m=[1.0, 1.0], + backend='warp', + ) + particles = [fluid, solid] + expected = _cpu_summation_density(particles, 0, 1, dim=3) + nnps = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + compute_summation_density(nnps, 0, 1) + solid.gpu.pull('rho') + + assert np.allclose(solid.rho, expected) + + +def test_warp_periodic_summation_density_matches_cpu_min_image(): + # Periodic in x with nx>=4 cells AND an out-of-box source particle (x=1.08, + # image at 0.08): exercises both the minimum-image distance and the + # wrap-binning of out-of-box positions (a clamped bin would mis-place 1.08 + # into the edge cell and the query at 0.30 would miss it). Matches a + # brute-force CPU min-image summation density. + lx = 1.0 + x = np.array([0.05, 0.30, 0.55, 0.80, 1.08]) + y = np.zeros_like(x) + z = np.zeros_like(x) + h = np.ones_like(x) * 0.12 # cell_min=0.24 -> nx=4 (clamp-bug regime) + m = np.ones_like(x) + pa = get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), h=h.copy(), + m=m.copy(), rho=np.zeros_like(x), backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + nnps.set_periodic_box({'xmin': 0.0, 'xmax': lx, 'periodic_in_x': True}) + assert nnps._bounds['periodic_x'] and nnps._bounds['nx'] >= 3 + + compute_summation_density(nnps, 0, 0, neighbor_mode='grid') + pa.gpu.pull('rho') + + cpu = CubicSpline(dim=2) + ref = np.zeros_like(x) + for i in range(len(x)): + total = 0.0 + for j in range(len(x)): + dx = x[i] - x[j] + dx -= lx * np.round(dx / lx) # minimum image in x + rij = abs(dx) + hij = 0.5 * (h[i] + h[j]) + total += m[j] * cpu.kernel(xij=[dx, 0.0, 0.0], rij=rij, h=hij) + ref[i] = total + + assert np.allclose(pa.rho, ref, rtol=1e-4, atol=1e-4) + # Without the wrap the two edge particles would each miss a neighbor; with + # it they pick up the across-boundary contribution (self + >=1 neighbor). + assert pa.rho[0] > 1.5 * m[0] * cpu.kernel(xij=[0, 0, 0], rij=0.0, h=h[0]) + + +def test_warp_periodic_box_rejects_invalid_setups(): + # A box too small for the support (L < 3*radius_scale*h) and a periodic flag + # without min/max must raise clearly rather than silently clamp or KeyError. + x = np.array([0.1, 0.5, 0.9]) + z = np.zeros_like(x) + + def make(h_val): + return get_particle_array( + name='fluid', x=x.copy(), y=z.copy(), z=z.copy(), + h=np.ones_like(x) * h_val, m=np.ones_like(x), + rho=np.zeros_like(x), backend='warp', + ) + + nnps_small = UniformGridWarpNNPS(dim=2, particles=[make(0.4)], + radius_scale=2.0) + with pytest.raises(ValueError): + # support = 2*0.4 = 0.8, L = 1 -> floor(1/0.8) = 1 < 3 + nnps_small.set_periodic_box( + {'xmin': 0.0, 'xmax': 1.0, 'periodic_in_x': True}) + + nnps_missing = UniformGridWarpNNPS(dim=2, particles=[make(0.1)], + radius_scale=2.0) + with pytest.raises(ValueError): + nnps_missing.set_periodic_box({'periodic_in_x': True}) + + +def test_warp_periodic_lattice_density_is_uniform(): + # A uniform lattice tiling a doubly-periodic box has, by symmetry, the same + # density at every particle -- including boundary particles. A non-periodic + # neighbor search would show boundary deficiency (lower density at edges). + n = 12 + dx = 1.0 / n + coords = (np.arange(n) + 0.5) * dx + gx, gy = np.meshgrid(coords, coords) + x = gx.ravel() + y = gy.ravel() + z = np.zeros_like(x) + h = np.ones_like(x) * 1.3 * dx + m = np.ones_like(x) * dx * dx # rho0 = 1 + pa = get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), h=h.copy(), + m=m.copy(), rho=np.zeros_like(x), backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + nnps.set_periodic_box({ + 'xmin': 0.0, 'xmax': 1.0, 'ymin': 0.0, 'ymax': 1.0, + 'periodic_in_x': True, 'periodic_in_y': True, + }) + compute_summation_density(nnps, 0, 0, neighbor_mode='grid') + pa.gpu.pull('rho') + + mean_rho = float(np.mean(pa.rho)) + # Uniform to fp32 scale -- no boundary deficiency. + assert np.std(pa.rho) < 1e-4 * mean_rho + # A well-sampled lattice reproduces rho0 to a few percent. + assert abs(mean_rho - 1.0) < 0.05 + + +def test_warp_continuity_matches_cpu_in_2d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + u=[1.0, 0.5, -0.5, 0.0], + v=[0.0, 0.2, 0.4, -0.1], + w=[0.0, 0.0, 0.0, 0.0], + arho=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_continuity(particles, 0, 0, dim=2) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + actual = compute_continuity(nnps, 0, 0).get() + + assert np.allclose(actual, expected) + + +def test_warp_continuity_matches_cpu_cross_array_in_3d_and_pulls_arho(): + fluid = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.1, 0.0, 1.5], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + u=[1.0, 0.5, -0.5, 0.0], + v=[0.0, 0.2, 0.4, -0.1], + w=[0.1, 0.3, -0.2, 0.0], + backend='warp', + ) + solid = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.25, 0.25], + m=[1.0, 1.0], + u=[-0.1, 0.2], + v=[0.3, -0.2], + w=[0.0, 0.1], + arho=[0.0, 0.0], + backend='warp', + ) + particles = [fluid, solid] + expected = _cpu_continuity(particles, 0, 1, dim=3) + nnps = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + compute_continuity(nnps, 0, 1) + solid.gpu.pull('arho') + + assert np.allclose(solid.arho, expected) + + +def test_warp_pressure_gradient_matches_cpu_in_2d(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + rho=[1.0, 1.1, 0.9, 1.2], + p=[2.0, 3.0, 1.5, 0.5], + au=[0.0, 0.0, 0.0, 0.0], + av=[0.0, 0.0, 0.0, 0.0], + aw=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_pressure_gradient(particles, 0, 0, dim=2) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + au, av, aw = compute_pressure_gradient(nnps, 0, 0) + + assert np.allclose(au.get(), expected[:, 0]) + assert np.allclose(av.get(), expected[:, 1]) + assert np.allclose(aw.get(), expected[:, 2]) + + +def test_warp_gaussian_pressure_gradient_matches_pysph_kernel(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 0.62], + y=[0.0, 0.0, 0.1, -0.05], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.25, 0.25, 0.35, 0.3], + m=[1.0, 2.0, 1.5, 1.0], + rho=[1.0, 1.1, 0.9, 1.2], + p=[2.0, 3.0, 1.5, 0.5], + au=[0.0, 0.0, 0.0, 0.0], + av=[0.0, 0.0, 0.0, 0.0], + aw=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + expected = _cpu_pressure_gradient( + particles, 0, 0, dim=2, radius_scale=3.0, kernel='gaussian' + ) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=3.0) + + au, av, aw = compute_pressure_gradient( + nnps, 0, 0, kernel='gaussian' + ) + + assert np.allclose(au.get(), expected[:, 0]) + assert np.allclose(av.get(), expected[:, 1]) + assert np.allclose(aw.get(), expected[:, 2]) + + +def test_warp_pressure_gradient_matches_cpu_cross_array_in_3d_and_pulls_accel(): + fluid = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.4, 1.5], + y=[0.0, 0.0, 0.1, 1.5], + z=[0.0, 0.1, 0.0, 1.5], + h=[0.25, 0.25, 0.35, 0.25], + m=[1.0, 2.0, 1.5, 1.0], + rho=[1.0, 1.1, 0.9, 1.2], + p=[2.0, 3.0, 1.5, 0.5], + backend='warp', + ) + solid = get_particle_array( + name='solid', + x=[0.1, 0.8], + y=[0.0, 0.0], + z=[0.0, 0.0], + h=[0.25, 0.25], + m=[1.0, 1.0], + rho=[1.05, 0.95], + p=[2.5, 1.0], + au=[0.0, 0.0], + av=[0.0, 0.0], + aw=[0.0, 0.0], + backend='warp', + ) + particles = [fluid, solid] + expected = _cpu_pressure_gradient(particles, 0, 1, dim=3) + nnps = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + compute_pressure_gradient(nnps, 0, 1) + solid.gpu.pull('au', 'av', 'aw') + + assert np.allclose(solid.au, expected[:, 0]) + assert np.allclose(solid.av, expected[:, 1]) + assert np.allclose(solid.aw, expected[:, 2]) + + +def test_warp_artificial_viscosity_matches_cpu_and_adds_to_acceleration(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.45, 1.2], + y=[0.0, 0.03, -0.02, 0.1], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.35, 0.35, 0.4, 0.35], + m=[1.0, 1.5, 1.2, 0.8], + rho=[1.0, 1.1, 0.9, 1.2], + cs=[4.0, 5.0, 6.0, 7.0], + u=[1.0, -1.0, -0.2, 0.0], + v=[0.0, 0.05, -0.1, 0.0], + w=[0.0, 0.0, 0.0, 0.0], + au=[0.5, -0.25, 0.1, 0.0], + av=[0.0, 0.2, -0.1, 0.0], + aw=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + alpha = 0.1 + beta = 0.2 + c0 = 5.0 + initial = np.column_stack([pa.au.copy(), pa.av.copy(), pa.aw.copy()]) + expected = initial + _cpu_artificial_viscosity( + particles, 0, 0, dim=2, alpha=alpha, beta=beta, c0=c0 + ) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + au, av, aw = compute_artificial_viscosity( + nnps, 0, 0, alpha=alpha, beta=beta, c0=c0 + ) + + assert np.allclose(au.get(), expected[:, 0]) + assert np.allclose(av.get(), expected[:, 1]) + assert np.allclose(aw.get(), expected[:, 2]) + + +def test_warp_xsph_correction_matches_cpu_reference(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.45, 0.7], + y=[0.0, 0.03, -0.02, 0.1], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.35, 0.35, 0.4, 0.35], + m=[1.0, 1.5, 1.2, 0.8], + rho=[1.0, 1.1, 0.9, 1.2], + u=[1.0, -1.0, -0.2, 0.0], + v=[0.0, 0.05, -0.1, 0.0], + w=[0.0, 0.0, 0.0, 0.0], + ax=[0.0, 0.0, 0.0, 0.0], + ay=[0.0, 0.0, 0.0, 0.0], + az=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + eps = 0.5 + expected = _cpu_xsph_correction( + particles, 0, 0, dim=2, eps=eps, radius_scale=3.0, + kernel='gaussian' + ) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=3.0) + + ax, ay, az = compute_xsph_correction( + nnps, 0, 0, eps=eps, kernel='gaussian' + ) + + assert np.allclose(ax.get(), expected[:, 0]) + assert np.allclose(ay.get(), expected[:, 1]) + assert np.allclose(az.get(), expected[:, 2]) + + +def test_warp_adaptive_timestep_matches_cpu_reference_and_clamps(): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.45, 0.7], + y=[0.0, 0.03, -0.02, 0.1], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.35, 0.35, 0.4, 0.35], + m=[1.0, 1.5, 1.2, 0.8], + u=[1.0, -1.0, -0.2, 0.0], + v=[0.0, 0.05, -0.1, 0.0], + w=[0.0, 0.0, 0.0, 0.0], + au=[4.0, -0.5, 0.25, 0.0], + av=[0.0, 0.2, -0.1, 0.0], + aw=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + particles = [pa] + c0 = 5.0 + cfl = 0.3 + dt_min = 1.0e-6 + dt_max = 1.0e-2 + expected = _cpu_wcsph_dt( + particles, 0, dim=2, c0=c0, cfl=cfl, dt_min=dt_min, + dt_max=dt_max + ) + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + + actual = compute_wcsph_adaptive_timestep( + nnps, 0, c0=c0, cfl=cfl, dt_min=dt_min, dt_max=dt_max + ) + pa.gpu.pull('dt_cfl', 'dt_force') + + assert np.isclose(actual, expected) + assert np.all(np.isfinite(pa.dt_cfl)) + assert np.all(np.isfinite(pa.dt_force)) + + +def test_warp_apply_body_force_adds_ramped_gravity_to_acceleration(): + # ADR-0005: gravity is a body force == acceleration; apply_body_force adds + # ramp*g to au/av/aw (matching MomentumEquation's gz), under dim>1/dim>2 + # guards. dim=3 with gz<0 and a partial n_damp ramp; aw must update. + au0 = np.array([1.0, -0.5, 0.2]) + av0 = np.array([0.0, 0.3, -0.1]) + aw0 = np.array([0.4, 0.0, -0.2]) + pa = get_particle_array( + name='fluid', + x=[0.0, 0.1, 0.2], y=[0.0, 0.05, -0.05], z=[0.0, 0.1, -0.1], + h=[0.3, 0.3, 0.3], m=[1.0, 1.0, 1.0], + au=au0.copy(), av=av0.copy(), aw=aw0.copy(), + backend='warp', + ) + gx, gy, gz, ramp = 0.0, 0.0, -9.81, 0.5 + apply_body_force(pa, gx=gx, gy=gy, gz=gz, dim=3, ramp=ramp) + pa.gpu.pull('au', 'av', 'aw') + + assert np.allclose(pa.au, au0 + ramp * gx, rtol=1e-5, atol=1e-7) + assert np.allclose(pa.av, av0 + ramp * gy, rtol=1e-5, atol=1e-7) + assert np.allclose(pa.aw, aw0 + ramp * gz, rtol=1e-5, atol=1e-7) + + +def test_warp_apply_body_force_default_is_noop_and_respects_2d_guard(): + # Default g=0 leaves acceleration unchanged; under dim=2 the aw (z) + # component is never touched even with a nonzero gz. + au0 = np.array([1.0, -0.5]) + av0 = np.array([0.2, 0.3]) + aw0 = np.array([0.7, -0.4]) + pa = get_particle_array( + name='fluid', + x=[0.0, 0.1], y=[0.0, 0.05], z=[0.0, 0.0], + h=[0.3, 0.3], m=[1.0, 1.0], + au=au0.copy(), av=av0.copy(), aw=aw0.copy(), + backend='warp', + ) + apply_body_force(pa, dim=2) # default g=0 -> no-op + apply_body_force(pa, gz=-9.81, dim=2) # 2D: aw untouched + pa.gpu.pull('au', 'av', 'aw') + + assert np.allclose(pa.au, au0) + assert np.allclose(pa.av, av0) + assert np.allclose(pa.aw, aw0) + + +def test_warp_adaptive_timestep_matches_cpu_reference_and_clamps_in_3d(): + # dim=3 pin (ADR-0005): the CFL dt-factors (vij.xij/rij2) and dt_force + # (au^2+av^2+aw^2) must include the z/w third component. Mirrors the 2D + # test with nonzero z, w, aw; _cpu_wcsph_dt already handles dim=3. + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.45, 0.7], + y=[0.0, 0.03, -0.02, 0.1], + z=[0.0, 0.05, -0.03, 0.08], + h=[0.35, 0.35, 0.4, 0.35], + m=[1.0, 1.5, 1.2, 0.8], + u=[1.0, -1.0, -0.2, 0.0], + v=[0.0, 0.05, -0.1, 0.0], + w=[0.0, 0.1, -0.05, 0.0], + au=[4.0, -0.5, 0.25, 0.0], + av=[0.0, 0.2, -0.1, 0.0], + aw=[0.0, 0.3, -0.2, 0.0], + backend='warp', + ) + particles = [pa] + c0 = 5.0 + cfl = 0.3 + dt_min = 1.0e-6 + dt_max = 1.0e-2 + expected = _cpu_wcsph_dt( + particles, 0, dim=3, c0=c0, cfl=cfl, dt_min=dt_min, dt_max=dt_max + ) + nnps = UniformGridWarpNNPS(dim=3, particles=particles, radius_scale=2.0) + + actual = compute_wcsph_adaptive_timestep( + nnps, 0, c0=c0, cfl=cfl, dt_min=dt_min, dt_max=dt_max + ) + pa.gpu.pull('dt_cfl', 'dt_force') + + assert np.isclose(actual, expected) + assert np.all(np.isfinite(pa.dt_cfl)) + assert np.all(np.isfinite(pa.dt_force)) + + +def test_warp_equation_helpers_accept_prebuilt_neighbor_cache(monkeypatch): + pa = get_particle_array( + name='fluid', + x=[0.0, 0.2, 0.45, 0.7], + y=[0.0, 0.03, -0.02, 0.1], + z=[0.0, 0.0, 0.0, 0.0], + h=[0.35, 0.35, 0.4, 0.35], + m=[1.0, 1.5, 1.2, 0.8], + rho=[1.0, 1.1, 0.9, 1.2], + p=[2.0, 3.0, 1.5, 0.5], + u=[1.0, -1.0, -0.2, 0.0], + v=[0.0, 0.05, -0.1, 0.0], + w=[0.0, 0.0, 0.0, 0.0], + au=[0.0, 0.0, 0.0, 0.0], + av=[0.0, 0.0, 0.0, 0.0], + aw=[0.0, 0.0, 0.0, 0.0], + arho=[0.0, 0.0, 0.0, 0.0], + ax=[0.0, 0.0, 0.0, 0.0], + ay=[0.0, 0.0, 0.0, 0.0], + az=[0.0, 0.0, 0.0, 0.0], + backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + cache = nnps.build_neighbor_cache_gpu(0, 0) + calls = [] + original = nnps.build_neighbor_cache_gpu + + def counted_cache(src_index, dst_index): + calls.append((src_index, dst_index)) + return original(src_index, dst_index) + + monkeypatch.setattr(nnps, 'build_neighbor_cache_gpu', counted_cache) + + compute_pressure_gradient(nnps, 0, 0) + assert calls == [(0, 0)] + + compute_pressure_gradient(nnps, 0, 0, cache=cache) + compute_artificial_viscosity(nnps, 0, 0, alpha=0.1, cache=cache) + compute_continuity(nnps, 0, 0, cache=cache) + compute_xsph_correction(nnps, 0, 0, eps=0.5, cache=cache) + assert calls == [(0, 0)] + + +def test_warp_continuity_step_builds_no_flat_neighbor_cache(monkeypatch): + # ADR-0004: both neighbor consumers (fused equations + adaptive CFL + # dt-factors) walk the cell list directly, so the continuity step never + # materializes a flat CSR neighbor list. adaptive_dt=True exercises both + # consumers; the grid itself is still built (via _build_grid). + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + pa = get_particle_array( + name='fluid', + x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35]), + m=np.asarray([1.0, 1.5, 1.2, 0.8]), + rho=np.asarray([1.0, 1.03, 0.98, 1.01]), + p=np.zeros_like(x), + cs=np.ones_like(x) * 5.0, + u=np.asarray([0.1, -0.05, 0.2, 0.0]), + v=np.asarray([0.0, 0.15, -0.1, 0.05]), + w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + ax=np.zeros_like(x), ay=np.zeros_like(x), az=np.zeros_like(x), + arho=np.zeros_like(x), + backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + original = nnps.build_neighbor_cache_gpu + grid_original = nnps._build_grid + calls = [] + grid_calls = [] + + def counted_cache(src_index, dst_index): + calls.append((src_index, dst_index)) + return original(src_index, dst_index) + + def counted_grid(src_index): + grid_calls.append(src_index) + return grid_original(src_index) + + monkeypatch.setattr(nnps, 'build_neighbor_cache_gpu', counted_cache) + monkeypatch.setattr(nnps, '_build_grid', counted_grid) + + wc_sph_leapfrog_step( + nnps, dt=1.0e-3, rho0=1.0, c0=5.0, alpha=0.1, beta=0.0, + eos='tait', gamma=7.0, xsph_eps=0.5, adaptive_dt=True, + cfl=0.3, dt_min=1.0e-8, dt_max=1.0e-2, + density_mode='continuity' + ) + + # No flat neighbor cache anywhere on the continuity path, and the grid is + # consulted (built once per half-stage, reused by both consumers). + assert calls == [] + assert len(grid_calls) >= 2 + + +def test_warp_equation_helpers_reject_custom_output_names(): + # The generator-backed helpers write the block's canonical output arrays; + # a non-default out_prop/out_props can no longer be honored, so it must + # fail fast rather than silently writing the wrong array. The guard runs + # before any device work, so nnps is never dereferenced here. + with pytest.raises(ValueError): + compute_summation_density(None, out_prop='rho_custom') + with pytest.raises(ValueError): + compute_continuity(None, out_prop='arho_custom') + with pytest.raises(ValueError): + compute_pressure_gradient(None, out_props=('bu', 'bv', 'bw')) + with pytest.raises(ValueError): + compute_artificial_viscosity(None, out_props=('bu', 'bv', 'bw')) + with pytest.raises(ValueError): + compute_xsph_correction(None, out_props=('bx', 'by', 'bz')) + + +def test_warp_fused_accel_matches_separate_helpers(): + # Fusion-consistency check: after the generator migration both sides are + # generator-backed (the fused 4-equation group vs the four single-block + # helpers), so this asserts that fusing N blocks equals running them + # separately and composing (pressure gradient overwrite, viscosity add). + def make_pa(): + x = np.asarray([0.0, 0.2, 0.45, 0.7, 1.1]) + y = np.asarray([0.0, 0.03, -0.02, 0.1, -0.15]) + z = np.zeros_like(x) + return get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35, 0.38]), + m=np.asarray([1.0, 1.5, 1.2, 0.8, 1.1]), + rho=np.asarray([1.0, 1.1, 0.9, 1.2, 1.05]), + p=np.asarray([2.0, 3.0, 1.5, 0.5, 1.2]), + cs=np.asarray([5.0, 5.0, 5.0, 5.0, 5.0]), + u=np.asarray([1.0, -1.0, -0.2, 0.0, 0.3]), + v=np.asarray([0.0, 0.05, -0.1, 0.0, 0.2]), + w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + arho=np.zeros_like(x), ax=np.zeros_like(x), ay=np.zeros_like(x), + az=np.zeros_like(x), backend='warp', + ) + + alpha, beta, eps, kernel = 0.15, 0.05, 0.5, 'gaussian' + + # Reference: the four separate helpers chained on one cache. Inputs are + # pushed once, then helpers run with push=False so artificial viscosity + # accumulates onto the pressure-gradient result instead of clobbering it. + pa_sep = make_pa() + nnps_sep = UniformGridWarpNNPS(dim=2, particles=[pa_sep], radius_scale=3.0) + pa_sep.gpu.push( + 'x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', 'u', 'v', 'w', + 'au', 'av', 'aw', 'arho', 'ax', 'ay', 'az' + ) + cache_sep = nnps_sep.build_neighbor_cache_gpu(0, 0) + compute_pressure_gradient( + nnps_sep, 0, 0, kernel=kernel, cache=cache_sep, push=False + ) + compute_artificial_viscosity( + nnps_sep, 0, 0, alpha=alpha, beta=beta, kernel=kernel, + cache=cache_sep, push=False + ) + compute_continuity( + nnps_sep, 0, 0, kernel=kernel, cache=cache_sep, push=False + ) + compute_xsph_correction( + nnps_sep, 0, 0, eps=eps, kernel=kernel, cache=cache_sep, push=False + ) + pa_sep.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + # Fused: one generated group kernel. + pa_fused = make_pa() + nnps_fused = UniformGridWarpNNPS( + dim=2, particles=[pa_fused], radius_scale=3.0 + ) + compute_wcsph_accel_continuity( + nnps_fused, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True + ) + pa_fused.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + for name in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + assert np.allclose( + getattr(pa_sep, name), getattr(pa_fused, name), + rtol=1e-5, atol=1e-6 + ), name + + +def test_warp_fused_accel_matches_separate_helpers_in_3d(): + # dim=3 pin (ADR-0005): fusing the 4-equation group equals running the four + # single-block helpers and composing, with genuine 3D positions/velocities + # (nonzero z, w) so the third component is exercised on both sides. + def make_pa(): + x = np.asarray([0.0, 0.2, 0.45, 0.7, 1.1]) + y = np.asarray([0.0, 0.03, -0.02, 0.1, -0.15]) + z = np.asarray([0.0, 0.08, -0.05, 0.12, 0.04]) + return get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35, 0.38]), + m=np.asarray([1.0, 1.5, 1.2, 0.8, 1.1]), + rho=np.asarray([1.0, 1.1, 0.9, 1.2, 1.05]), + p=np.asarray([2.0, 3.0, 1.5, 0.5, 1.2]), + cs=np.asarray([5.0, 5.0, 5.0, 5.0, 5.0]), + u=np.asarray([1.0, -1.0, -0.2, 0.0, 0.3]), + v=np.asarray([0.0, 0.05, -0.1, 0.0, 0.2]), + w=np.asarray([0.1, -0.2, 0.15, -0.05, 0.0]), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + arho=np.zeros_like(x), ax=np.zeros_like(x), ay=np.zeros_like(x), + az=np.zeros_like(x), backend='warp', + ) + + alpha, beta, eps, kernel = 0.15, 0.05, 0.5, 'gaussian' + + pa_sep = make_pa() + nnps_sep = UniformGridWarpNNPS(dim=3, particles=[pa_sep], radius_scale=3.0) + pa_sep.gpu.push( + 'x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', 'u', 'v', 'w', + 'au', 'av', 'aw', 'arho', 'ax', 'ay', 'az' + ) + cache_sep = nnps_sep.build_neighbor_cache_gpu(0, 0) + compute_pressure_gradient( + nnps_sep, 0, 0, kernel=kernel, cache=cache_sep, push=False + ) + compute_artificial_viscosity( + nnps_sep, 0, 0, alpha=alpha, beta=beta, kernel=kernel, + cache=cache_sep, push=False + ) + compute_continuity( + nnps_sep, 0, 0, kernel=kernel, cache=cache_sep, push=False + ) + compute_xsph_correction( + nnps_sep, 0, 0, eps=eps, kernel=kernel, cache=cache_sep, push=False + ) + pa_sep.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + pa_fused = make_pa() + nnps_fused = UniformGridWarpNNPS( + dim=3, particles=[pa_fused], radius_scale=3.0 + ) + compute_wcsph_accel_continuity( + nnps_fused, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True + ) + pa_fused.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + for name in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + assert np.allclose( + getattr(pa_sep, name), getattr(pa_fused, name), + rtol=1e-5, atol=1e-6 + ), name + + +def test_warp_grid_direct_accel_matches_flat_fused(): + # ADR-0004: the grid-direct fused kernel must visit exactly the neighbor + # set the flat CSR list contained (same support cutoff), so its result + # matches the flat fused kernel to fp32 reordering scale. This isolates the + # neighbor-mode change from the equation math. + def make_pa(): + x = np.asarray([0.0, 0.2, 0.45, 0.7, 1.1, 0.15, 0.9]) + y = np.asarray([0.0, 0.03, -0.02, 0.1, -0.15, 0.25, 0.18]) + z = np.zeros_like(x) + return get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35, 0.38, 0.36, 0.34]), + m=np.asarray([1.0, 1.5, 1.2, 0.8, 1.1, 0.95, 1.05]), + rho=np.asarray([1.0, 1.1, 0.9, 1.2, 1.05, 0.97, 1.03]), + p=np.asarray([2.0, 3.0, 1.5, 0.5, 1.2, 0.8, 1.7]), + cs=np.ones_like(x) * 5.0, + u=np.asarray([1.0, -1.0, -0.2, 0.0, 0.3, 0.4, -0.3]), + v=np.asarray([0.0, 0.05, -0.1, 0.0, 0.2, -0.15, 0.1]), + w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + arho=np.zeros_like(x), ax=np.zeros_like(x), ay=np.zeros_like(x), + az=np.zeros_like(x), backend='warp', + ) + + alpha, beta, eps, kernel = 0.15, 0.05, 0.5, 'gaussian' + + pa_flat = make_pa() + nnps_flat = UniformGridWarpNNPS(dim=2, particles=[pa_flat], radius_scale=3.0) + compute_wcsph_accel_continuity( + nnps_flat, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True, neighbor_mode='flat' + ) + pa_flat.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + pa_grid = make_pa() + nnps_grid = UniformGridWarpNNPS(dim=2, particles=[pa_grid], radius_scale=3.0) + compute_wcsph_accel_continuity( + nnps_grid, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True, neighbor_mode='grid' + ) + pa_grid.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + for name in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + assert np.allclose( + getattr(pa_flat, name), getattr(pa_grid, name), + rtol=1e-5, atol=1e-6 + ), name + + +def test_warp_grid_direct_accel_matches_flat_fused_in_3d(): + # dim=3 pin (ADR-0005): the grid-direct fused kernel (27-cell triple-loop + # walk + 3D cell id) must visit exactly the flat CSR neighbor set in 3D, + # matching the flat fused result to fp32 reordering scale. Nonzero z, w. + def make_pa(): + x = np.asarray([0.0, 0.2, 0.45, 0.7, 1.1, 0.15, 0.9]) + y = np.asarray([0.0, 0.03, -0.02, 0.1, -0.15, 0.25, 0.18]) + z = np.asarray([0.0, 0.07, -0.04, 0.11, 0.05, -0.08, 0.13]) + return get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35, 0.38, 0.36, 0.34]), + m=np.asarray([1.0, 1.5, 1.2, 0.8, 1.1, 0.95, 1.05]), + rho=np.asarray([1.0, 1.1, 0.9, 1.2, 1.05, 0.97, 1.03]), + p=np.asarray([2.0, 3.0, 1.5, 0.5, 1.2, 0.8, 1.7]), + cs=np.ones_like(x) * 5.0, + u=np.asarray([1.0, -1.0, -0.2, 0.0, 0.3, 0.4, -0.3]), + v=np.asarray([0.0, 0.05, -0.1, 0.0, 0.2, -0.15, 0.1]), + w=np.asarray([0.1, -0.2, 0.15, -0.05, 0.0, 0.25, -0.1]), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + arho=np.zeros_like(x), ax=np.zeros_like(x), ay=np.zeros_like(x), + az=np.zeros_like(x), backend='warp', + ) + + alpha, beta, eps, kernel = 0.15, 0.05, 0.5, 'gaussian' + + pa_flat = make_pa() + nnps_flat = UniformGridWarpNNPS(dim=3, particles=[pa_flat], radius_scale=3.0) + compute_wcsph_accel_continuity( + nnps_flat, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True, neighbor_mode='flat' + ) + pa_flat.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + pa_grid = make_pa() + nnps_grid = UniformGridWarpNNPS(dim=3, particles=[pa_grid], radius_scale=3.0) + compute_wcsph_accel_continuity( + nnps_grid, 0, 0, alpha=alpha, beta=beta, eps=eps, kernel=kernel, + push=True, neighbor_mode='grid' + ) + pa_grid.gpu.pull('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + for name in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + assert np.allclose( + getattr(pa_flat, name), getattr(pa_grid, name), + rtol=1e-5, atol=1e-6 + ), name + + +def test_warp_continuity_step_issues_single_fused_launch_per_stage( + monkeypatch): + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + pa = get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35]), + m=np.asarray([1.0, 1.5, 1.2, 0.8]), + rho=np.asarray([1.0, 1.03, 0.98, 1.01]), + p=np.zeros_like(x), cs=np.ones_like(x) * 5.0, + u=np.asarray([0.1, -0.05, 0.2, 0.0]), + v=np.asarray([0.0, 0.15, -0.1, 0.05]), w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + ax=np.zeros_like(x), ay=np.zeros_like(x), az=np.zeros_like(x), + arho=np.zeros_like(x), backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + + counts = {'fused': 0, 'separate': 0} + fused_original = warp_sph.compute_wcsph_accel_continuity + + def counted_fused(*args, **kwargs): + counts['fused'] += 1 + return fused_original(*args, **kwargs) + + def counted_separate(name): + original = getattr(warp_sph, name) + + def wrapper(*args, **kwargs): + counts['separate'] += 1 + return original(*args, **kwargs) + return wrapper + + monkeypatch.setattr( + warp_sph, 'compute_wcsph_accel_continuity', counted_fused + ) + for name in ('compute_pressure_gradient', 'compute_artificial_viscosity', + 'compute_continuity', 'compute_xsph_correction'): + monkeypatch.setattr(warp_sph, name, counted_separate(name)) + + wc_sph_leapfrog_step( + nnps, dt=1.0e-3, rho0=1.0, c0=5.0, alpha=0.1, beta=0.0, + eos='tait', gamma=7.0, xsph_eps=0.5, density_mode='continuity' + ) + + # One fused launch per PEC half-stage, and none of the per-equation + # neighbor-loop helpers are called in the continuity path. + assert counts['fused'] == 2 + assert counts['separate'] == 0 + + +def test_warp_leapfrog_adaptive_timestep_scale_and_step_cap(): + def make_pa(): + x = np.asarray([0.0, 0.2, 0.45, 0.7]) + y = np.asarray([0.0, 0.03, -0.02, 0.1]) + z = np.zeros_like(x) + return get_particle_array( + name='fluid', + x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35]), + m=np.asarray([1.0, 1.5, 1.2, 0.8]), + rho=np.ones_like(x), + p=np.zeros_like(x), + cs=np.ones_like(x) * 5.0, + u=np.asarray([1.0, -1.0, -0.2, 0.0]), + v=np.asarray([0.0, 0.05, -0.1, 0.0]), + w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + backend='warp', + ) + + raw_pa = make_pa() + raw_nnps = UniformGridWarpNNPS( + dim=2, particles=[raw_pa], radius_scale=2.0 + ) + _result, raw_dt = wc_sph_leapfrog_step( + raw_nnps, dt=1.0e-3, rho0=1.0, c0=5.0, adaptive_dt=True, + cfl=0.3, dt_min=1.0e-8, dt_max=1.0e-2, return_dt=True + ) + + scaled_pa = make_pa() + scaled_nnps = UniformGridWarpNNPS( + dim=2, particles=[scaled_pa], radius_scale=2.0 + ) + _result, scaled_dt = wc_sph_leapfrog_step( + scaled_nnps, dt=1.0e-3, rho0=1.0, c0=5.0, adaptive_dt=True, + cfl=0.3, dt_min=1.0e-8, dt_max=1.0e-2, return_dt=True, + adaptive_dt_scale=0.5 + ) + + capped_pa = make_pa() + capped_nnps = UniformGridWarpNNPS( + dim=2, particles=[capped_pa], radius_scale=2.0 + ) + _result, capped_dt = wc_sph_leapfrog_step( + capped_nnps, dt=1.0e-3, rho0=1.0, c0=5.0, adaptive_dt=True, + cfl=0.3, dt_min=1.0e-8, dt_max=1.0e-2, return_dt=True, + step_dt_max=1.0e-7 + ) + + assert np.isclose(scaled_dt, 0.5 * raw_dt) + assert np.isclose(capped_dt, 1.0e-7) + + +def test_warp_wc_sph_euler_step_with_tait_eos_uses_sound_speed_in_avisc(): + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + h = np.asarray([0.35, 0.35, 0.4, 0.35]) + m = np.asarray([1.0, 1.5, 1.2, 0.8]) + u = np.asarray([0.5, -0.4, 0.2, 0.0]) + v = np.asarray([0.0, 0.15, -0.1, 0.05]) + w = np.zeros_like(x) + dt = 1.0e-3 + rho0 = 1.0 + c0 = 5.0 + gamma = 7.0 + p0 = 0.1 + alpha = 0.1 + beta = 0.2 + pa = get_particle_array( + name='fluid', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=np.zeros_like(x), + p=np.zeros_like(x), + cs=np.zeros_like(x), + u=u.copy(), + v=v.copy(), + w=w.copy(), + au=np.zeros_like(x), + av=np.zeros_like(x), + aw=np.zeros_like(x), + backend='warp', + ) + particles = [pa] + expected_rho = _cpu_summation_density(particles, 0, 0, dim=2) + expected_p, expected_cs = _cpu_tait_eos( + expected_rho, rho0=rho0, c0=c0, gamma=gamma, p0=p0 + ) + expected_pa = get_particle_array( + name='expected', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=expected_rho, + p=expected_p, + cs=expected_cs, + u=u.copy(), + v=v.copy(), + w=w.copy(), + backend='warp', + ) + expected_acc = ( + _cpu_pressure_gradient([expected_pa], 0, 0, dim=2) + + _cpu_artificial_viscosity( + [expected_pa], 0, 0, dim=2, alpha=alpha, beta=beta, c0=c0 + ) + ) + expected_u = u + dt*expected_acc[:, 0] + expected_v = v + dt*expected_acc[:, 1] + expected_w = w + dt*expected_acc[:, 2] + + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + wc_sph_euler_step( + nnps, dt=dt, rho0=rho0, c0=c0, p0=p0, alpha=alpha, beta=beta, + eos='tait', gamma=gamma + ) + pa.gpu.pull('rho', 'p', 'cs', 'au', 'av', 'aw', 'u', 'v', 'w') + + assert np.all(np.isfinite(pa.cs)) + assert np.allclose(pa.rho, expected_rho) + assert np.allclose(pa.p, expected_p) + assert np.allclose(pa.cs, expected_cs) + assert np.allclose(pa.au, expected_acc[:, 0], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.av, expected_acc[:, 1], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.aw, expected_acc[:, 2], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.u, expected_u, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.v, expected_v, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.w, expected_w, rtol=1e-5, atol=1e-5) + + +def test_warp_euler_step_updates_velocity_and_position_on_device(): + pa = get_particle_array( + name='fluid', + x=[0.0, 1.0, 2.0], + y=[0.5, 1.5, 2.5], + z=[1.0, 2.0, 3.0], + u=[1.0, -1.0, 0.5], + v=[0.0, 2.0, -0.5], + w=[0.25, -0.25, 1.0], + au=[0.1, 0.2, -0.3], + av=[-0.2, 0.4, 0.1], + aw=[0.5, -0.5, 0.25], + backend='warp', + ) + dt = 0.25 + old_x = pa.x.copy() + old_y = pa.y.copy() + old_z = pa.z.copy() + old_u = pa.u.copy() + old_v = pa.v.copy() + old_w = pa.w.copy() + au = pa.au.copy() + av = pa.av.copy() + aw = pa.aw.copy() + + euler_step(pa, dt=dt, dim=2) + pa.gpu.pull('x', 'y', 'z', 'u', 'v', 'w') + + expected_u = old_u + dt*au + expected_v = old_v + dt*av + expected_w = old_w + dt*aw + assert np.allclose(pa.u, expected_u) + assert np.allclose(pa.v, expected_v) + assert np.allclose(pa.w, expected_w) + assert np.allclose(pa.x, old_x + dt*expected_u) + assert np.allclose(pa.y, old_y + dt*expected_v) + assert np.allclose(pa.z, old_z) + + +def test_warp_leapfrog_kick_drift_and_wrap_update_device_state(): + pa = get_particle_array( + name='fluid', + x=[0.95, -0.2, 1.8], + y=[0.9, 1.2, -0.1], + z=[0.0, 0.0, 0.0], + u=[0.4, 0.5, -0.25], + v=[0.3, -0.4, 0.1], + w=[0.0, 0.0, 0.0], + au=[0.2, -0.1, 0.4], + av=[-0.3, 0.2, 0.0], + aw=[0.0, 0.0, 0.0], + backend='warp', + ) + dt = 0.5 + expected_u = pa.u + 0.5*dt*pa.au + expected_v = pa.v + 0.5*dt*pa.av + expected_x = pa.x + dt*expected_u + expected_y = pa.y + dt*expected_v + expected_x = expected_x - np.floor(expected_x) + expected_y = expected_y - np.floor(expected_y) + + leapfrog_kick(pa, dt=0.5*dt, dim=2) + leapfrog_drift(pa, dt=dt, dim=2, push=False) + wrap_periodic( + pa, + {'xmin': 0.0, 'xmax': 1.0, 'ymin': 0.0, 'ymax': 1.0}, + dim=2, + ) + pa.gpu.pull('x', 'y', 'z', 'u', 'v', 'w') + + assert np.allclose(pa.u, expected_u) + assert np.allclose(pa.v, expected_v) + assert np.allclose(pa.x, expected_x) + assert np.allclose(pa.y, expected_y) + assert np.allclose(pa.z, np.zeros(3)) + + +def test_warp_wc_sph_euler_step_matches_cpu_expected_state(): + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + h = np.asarray([0.35, 0.35, 0.4, 0.35]) + m = np.asarray([1.0, 1.5, 1.2, 0.8]) + u = np.asarray([0.1, -0.05, 0.2, 0.0]) + v = np.asarray([0.0, 0.15, -0.1, 0.05]) + w = np.zeros_like(x) + dt = 1.0e-3 + rho0 = 1.0 + c0 = 5.0 + p0 = 0.1 + pa = get_particle_array( + name='fluid', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=np.zeros_like(x), + p=np.zeros_like(x), + u=u.copy(), + v=v.copy(), + w=w.copy(), + au=np.zeros_like(x), + av=np.zeros_like(x), + aw=np.zeros_like(x), + backend='warp', + ) + particles = [pa] + expected_rho = _cpu_summation_density(particles, 0, 0, dim=2) + expected_p = p0 + c0*c0*(expected_rho - rho0) + expected_pa = get_particle_array( + name='expected', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=expected_rho, + p=expected_p, + backend='warp', + ) + expected_acc = _cpu_pressure_gradient([expected_pa], 0, 0, dim=2) + expected_u = u + dt*expected_acc[:, 0] + expected_v = v + dt*expected_acc[:, 1] + expected_w = w + dt*expected_acc[:, 2] + expected_x = x + dt*expected_u + expected_y = y + dt*expected_v + + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + wc_sph_euler_step(nnps, dt=dt, rho0=rho0, c0=c0, p0=p0) + pa.gpu.pull('rho', 'p', 'au', 'av', 'aw', 'x', 'y', 'z', 'u', 'v', 'w') + + assert np.all(np.isfinite(pa.rho)) + assert np.all(np.isfinite(pa.p)) + assert np.all(np.isfinite(pa.au)) + assert np.allclose(pa.rho, expected_rho) + assert np.allclose(pa.p, expected_p) + assert np.allclose(pa.au, expected_acc[:, 0], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.av, expected_acc[:, 1], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.aw, expected_acc[:, 2], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.u, expected_u, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.v, expected_v, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.w, expected_w, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.x, expected_x, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.y, expected_y, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.z, z) + + +def test_warp_summation_step_paths_build_no_flat_neighbor_cache(monkeypatch): + # Grid-direct extended to the summation paths: wc_sph_euler_step and the + # summation KDK leapfrog step walk the cell list directly, so neither builds + # a flat CSR neighbor cache; the grid is consulted instead. + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + + def make_pa(): + return get_particle_array( + name='fluid', x=x.copy(), y=y.copy(), z=z.copy(), + h=np.asarray([0.35, 0.35, 0.4, 0.35]), + m=np.asarray([1.0, 1.5, 1.2, 0.8]), + rho=np.ones_like(x), p=np.zeros_like(x), cs=np.ones_like(x) * 5.0, + u=np.asarray([0.1, -0.05, 0.2, 0.0]), + v=np.asarray([0.0, 0.15, -0.1, 0.05]), w=z.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + ax=np.zeros_like(x), ay=np.zeros_like(x), az=np.zeros_like(x), + backend='warp', + ) + + def run_and_check(step_fn): + pa = make_pa() + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + flat_orig = nnps.build_neighbor_cache_gpu + grid_orig = nnps._build_grid + calls = [] + grid_calls = [] + + def counted_cache(src_index, dst_index): + calls.append((src_index, dst_index)) + return flat_orig(src_index, dst_index) + + def counted_grid(src_index): + grid_calls.append(src_index) + return grid_orig(src_index) + + monkeypatch.setattr(nnps, 'build_neighbor_cache_gpu', counted_cache) + monkeypatch.setattr(nnps, '_build_grid', counted_grid) + step_fn(nnps) + assert calls == [], 'built a flat cache: %r' % (calls,) + assert len(grid_calls) >= 1 + + run_and_check(lambda n: wc_sph_euler_step( + n, dt=1.0e-3, rho0=1.0, c0=5.0, alpha=0.1, eos='tait')) + run_and_check(lambda n: wc_sph_leapfrog_step( + n, dt=1.0e-3, rho0=1.0, c0=5.0, alpha=0.1, eos='tait', xsph_eps=0.5, + adaptive_dt=True, cfl=0.3, dt_min=1.0e-8, dt_max=1.0e-2, + density_mode='summation')) + + +def test_warp_wc_sph_leapfrog_step_matches_cpu_expected_state(): + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + h = np.asarray([0.35, 0.35, 0.4, 0.35]) + m = np.asarray([1.0, 1.5, 1.2, 0.8]) + u = np.asarray([0.1, -0.05, 0.2, 0.0]) + v = np.asarray([0.0, 0.15, -0.1, 0.05]) + w = np.zeros_like(x) + dt = 1.0e-3 + rho0 = 1.0 + c0 = 5.0 + p0 = 0.1 + pa = get_particle_array( + name='fluid', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=np.zeros_like(x), + p=np.zeros_like(x), + u=u.copy(), + v=v.copy(), + w=w.copy(), + au=np.zeros_like(x), + av=np.zeros_like(x), + aw=np.zeros_like(x), + backend='warp', + ) + particles = [pa] + rho_n = _cpu_summation_density(particles, 0, 0, dim=2) + p_n = p0 + c0*c0*(rho_n - rho0) + force_pa_n = get_particle_array( + name='expected_n', + x=x.copy(), + y=y.copy(), + z=z.copy(), + h=h.copy(), + m=m.copy(), + rho=rho_n, + p=p_n, + backend='warp', + ) + acc_n = _cpu_pressure_gradient([force_pa_n], 0, 0, dim=2) + u_half = u + 0.5*dt*acc_n[:, 0] + v_half = v + 0.5*dt*acc_n[:, 1] + w_half = w + 0.5*dt*acc_n[:, 2] + x_np1 = x + dt*u_half + y_np1 = y + dt*v_half + z_np1 = z + dt*w_half + + force_pa_np1 = get_particle_array( + name='expected_np1', + x=x_np1.copy(), + y=y_np1.copy(), + z=z_np1.copy(), + h=h.copy(), + m=m.copy(), + rho=np.zeros_like(x), + p=np.zeros_like(x), + backend='warp', + ) + rho_np1 = _cpu_summation_density([force_pa_np1], 0, 0, dim=2) + p_np1 = p0 + c0*c0*(rho_np1 - rho0) + force_pa_np1.rho[:] = rho_np1 + force_pa_np1.p[:] = p_np1 + acc_np1 = _cpu_pressure_gradient([force_pa_np1], 0, 0, dim=2) + expected_u = u_half + 0.5*dt*acc_np1[:, 0] + expected_v = v_half + 0.5*dt*acc_np1[:, 1] + expected_w = w_half + 0.5*dt*acc_np1[:, 2] + + nnps = UniformGridWarpNNPS(dim=2, particles=particles, radius_scale=2.0) + wc_sph_leapfrog_step(nnps, dt=dt, rho0=rho0, c0=c0, p0=p0) + pa.gpu.pull('rho', 'p', 'au', 'av', 'aw', 'x', 'y', 'z', 'u', 'v', 'w') + + assert np.all(np.isfinite(pa.rho)) + assert np.all(np.isfinite(pa.p)) + assert np.all(np.isfinite(pa.au)) + assert np.allclose(pa.rho, rho_np1) + assert np.allclose(pa.p, p_np1) + assert np.allclose(pa.au, acc_np1[:, 0], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.av, acc_np1[:, 1], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.aw, acc_np1[:, 2], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.u, expected_u, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.v, expected_v, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.w, expected_w, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.x, x_np1, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.y, y_np1, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.z, z_np1, rtol=1e-5, atol=1e-5) + + +def test_warp_wcsph_pec_stage_uses_saved_density_and_xsph_advection(): + x = np.asarray([0.0, 0.2, 0.45]) + y = np.asarray([0.0, 0.1, -0.05]) + z = np.zeros_like(x) + rho = np.asarray([1.0, 1.1, 0.95]) + u = np.asarray([0.1, -0.05, 0.2]) + v = np.asarray([0.0, 0.15, -0.1]) + w = np.zeros_like(x) + au = np.asarray([0.3, -0.2, 0.05]) + av = np.asarray([-0.1, 0.25, 0.4]) + aw = np.zeros_like(x) + ax = np.asarray([0.02, -0.01, 0.03]) + ay = np.asarray([0.0, 0.015, -0.025]) + az = np.zeros_like(x) + arho = np.asarray([0.4, -0.2, 0.1]) + dt = 0.01 + stage = 0.5 + pa = get_particle_array( + name='fluid', + x=x.copy(), y=y.copy(), z=z.copy(), + u=u.copy(), v=v.copy(), w=w.copy(), rho=rho.copy(), + au=au.copy(), av=av.copy(), aw=aw.copy(), + ax=ax.copy(), ay=ay.copy(), az=az.copy(), arho=arho.copy(), + backend='warp', + ) + + save_wcsph_state(pa, dim=2) + wcsph_pec_stage(pa, dt=dt, stage=stage, dim=2, xsph=True, push=False) + pa.gpu.pull('x', 'y', 'rho', 'u', 'v') + + dt_factor = dt * stage + assert np.allclose(pa.u, u + dt_factor * au) + assert np.allclose(pa.v, v + dt_factor * av) + assert np.allclose(pa.rho, rho + dt_factor * arho) + assert np.allclose(pa.x, x + dt_factor * (u + ax)) + assert np.allclose(pa.y, y + dt_factor * (v + ay)) + + +def test_warp_wc_sph_leapfrog_continuity_mode_matches_cpu_pec_state(): + x = np.asarray([0.0, 0.2, 0.45, 1.2]) + y = np.asarray([0.0, 0.1, -0.05, 0.2]) + z = np.zeros_like(x) + h = np.asarray([0.35, 0.35, 0.4, 0.35]) + m = np.asarray([1.0, 1.5, 1.2, 0.8]) + rho = np.asarray([1.0, 1.03, 0.98, 1.01]) + u = np.asarray([0.1, -0.05, 0.2, 0.0]) + v = np.asarray([0.0, 0.15, -0.1, 0.05]) + w = np.zeros_like(x) + dt = 1.0e-3 + rho0 = 1.0 + c0 = 5.0 + gamma = 7.0 + xsph_eps = 0.5 + pa = get_particle_array( + name='fluid', + x=x.copy(), y=y.copy(), z=z.copy(), h=h.copy(), m=m.copy(), + rho=rho.copy(), p=np.zeros_like(x), cs=np.ones_like(x)*c0, + u=u.copy(), v=v.copy(), w=w.copy(), + au=np.zeros_like(x), av=np.zeros_like(x), aw=np.zeros_like(x), + ax=np.zeros_like(x), ay=np.zeros_like(x), az=np.zeros_like(x), + arho=np.zeros_like(x), backend='warp', + ) + p_n, cs_n = _cpu_tait_eos(rho, rho0=rho0, c0=c0, gamma=gamma) + cpu_n = get_particle_array( + name='cpu_n', + x=x.copy(), y=y.copy(), z=z.copy(), h=h.copy(), m=m.copy(), + rho=rho.copy(), p=p_n, cs=cs_n, + u=u.copy(), v=v.copy(), w=w.copy(), backend='warp', + ) + particles_n = [cpu_n] + acc_n = _cpu_pressure_gradient(particles_n, 0, 0, dim=2) + arho_n = _cpu_continuity(particles_n, 0, 0, dim=2) + xsph_n = _cpu_xsph_correction( + particles_n, 0, 0, dim=2, eps=xsph_eps + ) + u_half = u + 0.5 * dt * acc_n[:, 0] + v_half = v + 0.5 * dt * acc_n[:, 1] + w_half = w + 0.5 * dt * acc_n[:, 2] + rho_half = rho + 0.5 * dt * arho_n + x_half = x + 0.5 * dt * (u + xsph_n[:, 0]) + y_half = y + 0.5 * dt * (v + xsph_n[:, 1]) + z_half = z + 0.5 * dt * (w + xsph_n[:, 2]) + + p_half, cs_half = _cpu_tait_eos( + rho_half, rho0=rho0, c0=c0, gamma=gamma + ) + cpu_half = get_particle_array( + name='cpu_half', + x=x_half.copy(), y=y_half.copy(), z=z_half.copy(), + h=h.copy(), m=m.copy(), rho=rho_half.copy(), p=p_half, + cs=cs_half, u=u_half.copy(), v=v_half.copy(), w=w_half.copy(), + backend='warp', + ) + particles_half = [cpu_half] + acc_half = _cpu_pressure_gradient(particles_half, 0, 0, dim=2) + arho_half = _cpu_continuity(particles_half, 0, 0, dim=2) + xsph_half = _cpu_xsph_correction( + particles_half, 0, 0, dim=2, eps=xsph_eps + ) + expected_u = u + dt * acc_half[:, 0] + expected_v = v + dt * acc_half[:, 1] + expected_w = w + dt * acc_half[:, 2] + expected_rho = rho + dt * arho_half + expected_x = x + dt * (u_half + xsph_half[:, 0]) + expected_y = y + dt * (v_half + xsph_half[:, 1]) + expected_z = z + dt * (w_half + xsph_half[:, 2]) + + nnps = UniformGridWarpNNPS(dim=2, particles=[pa], radius_scale=2.0) + wc_sph_leapfrog_step( + nnps, dt=dt, rho0=rho0, c0=c0, gamma=gamma, eos='tait', + xsph_eps=xsph_eps, density_mode='continuity' + ) + pa.gpu.pull('x', 'y', 'z', 'rho', 'u', 'v', 'w', 'au', 'av', 'aw', 'arho') + + assert np.allclose(pa.x, expected_x, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.y, expected_y, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.z, expected_z, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.u, expected_u, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.v, expected_v, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.w, expected_w, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.rho, expected_rho, rtol=1e-5, atol=1e-5) + assert np.allclose(pa.au, acc_half[:, 0], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.av, acc_half[:, 1], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.aw, acc_half[:, 2], rtol=1e-5, atol=1e-5) + assert np.allclose(pa.arho, arho_half, rtol=1e-5, atol=1e-5) + + +def test_warp_tait_eos_hg_correction_clamps_density_and_pressure(): + # ADR-0005: the wall EOS clamps rho to >= rho0 (so p >= 0) then Tait. + rho0, c0, gamma = 1000.0, 20.0, 7.0 + rho_in = np.array([900.0, 1000.0, 1100.0]) # below, at, above rho0 + pa = get_particle_array( + name='wall', x=[0.0, 0.1, 0.2], y=[0.0, 0.0, 0.0], z=[0.0, 0.0, 0.0], + h=[0.2, 0.2, 0.2], m=[1.0, 1.0, 1.0], rho=rho_in.copy(), + backend='warp', + ) + compute_tait_eos_hg_correction(pa, rho0=rho0, c0=c0, gamma=gamma) + pa.gpu.pull('rho', 'p', 'cs') + + # below-rho0 is clamped up to rho0; at/above are unchanged. + assert np.allclose(pa.rho, [1000.0, 1000.0, 1100.0]) + # clamped and at-rest give p == 0; compression gives p > 0; never negative. + assert np.all(pa.p >= -1.0e-6) + assert np.isclose(pa.p[0], 0.0, atol=1.0e-4) + assert np.isclose(pa.p[1], 0.0, atol=1.0e-4) + assert pa.p[2] > 0.0 + b = rho0 * c0 * c0 / gamma + expected_p2 = b * ((1100.0 / rho0) ** gamma - 1.0) + assert np.isclose(pa.p[2], expected_p2, rtol=1.0e-4) + + +def test_warp_dam_break_step_two_array_3d_is_finite_and_walls_fixed(): + # ADR-0005: a minimal 3D dam-break step over a fluid block + a fixed wall + # floor. Walls must not move, all fields must stay finite, and gravity must + # drive the fluid downward (mean vertical velocity becomes negative). + dx = 0.1 + fx, fy, fz = np.mgrid[0:3, 0:3, 0:3] + fx = (fx.ravel() + 1) * dx + fy = (fy.ravel() + 1) * dx + fz = (fz.ravel() + 2) * dx # fluid block sits above the floor + wx, wy = np.mgrid[0:5, 0:5] + wx = wx.ravel() * dx + wy = wy.ravel() * dx + wz = np.zeros_like(wx) # single-layer wall floor at z=0 + rho0 = 1000.0 + h = 1.3 * dx + m = rho0 * dx ** 3 + fluid = get_particle_array( + name='fluid', x=fx, y=fy, z=fz, + h=np.ones(fx.size) * h, m=np.ones(fx.size) * m, + rho=np.ones(fx.size) * rho0, backend='warp', + ) + wall = get_particle_array( + name='wall', x=wx, y=wy, z=wz, + h=np.ones(wx.size) * h, m=np.ones(wx.size) * m, + rho=np.ones(wx.size) * rho0, backend='warp', + ) + nnps = UniformGridWarpNNPS(dim=3, particles=[fluid, wall], radius_scale=2.0) + + wall_x0, wall_y0, wall_z0 = wall.x.copy(), wall.y.copy(), wall.z.copy() + c0 = 10.0 * np.sqrt(2.0 * 9.81 * 0.3) + dt = 1.0e-4 + for step in range(5): + wc_sph_dam_break_step( + nnps, fluid_index=0, solid_indices=(1,), dt=dt, rho0=rho0, + c0=c0, gamma=7.0, alpha=0.1, beta=0.0, kernel='wendland', + xsph_eps=0.5, gz=-9.81, gravity_ramp=1.0, push=(step == 0), + ) + fluid.gpu.pull('x', 'y', 'z', 'u', 'v', 'w', 'rho', 'p') + wall.gpu.pull('x', 'y', 'z', 'rho', 'p') + + for arr in (fluid.x, fluid.y, fluid.z, fluid.u, fluid.v, fluid.w, + fluid.rho, fluid.p, wall.rho, wall.p): + assert np.all(np.isfinite(arr)) + # Walls are fixed (position unchanged). + assert np.allclose(wall.x, wall_x0) + assert np.allclose(wall.y, wall_y0) + assert np.allclose(wall.z, wall_z0) + # Wall pressure is non-negative (HG correction). + assert np.all(wall.p >= -1.0e-3) + # Gravity pulled the fluid down. + assert fluid.w.mean() < 0.0 + + + + +# --------------------------------------------------------------------------- +# Rigid-body moments (ADR-0006): the device atomic_add SUM-reduction + host +# finalize must reproduce PySPH RigidBodyMoments. The CPU reference Application +# cannot run on Python 3.14 (compyle uses the removed ast.Str), so the reference +# here is a faithful numpy reimplementation of rigid_body.py:90-207. +# --------------------------------------------------------------------------- +def _reference_rigid_moments(x, y, z, m, fx, fy, fz, body_id, nbody, omega): + res = {k: np.zeros((nbody, 3)) for k in + ('cm', 'force', 'torque', 'omega_dot')} + res['total_mass'] = np.zeros(nbody) + res['inertia'] = np.zeros((nbody, 3, 3)) + res['mi'] = np.zeros(nbody * 16) + for b in range(nbody): + c = body_id == b + mb, xb, yb, zb = m[c], x[c], y[c], z[c] + fxb, fyb, fzb = fx[c], fy[c], fz[c] + base = b * 16 + # The 16 raw reduction slots (about the ORIGIN), as the kernel builds. + mi = res['mi'] + mi[base + 0] = mb.sum() + mi[base + 1] = (mb * xb).sum(); mi[base + 2] = (mb * yb).sum() + mi[base + 3] = (mb * zb).sum() + mi[base + 4] = (mb * (yb * yb + zb * zb)).sum() + mi[base + 5] = (mb * (xb * xb + zb * zb)).sum() + mi[base + 6] = (mb * (xb * xb + yb * yb)).sum() + mi[base + 7] = -(mb * xb * yb).sum() + mi[base + 8] = -(mb * xb * zb).sum() + mi[base + 9] = -(mb * yb * zb).sum() + mi[base + 10] = fxb.sum(); mi[base + 11] = fyb.sum() + mi[base + 12] = fzb.sum() + mi[base + 13] = (yb * fzb - zb * fyb).sum() + mi[base + 14] = (zb * fxb - xb * fzb).sum() + mi[base + 15] = (xb * fyb - yb * fxb).sum() + # Finalize (parallel-axis inertia, torque about COM, omega_dot). + M = mi[base + 0] + cm = np.array([mi[base + 1], mi[base + 2], mi[base + 3]]) / M + cx, cy, cz = cm + inertia = np.array([ + [mi[base + 4] - (cy * cy + cz * cz) * M, + mi[base + 7] + cx * cy * M, mi[base + 8] + cx * cz * M], + [mi[base + 7] + cx * cy * M, + mi[base + 5] - (cx * cx + cz * cz) * M, + mi[base + 9] + cy * cz * M], + [mi[base + 8] + cx * cz * M, mi[base + 9] + cy * cz * M, + mi[base + 6] - (cx * cx + cy * cy) * M]]) + force = np.array([mi[base + 10], mi[base + 11], mi[base + 12]]) + torque = np.array([mi[base + 13], mi[base + 14], mi[base + 15]]) \ + - np.cross(cm, force) + w = omega[b] + res['total_mass'][b] = M + res['cm'][b] = cm + res['inertia'][b] = inertia + res['force'][b] = force + res['torque'][b] = torque + res['omega_dot'][b] = np.linalg.solve( + inertia, torque - np.cross(w, inertia @ w)) + return res + + +def _make_two_body(): + """Two asymmetric ellipsoidal bodies (distinct sizes / graded density / + one-sided forces) so the inertia tensor has real off-diagonal terms and the + net torque / omega_dot are non-trivial. Returns x,y,z,m,fx,fy,fz,body_id.""" + parts = [] + centers = [(0.05, 0.18, 0.03), (0.30, 0.10, -0.02)] + axes = [(0.025, 0.045, 0.015), (0.020, 0.018, 0.030)] + for bid, (cen, (ax, ay, az)) in enumerate(zip(centers, axes)): + g = np.linspace(-2 * max(ax, ay, az), 2 * max(ax, ay, az), 18) + X, Y, Z = np.meshgrid(g, g, g, indexing='ij') + sel = (X / ax) ** 2 + (Y / ay) ** 2 + (Z / az) ** 2 <= 1.0 + xb, yb, zb = X[sel], Y[sel], Z[sel] + dx = g[1] - g[0] + rho = 500.0 * (1.0 + 0.6 * xb / ax + 0.4 * zb / az) + mb = rho * dx ** 3 + gacc = 9.81 + fxb = mb * gacc * (0.8 * xb / ax + 0.3) + fyb = -mb * gacc + mb * gacc * 1.3 * np.clip( + 0.20 - (yb + cen[1]), 0.0, None) / 0.20 + fzb = mb * gacc * (0.5 * xb / ax) + parts.append((xb + cen[0], yb + cen[1], zb + cen[2], mb, + fxb, fyb, fzb, np.full(xb.size, bid, dtype=np.int32))) + cols = [np.concatenate([p[i] for p in parts]) for i in range(8)] + cols[7] = cols[7].astype(np.int32) + return cols + + +@pytest.mark.parametrize('use_double', [False, True]) +def test_rigid_body_moments_matches_reference_3d(use_double): + from compyle.config import get_config + x, y, z, m, fx, fy, fz, body_id = _make_two_body() + nbody = 2 + omega = np.array([[0.3, -0.5, 0.2], [-0.1, 0.4, -0.25]]) + + cfg = get_config() + old = cfg.use_double + cfg.use_double = use_double + try: + pa = get_particle_array(name='body', x=x, y=y, z=z, m=m, backend='warp') + pa.add_property('fx', data=fx) + pa.add_property('fy', data=fy) + pa.add_property('fz', data=fz) + pa.add_property('body_id', type='int', data=body_id) + got = compute_rigid_body_moments(pa, nbody=nbody, omega=omega, push=True) + finally: + cfg.use_double = old + + # Reference from inputs rounded to the device dtype -> isolates reduction / + # finalize exactness from the (inherent) lower precision of f32 inputs. + cast = np.float64 if use_double else np.float32 + xr, yr, zr, mr, fxr, fyr, fzr = ( + a.astype(cast).astype(np.float64) for a in (x, y, z, m, fx, fy, fz)) + ref = _reference_rigid_moments(xr, yr, zr, mr, fxr, fyr, fzr, + body_id, nbody, omega) + + # The raw 16-slot reduction is the new primitive: f64 accumulation of the + # same (dtype-matched) values -> matches numpy to f64 round-off either path. + rmi = np.where(np.abs(ref['mi']) > 1e-30, ref['mi'], 1.0) + assert np.max(np.abs((got['mi'] - ref['mi']) / rmi)) < 1e-9 + # Downstream moments (finalize has inherent cancellation in I and torque). + assert np.allclose(got['total_mass'], ref['total_mass'], rtol=1e-10) + assert np.allclose(got['cm'], ref['cm'], rtol=1e-9, atol=1e-13) + assert np.allclose(got['force'], ref['force'], rtol=1e-9, atol=1e-12) + assert np.allclose(got['inertia'], ref['inertia'], rtol=1e-7, atol=1e-12) + assert np.allclose(got['torque'], ref['torque'], rtol=1e-6, atol=1e-9) + assert np.allclose(got['omega_dot'], ref['omega_dot'], rtol=1e-5, atol=1e-7) + # Bodies are asymmetric: real torque and real angular acceleration. + assert np.linalg.norm(ref['torque']) > 1e-6 + assert np.linalg.norm(ref['omega_dot']) > 1e-3 + + +def test_rigid_moments_f32_kernel_is_accurate_and_deterministic(): + # The fp32 path accumulates in f64 (the ADR-0006 locked decision), so the + # atomic_add reduction stays accurate AND deterministic despite f32 inputs. + x, y, z, m, fx, fy, fz, body_id = _make_two_body() + nbody = 2 + n = x.size + + def f32(a): + return wp.array(a.astype(np.float32), dtype=wp.float32, device='cuda:0') + bid = wp.array(body_id.astype(np.int32), dtype=wp.int32, device='cuda:0') + args = [f32(a) for a in (m, x, y, z, fx, fy, fz)] + + def reduce_once(): + mi = wp.zeros(nbody * 16, dtype=wp.float64, device='cuda:0') + wp.launch(warp_sph._rigid_moments_reduce_f32, dim=n, + inputs=[bid] + args + [mi], device='cuda:0') + wp.synchronize_device('cuda:0') + return mi.numpy() + + runs = np.stack([reduce_once() for _ in range(8)]) + # f64 accumulation -> run-to-run spread at f64 round-off (~1e-16 relative), + # vs ~3e-6 for an fp32 accumulator (atomic_add is order-dependent). Not + # bit-identical, but ~6 orders tighter -- the ADR-0006 mitigation (P0). + denom = np.where(np.abs(runs[0]) > 1e-30, np.abs(runs[0]), 1.0) + assert np.max((runs.max(0) - runs.min(0)) / denom) < 1e-12 + + # Accurate vs a numpy reference from the SAME f32-rounded inputs. + x32, y32, z32, m32, fx32, fy32, fz32 = ( + a.astype(np.float32).astype(np.float64) + for a in (x, y, z, m, fx, fy, fz)) + ref = _reference_rigid_moments(x32, y32, z32, m32, fx32, fy32, fz32, + body_id, nbody, np.zeros((nbody, 3))) + nz = np.abs(ref['mi']) > 1e-30 + assert np.max(np.abs((runs[0][nz] - ref['mi'][nz]) / ref['mi'][nz])) < 1e-9 + + +def _make_warp_rigid_pa(use_double=False): + from compyle.config import get_config + x, y, z, m, fx, fy, fz, body_id = _make_two_body() + cfg = get_config() + old = cfg.use_double + cfg.use_double = use_double + try: + pa = get_particle_array(name='body', x=x, y=y, z=z, m=m, + backend='warp') + pa.add_property('fx', data=fx) + pa.add_property('fy', data=fy) + pa.add_property('fz', data=fz) + pa.add_property('body_id', type='int', data=body_id) + finally: + cfg.use_double = old + return pa, (x, y, z, m, fx, fy, fz, body_id) + + +@pytest.mark.parametrize('use_double', [False, True]) +def test_rigid_body_device_finalize_matches_numpy(use_double): + pa, arrays = _make_warp_rigid_pa(use_double) + x, y, z, m, fx, fy, fz, body_id = arrays + omega = np.array([[0.3, -0.5, 0.2], [-0.1, 0.4, -0.25]]) + state = create_rigid_body_state(pa, nbody=2, omega=omega) + + compute_rigid_body_moments_device(pa, state, push=True) + wp.synchronize_device(state.device) + + cast = np.float64 if use_double else np.float32 + rounded = [a.astype(cast).astype(np.float64) + for a in (x, y, z, m, fx, fy, fz)] + ref = _reference_rigid_moments( + *rounded, body_id, 2, omega) + assert np.array_equal(state.error.numpy(), np.zeros(2, dtype=np.int32)) + assert np.allclose(state.total_mass.numpy(), ref['total_mass'], rtol=1e-10) + assert np.allclose(state.cm.numpy().reshape(2, 3), ref['cm'], + rtol=1e-9, atol=1e-13) + assert np.allclose(state.inertia.numpy().reshape(2, 3, 3), ref['inertia'], + rtol=1e-7, atol=1e-12) + assert np.allclose(state.force.numpy().reshape(2, 3), ref['force'], + rtol=1e-9, atol=1e-12) + assert np.allclose(state.torque.numpy().reshape(2, 3), ref['torque'], + rtol=1e-6, atol=1e-9) + assert np.allclose(state.omega_dot.numpy().reshape(2, 3), + ref['omega_dot'], rtol=1e-5, atol=1e-7) + + +@pytest.mark.parametrize('use_double', [False, True]) +def test_rigid_body_device_rk2_matches_numpy(use_double): + pa, arrays = _make_warp_rigid_pa(use_double) + x, y, z, m, fx, fy, fz, body_id = arrays + vc0 = np.array([[0.2, -0.1, 0.05], [-0.08, 0.03, 0.12]]) + omega0 = np.array([[0.3, -0.5, 0.2], [-0.1, 0.4, -0.25]]) + state = create_rigid_body_state( + pa, nbody=2, vc=vc0, omega=omega0) + pa.gpu.push('m', 'x', 'y', 'z', 'fx', 'fy', 'fz') + save_rigid_body_state(pa, state) + dt = 2.0e-4 + + cast = np.float64 if use_double else np.float32 + x0, y0, z0, mr, fxr, fyr, fzr = [ + a.astype(cast).astype(np.float64) + for a in (x, y, z, m, fx, fy, fz)] + p0 = np.column_stack((x0, y0, z0)) + + ref0 = _reference_rigid_moments( + x0, y0, z0, mr, fxr, fyr, fzr, body_id, 2, omega0) + rel0 = p0 - ref0['cm'][body_id] + vel0 = vc0[body_id] + np.cross(omega0[body_id], rel0) + pmid = p0 + 0.5 * dt * vel0 + vc_mid = vc0 + 0.5 * dt * ref0['force'] / ref0['total_mass'][:, None] + omega_mid = omega0 + 0.5 * dt * ref0['omega_dot'] + + rigid_body_rk2_stage(pa, state, dt=dt, stage=0.5) + wp.synchronize_device(state.device) + assert np.allclose(state.vc.numpy().reshape(2, 3), vc_mid, + rtol=1e-10, atol=1e-12) + assert np.allclose(state.omega.numpy().reshape(2, 3), omega_mid, + rtol=1e-9, atol=1e-12) + got_mid = np.column_stack((pa.gpu.x.dev.numpy(), pa.gpu.y.dev.numpy(), + pa.gpu.z.dev.numpy())) + tol = 2e-7 if not use_double else 2e-12 + assert np.allclose(got_mid, pmid, rtol=tol, atol=tol) + + ref_mid = _reference_rigid_moments( + *pmid.T, mr, fxr, fyr, fzr, body_id, 2, omega_mid) + rel_mid = pmid - ref_mid['cm'][body_id] + vel_mid = vc_mid[body_id] + np.cross(omega_mid[body_id], rel_mid) + pfinal = p0 + dt * vel_mid + vc_final = vc0 + dt * ref_mid['force'] / ref_mid['total_mass'][:, None] + omega_final = omega0 + dt * ref_mid['omega_dot'] + + rigid_body_rk2_stage(pa, state, dt=dt, stage=1.0) + wp.synchronize_device(state.device) + got_final = np.column_stack((pa.gpu.x.dev.numpy(), pa.gpu.y.dev.numpy(), + pa.gpu.z.dev.numpy())) + got_vel = np.column_stack((pa.gpu.u.dev.numpy(), pa.gpu.v.dev.numpy(), + pa.gpu.w.dev.numpy())) + assert np.allclose(got_final, pfinal, rtol=tol, atol=tol) + assert np.allclose(got_vel, vel_mid, rtol=tol, atol=tol) + assert np.allclose(state.vc.numpy().reshape(2, 3), vc_final, + rtol=1e-10, atol=1e-12) + assert np.allclose(state.omega.numpy().reshape(2, 3), omega_final, + rtol=1e-8, atol=1e-11) + + +def test_rigid_body_device_stage_has_no_host_barrier(monkeypatch): + pa, _ = _make_warp_rigid_pa(False) + state = create_rigid_body_state(pa, nbody=2) + pa.gpu.push('m', 'x', 'y', 'z', 'fx', 'fy', 'fz') + save_rigid_body_state(pa, state) + + def forbidden(*args, **kwargs): + raise AssertionError("device rigid stage crossed a host barrier") + + with monkeypatch.context() as patch: + patch.setattr(warp_sph, '_rigid_finalize_moments', forbidden) + patch.setattr(warp_sph.wp, 'synchronize_device', forbidden) + patch.setattr(type(pa.gpu), 'pull', forbidden) + rigid_body_rk2_stage(pa, state, dt=1.0e-5, stage=0.5) + rigid_body_rk2_stage(pa, state, dt=1.0e-5, stage=1.0) + + wp.synchronize_device(state.device) + assert np.array_equal(state.error.numpy(), np.zeros(2, dtype=np.int32)) + + +def test_rigid_body_device_pure_translation_preserves_geometry(): + pa, arrays = _make_warp_rigid_pa(False) + body_id = arrays[-1] + vc = np.array([[0.2, -0.1, 0.05], [-0.08, 0.03, 0.12]]) + state = create_rigid_body_state(pa, nbody=2, vc=vc) + pa.fx[:] = 0.0 + pa.fy[:] = 0.0 + pa.fz[:] = 0.0 + pa.gpu.push('m', 'x', 'y', 'z', 'fx', 'fy', 'fz') + save_rigid_body_state(pa, state) + before = np.column_stack((arrays[0], arrays[1], arrays[2])) + + rigid_body_rk2_stage(pa, state, dt=1.0e-3, stage=0.5) + rigid_body_rk2_stage(pa, state, dt=1.0e-3, stage=1.0) + wp.synchronize_device(state.device) + after = np.column_stack((pa.gpu.x.dev.numpy(), pa.gpu.y.dev.numpy(), + pa.gpu.z.dev.numpy())) + for body in range(2): + idx = np.flatnonzero(body_id == body) + assert np.allclose(after[idx] - after[idx[0]], + before[idx] - before[idx[0]], atol=2e-7) + assert np.allclose(state.vc.numpy().reshape(2, 3), vc) + assert np.allclose(state.omega.numpy(), 0.0) + + +def test_rigid_body_state_rejects_singular_geometry(): + pa = get_particle_array(name='body', x=[0.0, 0.5, 1.0], + m=[1.0, 1.0, 1.0], backend='warp') + with pytest.raises(ValueError, match='singular inertia'): + create_rigid_body_state(pa) + + +# --------------------------------------------------------------------------- +# P3 Liu fluid/rigid coupling: deterministic two-pass reaction, rigid density +# staging, and the sibling coupled dam-break driver. +# --------------------------------------------------------------------------- +def _make_liu_pair_arrays(use_double=False): + from compyle.config import get_config + cfg = get_config() + old = cfg.use_double + cfg.use_double = use_double + try: + fluid = get_particle_array( + name='fluid', + x=[-0.08, 0.00, 0.07, 0.02], + y=[0.00, -0.05, 0.03, 0.06], + z=[0.01, 0.04, -0.02, 0.07], + h=[0.14] * 4, m=[0.8, 1.0, 0.9, 1.1], + rho=[1010.0, 1005.0, 1008.0, 1012.0], + p=[1200.0, 900.0, 1050.0, 1400.0], backend='warp') + body = get_particle_array( + name='body', + x=[0.10, 0.16, 0.12, 0.18], + y=[0.00, 0.04, -0.05, -0.02], + z=[0.00, 0.05, 0.06, -0.04], + h=[0.14] * 4, m=[0.7, 0.75, 0.8, 0.72], + rho=[1002.0, 1004.0, 1001.0, 1003.0], + p=[300.0, 450.0, 250.0, 350.0], backend='warp') + for pa, props in ((fluid, ('au', 'av', 'aw')), + (body, ('fx', 'fy', 'fz', 'V'))): + for prop in props: + pa.add_property(prop) + finally: + cfg.use_double = old + return fluid, body + + +def _cpu_liu_reference(fluid, body): + kernel = WendlandQuintic(dim=3) + fluid_acc = np.zeros((len(fluid.x), 3)) + body_force = np.zeros((len(body.x), 3)) + for i in range(len(fluid.x)): + for j in range(len(body.x)): + xij = np.array([fluid.x[i] - body.x[j], + fluid.y[i] - body.y[j], + fluid.z[i] - body.z[j]]) + rij = np.linalg.norm(xij) + hij = 0.5 * (fluid.h[i] + body.h[j]) + if rij >= 2.0 * hij: + continue + dwij = [0.0, 0.0, 0.0] + kernel.gradient(xij=xij, rij=rij, h=hij, grad=dwij) + t1 = (body.p[j] / body.rho[j]**2 + + fluid.p[i] / fluid.rho[i]**2) + acc = -body.m[j] * t1 * np.asarray(dwij) + fluid_acc[i] += acc + body_force[j] -= fluid.m[i] * acc + return fluid_acc, body_force + + +@pytest.mark.parametrize('use_double', [False, True]) +def test_warp_liu_coupling_matches_reference_and_reacts_equally(use_double): + fluid, body = _make_liu_pair_arrays(use_double) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, body], radius_scale=2.0) + ref_acc, ref_force = _cpu_liu_reference(fluid, body) + + initialize_rigid_body_force(body, gx=0.0, gy=0.0, gz=0.0, push=True) + compute_liu_fluid_rigid_coupling( + nnps, 0, 1, kernel='wendland', push=True) + fluid.gpu.pull('au', 'av', 'aw') + body.gpu.pull('fx', 'fy', 'fz') + got_acc = np.column_stack((fluid.au, fluid.av, fluid.aw)) + got_force = np.column_stack((body.fx, body.fy, body.fz)) + + assert np.allclose(got_acc, ref_acc, rtol=2e-5, atol=2e-6) + assert np.allclose(got_force, ref_force, rtol=2e-5, atol=2e-6) + total = (fluid.m[:, None] * got_acc).sum(axis=0) + got_force.sum(axis=0) + assert np.allclose(total, 0.0, atol=2e-6) + + +def test_warp_rigid_number_density_matches_cpu_wendland(): + _, body = _make_liu_pair_arrays() + nnps = UniformGridWarpNNPS(dim=3, particles=[body], radius_scale=2.0) + kernel = WendlandQuintic(dim=3) + ref = np.zeros(len(body.x)) + for i in range(len(body.x)): + for j in range(len(body.x)): + xij = np.array([body.x[i] - body.x[j], + body.y[i] - body.y[j], + body.z[i] - body.z[j]]) + rij = np.linalg.norm(xij) + hij = 0.5 * (body.h[i] + body.h[j]) + if rij < 2.0 * hij: + ref[i] += kernel.kernel(xij=xij, rij=rij, h=hij) + compute_rigid_number_density(nnps, 0, kernel='wendland', push=True) + body.gpu.pull('V') + assert np.allclose(body.V, ref, rtol=2e-5, atol=2e-5) + + +def test_warp_rigid_density_and_body_force_stages(): + x = np.array([0.0, 0.1, 0.0, 0.0]) + pa = get_particle_array( + name='body', x=x, y=[0.0, 0.0, 0.1, 0.0], + z=[0.0, 0.0, 0.0, 0.1], m=[1.0, 2.0, 1.5, 0.5], + rho=[1000.0, 1001.0, 999.0, 1002.0], backend='warp') + pa.add_property('arho', data=[2.0, -3.0, 4.0, 1.0]) + for prop in ('fx', 'fy', 'fz'): + pa.add_property(prop) + rho_start = pa.rho.copy() + save_rigid_body_density(pa, push=True) + rigid_body_density_stage(pa, dt=0.02, stage=0.5) + initialize_rigid_body_force(pa, gx=1.0, gy=-2.0, gz=-9.81, push=True) + pa.gpu.pull('rho', 'fx', 'fy', 'fz') + assert np.allclose(pa.rho, rho_start + 0.01 * pa.arho) + assert np.allclose(pa.fx, pa.m) + assert np.allclose(pa.fy, -2.0 * pa.m) + assert np.allclose(pa.fz, -9.81 * pa.m) + + +def test_warp_dam_break_rigid_step_is_finite_and_moves_body(): + # Small genuine 3D fluid/body interaction. The fixed wall is deliberately + # far away: this gates Liu + rigid EPEC without introducing contact yet. + fgrid = np.array(np.meshgrid( + [-0.12, -0.06, 0.0], [-0.06, 0.0, 0.06], + [-0.04, 0.02, 0.08], indexing='ij')).reshape(3, -1).T + bgrid = np.array(np.meshgrid( + [0.055, 0.105], [-0.025, 0.025], [0.015, 0.065], + indexing='ij')).reshape(3, -1).T + wgrid = np.array(np.meshgrid( + [0.55, 0.62], [-0.08, 0.08], [0.0, 0.12], + indexing='ij')).reshape(3, -1).T + + def pa_from(name, xyz, mass, rho): + n = len(xyz) + return get_particle_array( + name=name, x=xyz[:, 0], y=xyz[:, 1], z=xyz[:, 2], + h=np.full(n, 0.09), m=np.full(n, mass), + rho=np.full(n, rho), backend='warp') + + fluid = pa_from('fluid', fgrid, 0.001, 1010.0) + wall = pa_from('wall', wgrid, 0.001, 1000.0) + body = pa_from('body', bgrid, 0.002, 1000.0) + for pa in (fluid, wall): + for prop in ('p', 'cs', 'au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + pa.add_property(prop) + for prop in ('p', 'cs', 'arho', 'fx', 'fy', 'fz', 'V'): + body.add_property(prop) + + state = create_rigid_body_state(body, nbody=1) + nnps = UniformGridWarpNNPS( + dim=3, particles=[fluid, wall, body], radius_scale=2.0) + body0 = bgrid.copy() + wall0 = wgrid.copy() + dt = 1.0e-5 + wc_sph_dam_break_rigid_step( + nnps, state, fluid_index=0, wall_indices=(1,), rigid_index=2, + dt=dt, rho0=1000.0, c0=20.0, alpha=0.0, beta=0.0, + kernel='wendland', xsph_eps=0.0, gz=-9.81, push=True) + + fluid.gpu.pull('x', 'y', 'z', 'rho', 'u', 'v', 'w') + wall.gpu.pull('x', 'y', 'z') + body.gpu.pull('x', 'y', 'z', 'rho', 'u', 'v', 'w', 'fx', 'fy', 'fz') + got_body = np.column_stack((body.x, body.y, body.z)) + got_fluid = np.column_stack((fluid.x, fluid.y, fluid.z)) + assert np.isfinite(got_fluid).all() + assert np.isfinite(got_body).all() + assert np.isfinite(body.rho).all() + assert np.array_equal(state.error.numpy(), np.zeros(1, dtype=np.int32)) + assert np.linalg.norm(got_body - body0) > 0.0 + assert np.allclose(np.column_stack((wall.x, wall.y, wall.z)), wall0) + # One tiny step should preserve rigid distances to fp32 integration scale. + assert np.allclose( + np.linalg.norm(got_body - got_body[0], axis=1), + np.linalg.norm(body0 - body0[0], axis=1), atol=2e-7) + + +# --------------------------------------------------------------------------- +# Multilevel equation-group integration (ADR-0007, plan step 3): a generated +# group kernel in neighbor_mode='multilevel' walks the multilevel cell list and +# must produce the same physics as the uniform grid (identical accepted sets; +# only fp32 summation order differs). +# --------------------------------------------------------------------------- +def _mixed_resolution_2d(): + x = [0.0, 0.15, 0.3, 0.1, 0.4] + y = [0.0, 0.0, 0.0, 0.05, 0.05] + h = [0.1, 0.1, 0.1, 0.2, 0.2] # edges [0.1,0.2,0.4] -> levels [0,0,0,1,1] + m = [1.0, 1.0, 1.0, 1.0, 1.0] + return x, y, h, m + + +def test_warp_summation_density_multilevel_matches_grid_2d(): + x, y, h, m = _mixed_resolution_2d() + z = [0.0] * len(x) + ml_pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, m=m, + rho=[0.0] * len(x), backend='warp') + g_pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, m=m, + rho=[0.0] * len(x), backend='warp') + ml = MultilevelGridWarpNNPS(dim=2, particles=[ml_pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=2) + grid = UniformGridWarpNNPS(dim=2, particles=[g_pa], radius_scale=2.0) + rho_ml = warp_sph.compute_summation_density( + ml, kernel='cubic', neighbor_mode='multilevel').get() + rho_g = warp_sph.compute_summation_density( + grid, kernel='cubic', neighbor_mode='grid').get() + assert np.all(rho_ml > 0.0) + assert np.allclose(rho_ml, rho_g, rtol=1e-4, atol=1e-5), (rho_ml, rho_g) + + +def test_warp_summation_density_multilevel_matches_grid_3d(): + # 3D mixed resolution (the hard acceptance dim): a 3x3x3 fine block plus two + # coarse particles. Multilevel-generated density == uniform-grid density. + g = np.linspace(0.0, 0.2, 3) + FX, FY, FZ = np.meshgrid(g, g, g, indexing='ij') + fx, fy, fz = FX.ravel(), FY.ravel(), FZ.ravel() # 27 fine, h=0.1 + x = np.concatenate([fx, [0.05, 0.15]]) + y = np.concatenate([fy, [0.05, 0.15]]) + z = np.concatenate([fz, [0.05, 0.15]]) + h = np.concatenate([np.full(27, 0.1), [0.2, 0.2]]) # coarse level 1 + m = np.ones(x.size) + ml_pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, m=m, + rho=np.zeros(x.size), backend='warp') + g_pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h, m=m, + rho=np.zeros(x.size), backend='warp') + ml = MultilevelGridWarpNNPS(dim=3, particles=[ml_pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=2) + grid = UniformGridWarpNNPS(dim=3, particles=[g_pa], radius_scale=2.0) + rho_ml = warp_sph.compute_summation_density( + ml, kernel='wendland', neighbor_mode='multilevel').get() + rho_g = warp_sph.compute_summation_density( + grid, kernel='wendland', neighbor_mode='grid').get() + assert np.all(rho_ml > 0.0) + assert np.allclose(rho_ml, rho_g, rtol=1e-4, atol=1e-5), (rho_ml, rho_g) + + +def test_warp_multilevel_rejects_periodic_domain(): + # Per-level periodic tiling is deferred (ADR-0007): a multilevel run over a + # periodic box must raise, not silently use the non-periodic walk. + x = [0.1, 0.3, 0.5] + pa = get_particle_array( + name='fluid', x=x, y=[0.0] * 3, z=[0.0] * 3, + h=[0.1, 0.1, 0.1], m=[1.0] * 3, rho=[0.0] * 3, backend='warp', + ) + ml = MultilevelGridWarpNNPS(dim=1, particles=[pa], radius_scale=2.0, + h_ref=0.1, level_ratio=2.0, nlevels=2) + ml.set_periodic_box({'xmin': 0.0, 'xmax': 0.6}) + with pytest.raises(ValueError): + warp_sph.compute_summation_density( + ml, kernel='cubic', neighbor_mode='multilevel') diff --git a/pysph/base/warp_codegen.py b/pysph/base/warp_codegen.py new file mode 100644 index 000000000..d99916bbd --- /dev/null +++ b/pysph/base/warp_codegen.py @@ -0,0 +1,612 @@ +"""Dynamic Warp equation-group code generation (ADR-0003). + +Compose SPH physics as small ``WarpEquation`` building blocks and generate a +single fused Warp kernel per ``WarpGroup``, mirroring PySPH's equation/group +transpilation. Fusion is a *property of grouping*: the generator unions the +array signature of all equations in a group, computes the shared per-pair +geometry exactly once, and inlines each equation's per-pair ``loop`` body into a +single neighbor traversal. + +The model follows PySPH's group semantics: for each destination particle the +generated kernel loops its neighbors once and, for each pair ``(i, j)``, runs +every equation's ``loop`` snippet in order, each accumulating into a shared +per-output register ``_acc_``. The accumulators are written back to the +destination arrays once after the loop. + +A generated kernel is materialized by templating source from the equation +snippets, registering it with :mod:`linecache` so Warp can introspect it, and +wrapping it with ``wp.Kernel(func=..., source=...)``. Compiled kernels are +cached by structural signature ``(ordered equation signatures, dtype)`` so each +unique group compiles only once. +""" + +import hashlib +import linecache + +import numpy as np + +try: + import warp as wp +except ImportError: # pragma: no cover + wp = None + + +# Shared per-pair quantities the generator can compute once and expose to the +# ``loop`` snippets of every equation in a group. +SHARED_QUANTITIES = ( + 'dx', 'dy', 'dz', 'rij2', 'rij', 'hij', 'grad', 'wij', + 'vijx', 'vijy', 'vijz', +) + +# Position/smoothing arrays implied by the geometric shared quantities, and the +# velocity arrays implied by the relative-velocity shared quantities. These are +# auto-added to a group's array signature so equation blocks only declare the +# *extra* arrays they read. +_GEOM_POS = ('x', 'y', 'z') +_GEOM_H = ('h',) +_GEOM_VEL = ('u', 'v', 'w') + + +class WarpEquation(object): + """Base class for a composable Warp SPH equation block. + + Subclasses declare the particle arrays they read/write and the shared + per-pair quantities they need, then contribute ``initialize`` / ``loop`` / + ``post_loop`` source snippets. Snippets are plain Warp-Python source lines + (already indented relative to the generated body) that may reference: + + - ``i`` (destination particle index) and ``j`` (neighbor index); + - any shared quantity listed in :attr:`requires` (``dx``, ``rij``, ``hij``, + ``grad``, ``wij``, ``vijx`` ...); + - source arrays as ``s_`` and destination arrays as ``d_``; + - shared output accumulators as ``_acc_`` for each name in + :attr:`out_arrays` (initialized to zero by the generator, written back + after the loop); + - declared scalars by name (e.g. ``alpha``); + - the ``TYPE`` token, replaced by ``wp.float32`` / ``wp.float64``. + + Block-local temporaries should use a trailing underscore (e.g. ``tmpi_``) + to avoid colliding with generator-owned names. + """ + + #: extra source (neighbor ``j``) arrays read, beyond geometry/velocity + src_arrays = () + #: extra destination (particle ``i``) arrays read, beyond geometry/velocity + dst_arrays = () + #: destination arrays written (accumulated via ``_acc_``) + out_arrays = () + #: scalar kernel parameters consumed by the snippets, in order + scalars = () + #: subset of :data:`SHARED_QUANTITIES` this block needs in the loop + requires = () + + def initialize(self): + """Per-destination-particle setup emitted before the neighbor loop.""" + return "" + + def loop(self): + """Per-pair body emitted inside the neighbor loop.""" + return "" + + def post_loop(self): + """Per-destination-particle code emitted after the neighbor loop.""" + return "" + + def signature(self): + """Structural cache key fragment. Override if behavior depends on flags + that change the generated source (not on runtime scalar *values*).""" + return (type(self).__name__,) + + +class GroupKernel(object): + """A compiled fused kernel plus the metadata needed to launch it. + + The ordered name lists fully determine both the generated signature and the + launch-input order, so a caller binds device arrays/scalars by walking these + lists in order. + """ + + def __init__(self, kernel, src_names, dst_names, scalar_names, out_names, + dtype, source, neighbor_mode='flat'): + self.kernel = kernel + self.src_names = src_names # s_ arrays, in signature order + self.dst_names = dst_names # d_ arrays read, in order + self.scalar_names = scalar_names + self.out_names = out_names # d_ arrays written, in order + self.dtype = dtype + self.source = source + self.neighbor_mode = neighbor_mode + + +def _dtype_tokens(dtype): + """Return ``(type_token, func_suffix, np_dtype)`` for a requested dtype.""" + if dtype in (np.float32, 'float32', 'f32'): + return 'wp.float32', 'f32', np.float32 + if dtype in (np.float64, 'float64', 'f64'): + return 'wp.float64', 'f64', np.float64 + raise ValueError("dtype must be float32 or float64, got %r" % (dtype,)) + + +def _collect(equations, neighbor_mode='flat'): + """Union the array/scalar signature across a group, preserving order and + auto-adding the arrays implied by the requested shared quantities. + + In ``grid`` mode the destination/source position and smoothing arrays are + forced into the signature even if no equation requested them: the + grid-direct loop needs ``x,y,z`` to locate the destination cell and ``h`` + (with ``radius_scale``) to apply the support cutoff that reproduces the + flat neighbor list's membership. + """ + requires = set() + for eq in equations: + requires.update(eq.requires) + unknown = requires - set(SHARED_QUANTITIES) + if unknown: + raise ValueError("unknown shared quantities: %s" % sorted(unknown)) + if neighbor_mode in ('grid', 'multilevel'): + # The cell walk + support cutoff always needs positions and h. + requires |= {'dx', 'dy', 'dz', 'rij2'} + + src = [] + dst = [] + scalars = [] + out = [] + + def add(seq, name): + if name not in seq: + seq.append(name) + + # Geometry-implied arrays first so the signature is stable and readable. + needs_pos = requires & {'dx', 'dy', 'dz', 'rij2', 'rij', 'grad', 'wij'} + needs_h = requires & {'hij', 'grad', 'wij'} + needs_vel = requires & {'vijx', 'vijy', 'vijz'} + if neighbor_mode in ('grid', 'multilevel'): + # The support cutoff reads radius_scale * h on both i and j. + needs_h = needs_h | {'h'} + if needs_pos: + for n in _GEOM_POS: + add(src, n) + add(dst, n) + if needs_h: + for n in _GEOM_H: + add(src, n) + add(dst, n) + if needs_vel: + for n in _GEOM_VEL: + add(src, n) + add(dst, n) + + for eq in equations: + for n in eq.src_arrays: + add(src, n) + for n in eq.dst_arrays: + add(dst, n) + for n in eq.scalars: + add(scalars, n) + for n in eq.out_arrays: + add(out, n) + return src, dst, scalars, out, requires + + +def _reindent(text, extra): + """Prefix every non-blank line of ``text`` with ``extra`` spaces, keeping + the snippet's internal relative indentation intact.""" + if extra <= 0: + return text + pad = " " * extra + return "\n".join( + (pad + line) if line.strip() else line + for line in text.split("\n") + ) + + +def _emit_geometry(requires, type_token, func_suffix, phase='all', + periodic=False): + """Emit the shared per-pair geometry lines requested by the group. + + ``phase`` controls which lines are emitted, so the grid-direct loop can + compute ``dx,dy,dz,rij2`` (``pre``) before the support cutoff and the rest + (``post``) inside it. ``all`` emits the full sequence in the original order + (used by the flat path, byte-identical to before this split). All lines are + emitted at the flat 8-space loop indent; the grid path reindents them. + + When ``periodic`` is set (grid mode only) the per-pair separations are + minimum-image corrected per dimension, guarded by the runtime + ``periodic_{x,y,z}`` flags, so a wrapped neighbor's distance is the shortest + image (ADR-0004 periodic follow-up). + """ + emit_pre = phase in ('all', 'pre') + emit_post = phase in ('all', 'post') + lines = [] + L = lines.append + needs_pos = requires & {'dx', 'dy', 'dz', 'rij2', 'rij', 'grad', 'wij'} + if emit_pre and needs_pos: + L(" dx = d_x[i] - s_x[j]") + if periodic: + L(" if periodic_x == wp.int32(1):") + L(" dx = dx - box_lx * wp.round(dx / box_lx)") + L(" dy = %s(0.0)" % type_token) + L(" dz = %s(0.0)" % type_token) + L(" if dim > wp.int32(1):") + L(" dy = d_y[i] - s_y[j]") + if periodic: + L(" if periodic_y == wp.int32(1):") + L(" dy = dy - box_ly * wp.round(dy / box_ly)") + L(" if dim > wp.int32(2):") + L(" dz = d_z[i] - s_z[j]") + if periodic: + L(" if periodic_z == wp.int32(1):") + L(" dz = dz - box_lz * wp.round(dz / box_lz)") + if emit_pre and (requires & {'rij2', 'rij', 'grad', 'wij'}): + L(" rij2 = dx*dx + dy*dy + dz*dz") + if emit_post and (requires & {'rij', 'grad', 'wij'}): + L(" rij = wp.sqrt(rij2)") + if emit_post and (requires & {'hij', 'grad', 'wij'}): + L(" hij = %s(0.5) * (d_h[i] + s_h[j])" % type_token) + if emit_post and 'grad' in requires: + L(" grad = %s(0.0)" % type_token) + L(" if rij > %s(1.0e-12):" % type_token) + L(" grad = _kernel_dwdq_%s(rij, hij, dim, kernel_id)" + " / (hij * rij)" % func_suffix) + if emit_post and 'wij' in requires: + L(" wij = _kernel_value_%s(rij, hij, dim, kernel_id)" + % func_suffix) + if emit_post and (requires & {'vijx', 'vijy', 'vijz'}): + L(" vijx = d_u[i] - s_u[j]") + L(" vijy = %s(0.0)" % type_token) + L(" vijz = %s(0.0)" % type_token) + L(" if dim > wp.int32(1):") + L(" vijy = d_v[i] - s_v[j]") + L(" if dim > wp.int32(2):") + L(" vijz = d_w[i] - s_w[j]") + return lines + + +def generate_group_source(equations, dtype, func_name='_warp_group_kernel', + neighbor_mode='flat', accumulate_outputs=False, + periodic=False): + """Generate the Warp kernel source for a group of equations. + + ``neighbor_mode`` selects the neighbor source: ``flat`` reads a prebuilt CSR + list (``starts/lengths/neighbors``); ``grid`` walks a uniform-grid cell list + directly (``cell_starts/cell_counts/cell_particles`` + bounds), applying the + support cutoff inline so it visits exactly the flat list's neighbor set + (ADR-0004). The per-pair geometry and every equation snippet are identical + across modes -- only the loop that produces ``j`` differs. + + ``accumulate_outputs`` controls how the shared accumulators are seeded: when + ``False`` (default) each ``_acc_`` starts at zero and the destination + arrays are overwritten; when ``True`` each starts from the existing + ``d_[i]`` so the group adds to the destination arrays (read-modify-write, + e.g. a standalone artificial-viscosity term composed onto a prior + pressure-gradient acceleration). + + Returns ``(source, src_names, dst_names, scalar_names, out_names)``. + """ + if neighbor_mode not in ('flat', 'grid', 'multilevel'): + raise ValueError( + "neighbor_mode must be 'flat', 'grid' or 'multilevel'") + if periodic and neighbor_mode != 'grid': + raise ValueError("periodic minimum-image requires neighbor_mode='grid'") + type_token, func_suffix, _ = _dtype_tokens(dtype) + src_names, dst_names, scalar_names, out_names, requires = _collect( + equations, neighbor_mode=neighbor_mode + ) + + def subst(snippet): + return snippet.replace('TYPE', type_token) + + lines = [] + L = lines.append + + # --- signature --- + L("def %s(" % func_name) + for n in src_names: + L(" s_%s: wp.array(dtype=%s)," % (n, type_token)) + for n in dst_names: + L(" d_%s: wp.array(dtype=%s)," % (n, type_token)) + if neighbor_mode == 'flat': + L(" starts: wp.array(dtype=wp.int32),") + L(" lengths: wp.array(dtype=wp.int32),") + L(" neighbors: wp.array(dtype=wp.uint32),") + elif neighbor_mode == 'multilevel': + # One flattened global cell list across all levels, plus per-level + # (length nlevels) metadata arrays for the variable-stencil walk. + L(" cell_starts: wp.array(dtype=wp.int32),") + L(" cell_counts: wp.array(dtype=wp.int32),") + L(" cell_particles: wp.array(dtype=wp.uint32),") + L(" origin_x: wp.array(dtype=%s)," % type_token) + L(" origin_y: wp.array(dtype=%s)," % type_token) + L(" origin_z: wp.array(dtype=%s)," % type_token) + L(" cell_size: wp.array(dtype=%s)," % type_token) + L(" lnx: wp.array(dtype=wp.int32),") + L(" lny: wp.array(dtype=wp.int32),") + L(" lnz: wp.array(dtype=wp.int32),") + L(" cell_offset: wp.array(dtype=wp.int32),") + L(" support: wp.array(dtype=%s)," % type_token) + L(" nlevels: wp.int32,") + L(" radius_scale: %s," % type_token) + else: + L(" cell_starts: wp.array(dtype=wp.int32),") + L(" cell_counts: wp.array(dtype=wp.int32),") + L(" cell_particles: wp.array(dtype=wp.uint32),") + L(" xmin: %s," % type_token) + L(" ymin: %s," % type_token) + L(" zmin: %s," % type_token) + L(" cell_size: %s," % type_token) + L(" nx: wp.int32,") + L(" ny: wp.int32,") + L(" nz: wp.int32,") + L(" ncells: wp.int32,") + L(" radius_scale: %s," % type_token) + if periodic: + L(" box_lx: %s," % type_token) + L(" box_ly: %s," % type_token) + L(" box_lz: %s," % type_token) + L(" periodic_x: wp.int32,") + L(" periodic_y: wp.int32,") + L(" periodic_z: wp.int32,") + L(" dim: wp.int32,") + L(" kernel_id: wp.int32,") + for n in scalar_names: + L(" %s: %s," % (n, type_token)) + for n in out_names: + L(" d_%s: wp.array(dtype=%s)," % (n, type_token)) + L("):") + + # --- per-particle preamble --- + L(" i = wp.tid()") + for n in out_names: + if accumulate_outputs: + L(" _acc_%s = d_%s[i]" % (n, n)) + else: + L(" _acc_%s = %s(0.0)" % (n, type_token)) + for eq in equations: + snippet = eq.initialize() + if snippet: + L(subst(snippet)) + + if neighbor_mode == 'flat': + # --- single CSR neighbor loop --- + L(" start = starts[i]") + L(" stop = start + lengths[i]") + L(" for pos in range(start, stop):") + L(" j = wp.int32(neighbors[pos])") + for line in _emit_geometry(requires, type_token, func_suffix): + L(line) + for eq in equations: + snippet = eq.loop() + if snippet: + L(subst(snippet)) + elif neighbor_mode == 'grid': + # --- direct uniform-grid cell-list walk (ADR-0004) --- + # Geometry/snippet lines are authored at the flat 8-space loop indent; + # reindent them to sit inside the cell walk (pre-cutoff at +20 -> col + # 28, post-cutoff body at +24 -> col 32). + L(" ix0 = wp.int32(wp.floor((d_x[i] - xmin) / cell_size))") + L(" iy0 = wp.int32(0)") + L(" iz0 = wp.int32(0)") + L(" if dim > wp.int32(1):") + L(" iy0 = wp.int32(wp.floor((d_y[i] - ymin) / cell_size))") + L(" if dim > wp.int32(2):") + L(" iz0 = wp.int32(wp.floor((d_z[i] - zmin) / cell_size))") + L(" for dzc in range(-1, 2):") + L(" for dyc in range(-1, 2):") + L(" for dxc in range(-1, 2):") + L(" ix = ix0 + wp.int32(dxc)") + L(" iy = iy0 + wp.int32(dyc)") + L(" iz = iz0 + wp.int32(dzc)") + if periodic: + # Per-dim: wrap the cell index when that dim is periodic + # (always in-bounds), else keep the bounds check. ny/nz == 1 for + # lower dims makes the non-periodic check select only index 0. + L(" cell_ok = True") + L(" if periodic_x == wp.int32(1):") + L(" ix = ((ix % nx) + nx) % nx") + L(" else:") + L(" if ix < 0 or ix >= nx:") + L(" cell_ok = False") + L(" if periodic_y == wp.int32(1):") + L(" iy = ((iy % ny) + ny) % ny") + L(" else:") + L(" if iy < 0 or iy >= ny:") + L(" cell_ok = False") + L(" if periodic_z == wp.int32(1):") + L(" iz = ((iz % nz) + nz) % nz") + L(" else:") + L(" if iz < 0 or iz >= nz:") + L(" cell_ok = False") + L(" if cell_ok:") + else: + L(" if ix >= 0 and ix < nx and iy >= 0 and iy < ny" + " and iz >= 0 and iz < nz:") + L(" cid = ix + iy * nx + iz * nx * ny") + L(" if cid >= 0 and cid < ncells:") + L(" c_start_ = cell_starts[cid]") + L(" c_stop_ = c_start_ + cell_counts[cid]") + L(" for pos in range(c_start_, c_stop_):") + L(" j = wp.int32(cell_particles[pos])") + pre = _emit_geometry(requires, type_token, func_suffix, phase='pre', + periodic=periodic) + for line in pre: + L(_reindent(line, 20)) + L(" hi_ = radius_scale * d_h[i]") + L(" hj_ = radius_scale * s_h[j]") + L(" if rij2 < hi_*hi_ or rij2 < hj_*hj_:") + post = _emit_geometry(requires, type_token, func_suffix, phase='post') + for line in post: + L(_reindent(line, 24)) + for eq in equations: + snippet = eq.loop() + if snippet: + L(_reindent(subst(snippet), 24)) + else: + # --- direct multilevel cell-list walk (ADR-0007) --- + # Loop over levels; per level convert the query radius + # max(radius_scale*h_i, support[k]) into a variable cell-index range + # (not a fixed 3x3x3 stencil) with a +/-1 guard band, then apply the + # exact symmetric cutoff. Reindents match the grid walk: pre-cutoff at + # +20 (col 28), post-cutoff body at +24 (col 32). + L(" for lk in range(nlevels):") + L(" nxk = lnx[lk]") + L(" if nxk > wp.int32(0):") + L(" csk = cell_size[lk]") + L(" qr = radius_scale * d_h[i]") + L(" if support[lk] > qr:") + L(" qr = support[lk]") + L(" ixlo = wp.int32(wp.floor((d_x[i] - qr - origin_x[lk]) /" + " csk)) - wp.int32(1)") + L(" ixhi = wp.int32(wp.floor((d_x[i] + qr - origin_x[lk]) /" + " csk)) + wp.int32(1)") + L(" ixlo = wp.clamp(ixlo, wp.int32(0), nxk - wp.int32(1))") + L(" ixhi = wp.clamp(ixhi, wp.int32(0), nxk - wp.int32(1))") + L(" iylo = wp.int32(0)") + L(" iyhi = wp.int32(0)") + L(" nyk = lny[lk]") + L(" if dim > wp.int32(1):") + L(" iylo = wp.int32(wp.floor((d_y[i] - qr -" + " origin_y[lk]) / csk)) - wp.int32(1)") + L(" iyhi = wp.int32(wp.floor((d_y[i] + qr -" + " origin_y[lk]) / csk)) + wp.int32(1)") + L(" iylo = wp.clamp(iylo, wp.int32(0), nyk -" + " wp.int32(1))") + L(" iyhi = wp.clamp(iyhi, wp.int32(0), nyk -" + " wp.int32(1))") + L(" izlo = wp.int32(0)") + L(" izhi = wp.int32(0)") + L(" nzk = lnz[lk]") + L(" if dim > wp.int32(2):") + L(" izlo = wp.int32(wp.floor((d_z[i] - qr -" + " origin_z[lk]) / csk)) - wp.int32(1)") + L(" izhi = wp.int32(wp.floor((d_z[i] + qr -" + " origin_z[lk]) / csk)) + wp.int32(1)") + L(" izlo = wp.clamp(izlo, wp.int32(0), nzk -" + " wp.int32(1))") + L(" izhi = wp.clamp(izhi, wp.int32(0), nzk -" + " wp.int32(1))") + L(" off = cell_offset[lk]") + L(" for iz in range(izlo, izhi + 1):") + L(" for iy in range(iylo, iyhi + 1):") + L(" for ix in range(ixlo, ixhi + 1):") + L(" cid = off + ix + iy * nxk + iz * nxk * nyk") + L(" c_start_ = cell_starts[cid]") + L(" c_stop_ = c_start_ + cell_counts[cid]") + L(" for pos in range(c_start_, c_stop_):") + L(" j = wp.int32(cell_particles[pos])") + pre = _emit_geometry(requires, type_token, func_suffix, phase='pre') + for line in pre: + L(_reindent(line, 20)) + L(" hi_ = radius_scale * d_h[i]") + L(" hj_ = radius_scale * s_h[j]") + L(" if rij2 < hi_*hi_ or rij2 < hj_*hj_:") + post = _emit_geometry(requires, type_token, func_suffix, phase='post') + for line in post: + L(_reindent(line, 24)) + for eq in equations: + snippet = eq.loop() + if snippet: + L(_reindent(subst(snippet), 24)) + + # --- write back / post-loop --- + for n in out_names: + L(" d_%s[i] = _acc_%s" % (n, n)) + for eq in equations: + snippet = eq.post_loop() + if snippet: + L(subst(snippet)) + + source = "\n".join(lines) + "\n" + return source, src_names, dst_names, scalar_names, out_names + + +# Cache of compiled group kernels, keyed by structural signature. +_KERNEL_CACHE = {} + + +def _cache_key(equations, dtype, neighbor_mode='flat', + accumulate_outputs=False, periodic=False): + type_token, _, _ = _dtype_tokens(dtype) + key = ( + tuple(eq.signature() for eq in equations), type_token, neighbor_mode, + bool(accumulate_outputs), + ) + # Append the periodic marker only when set, so non-periodic kernels keep + # their exact prior structural key (and md5-derived name / Warp disk-cache + # hash) -- they neither change nor recompile when periodic support is added. + if periodic: + key = key + ('periodic',) + return key + + +def build_group_kernel(equations, dtype, device_funcs, key=None, + neighbor_mode='flat', accumulate_outputs=False, + periodic=False): + """Generate (or fetch from cache) the fused kernel for ``equations``. + + ``device_funcs`` maps the device ``wp.func`` names referenced by the + generated geometry (``_kernel_dwdq_f32`` etc.) to their ``wp.Function`` + objects; these seed the namespace the generated function executes in so + Warp can resolve them. ``neighbor_mode`` (``flat``/``grid``) selects the + neighbor source and is part of the structural cache key, so the two + variants of a group compile independently (ADR-0004). ``periodic`` (grid + only) compiles a minimum-image / wrapped-cell-walk variant, also part of the + cache key, so the non-periodic kernels are byte-identical and untouched. + + Returns a :class:`GroupKernel`. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for build_group_kernel") + + cache_key = _cache_key( + equations, dtype, neighbor_mode, accumulate_outputs, periodic + ) + cached = _KERNEL_CACHE.get(cache_key) + if cached is not None: + return cached + + if key is None: + type_token, func_suffix, _ = _dtype_tokens(dtype) + # Deterministic, call-order-independent name derived from the structural + # cache key, so the generated source -- and therefore Warp's on-disk + # kernel cache hash -- is stable across runs/sessions. A name that + # depends on build order (e.g. len(_KERNEL_CACHE)) changes the source + # whenever the order shifts and forces a cold recompile every session. + digest = hashlib.md5(repr(cache_key).encode('utf-8')).hexdigest()[:12] + key = "warp_group_%s_%s%s%s_%s" % ( + func_suffix, neighbor_mode, + "_acc" if accumulate_outputs else "", + "_per" if periodic else "", digest + ) + func_name = key + + source, src_names, dst_names, scalar_names, out_names = ( + generate_group_source( + equations, dtype, func_name=func_name, neighbor_mode=neighbor_mode, + accumulate_outputs=accumulate_outputs, periodic=periodic, + ) + ) + + # Register the source with linecache so Warp can introspect the exec'd + # function, then compile it into a namespace seeded with wp + device funcs. + fname = "" % key + linecache.cache[fname] = ( + len(source), None, source.splitlines(True), fname + ) + namespace = {'wp': wp} + namespace.update(device_funcs) + exec(compile(source, fname, 'exec'), namespace) + fn = namespace[func_name] + kernel = wp.Kernel(func=fn, key=key, source=source) + + group_kernel = GroupKernel( + kernel=kernel, src_names=src_names, dst_names=dst_names, + scalar_names=scalar_names, out_names=out_names, dtype=dtype, + source=source, neighbor_mode=neighbor_mode, + ) + _KERNEL_CACHE[cache_key] = group_kernel + return group_kernel + + +def clear_kernel_cache(): + """Drop the compiled-group cache (used by tests).""" + _KERNEL_CACHE.clear() diff --git a/pysph/base/warp_device_helper.py b/pysph/base/warp_device_helper.py new file mode 100644 index 000000000..57f54f061 --- /dev/null +++ b/pysph/base/warp_device_helper.py @@ -0,0 +1,504 @@ +from __future__ import print_function + +import numpy as np +import warp as wp + +try: + from compyle.config import get_config +except ImportError: # pragma: no cover - used only for isolated helper probes. + get_config = None + + +_NUMPY_TO_WARP = { + np.dtype(np.float64): wp.float64, + np.dtype(np.float32): wp.float32, + np.dtype(np.int64): wp.int64, + np.dtype(np.int32): wp.int32, + np.dtype(np.uint32): wp.uint32, +} + + +@wp.kernel +def _gather_f64(src: wp.array(dtype=wp.float64), indices: wp.array(dtype=wp.int32), + stride: int, dst: wp.array(dtype=wp.float64)): + i = wp.tid() + p = i // stride + c = i - p * stride + dst[i] = src[indices[p] * stride + c] + + +@wp.kernel +def _gather_f32(src: wp.array(dtype=wp.float32), indices: wp.array(dtype=wp.int32), + stride: int, dst: wp.array(dtype=wp.float32)): + i = wp.tid() + p = i // stride + c = i - p * stride + dst[i] = src[indices[p] * stride + c] + + +@wp.kernel +def _gather_i64(src: wp.array(dtype=wp.int64), indices: wp.array(dtype=wp.int32), + stride: int, dst: wp.array(dtype=wp.int64)): + i = wp.tid() + p = i // stride + c = i - p * stride + dst[i] = src[indices[p] * stride + c] + + +@wp.kernel +def _gather_i32(src: wp.array(dtype=wp.int32), indices: wp.array(dtype=wp.int32), + stride: int, dst: wp.array(dtype=wp.int32)): + i = wp.tid() + p = i // stride + c = i - p * stride + dst[i] = src[indices[p] * stride + c] + + +@wp.kernel +def _gather_u32(src: wp.array(dtype=wp.uint32), indices: wp.array(dtype=wp.int32), + stride: int, dst: wp.array(dtype=wp.uint32)): + i = wp.tid() + p = i // stride + c = i - p * stride + dst[i] = src[indices[p] * stride + c] + + +_GATHER_KERNELS = { + np.dtype(np.float64): _gather_f64, + np.dtype(np.float32): _gather_f32, + np.dtype(np.int64): _gather_i64, + np.dtype(np.int32): _gather_i32, + np.dtype(np.uint32): _gather_u32, +} + + +_DTYPE_TO_CTYPE = { + np.dtype(np.float64): 'double', + np.dtype(np.float32): 'float', + np.dtype(np.int64): 'long', + np.dtype(np.int32): 'int', + np.dtype(np.uint32): 'unsigned int', +} + + +def _warp_dtype(dtype): + dtype = np.dtype(dtype) + try: + return _NUMPY_TO_WARP[dtype] + except KeyError: + raise TypeError('Unsupported Warp array dtype: %s' % dtype) + + +def _as_numpy_array(value, dtype=None): + if isinstance(value, WarpArray): + value = value.get() + elif hasattr(value, 'get'): + value = value.get() + elif hasattr(value, 'get_npy_array'): + value = value.get_npy_array() + return np.asarray(value, dtype=dtype) + + +def _as_indices(indices): + return _as_numpy_array(indices, dtype=np.int64).ravel() + + +def _dtype_to_ctype(dtype): + dtype = np.dtype(dtype) + try: + return _DTYPE_TO_CTYPE[dtype] + except KeyError: + raise TypeError('Unsupported ParticleArray dtype: %s' % dtype) + + +class WarpArray(object): + """Small compatibility wrapper around a one-dimensional Warp array.""" + + def __init__(self, data=None, dtype=None, n=None, device=None): + self.device = device + if data is None: + dtype = np.dtype(np.float64 if dtype is None else dtype) + self._array = wp.zeros(n or 0, dtype=_warp_dtype(dtype), + device=device) + self.dtype = dtype + else: + arr = np.asarray(data, dtype=dtype) + self.dtype = arr.dtype + self._array = wp.from_numpy(arr, dtype=_warp_dtype(self.dtype), + device=device) + + @property + def data(self): + return self._array + + @property + def dev(self): + return self._array + + def __len__(self): + return self._array.shape[0] + + def __getitem__(self, index): + return self.get()[index] + + def __setitem__(self, index, value): + arr = self.get() + arr[index] = value + self.set(arr) + + def get(self): + return self._array.numpy() + + def set(self, data): + arr = np.asarray(data, dtype=self.dtype) + self._array = wp.from_numpy(arr, dtype=_warp_dtype(self.dtype), + device=self.device) + + def fill(self, value): + self.set(np.full(len(self), value, dtype=self.dtype)) + + def extend(self, data): + data = _as_numpy_array(data, dtype=self.dtype) + self.set(np.concatenate([self.get(), data])) + + def resize(self, size): + old = self.get() + new = np.zeros(size, dtype=self.dtype) + new[:min(size, old.size)] = old[:min(size, old.size)] + self.set(new) + + def copy(self): + return WarpArray(self.get().copy(), device=self.device) + + def aligned(self, indices, stride=1): + indices = np.asarray(indices, dtype=np.int32) + if len(indices) == 0: + return WarpArray(np.array([], dtype=self.dtype), + device=self.device) + dst = wp.empty(len(indices) * stride, dtype=_warp_dtype(self.dtype), + device=self.device) + wp_indices = wp.from_numpy(indices, dtype=wp.int32, + device=self.device) + kernel = _GATHER_KERNELS[np.dtype(self.dtype)] + wp.launch(kernel, dim=dst.shape[0], + inputs=[self._array, wp_indices, stride, dst], + device=self.device) + wp.synchronize_device(self.device) + return WarpArray(dst.numpy(), dtype=self.dtype, device=self.device) + + +class WarpDeviceHelper(object): + """Manage ParticleArray properties/constants with NVIDIA Warp arrays.""" + + def __init__(self, particle_array, backend='warp', device=None): + self.backend = backend + self.device = wp.get_device(device) + self._particle_array = pa = particle_array + self.use_double = True if get_config is None else get_config().use_double + self._dtype = np.float64 if self.use_double else np.float32 + self.num_real_particles = pa.num_real_particles + self._data = {} + self.properties = [] + self.constants = [] + + for prop, ary in pa.properties.items(): + self.add_prop(prop, ary) + for prop, ary in pa.constants.items(): + self.add_const(prop, ary) + + def _get_array(self, ary): + ctype = ary.get_c_type() + if ctype in ['float', 'double']: + return ary.get_npy_array().astype(self._dtype) + else: + return ary.get_npy_array() + + def _get_prop_or_const(self, prop): + pa = self._particle_array + return pa.properties.get(prop, pa.constants.get(prop)) + + def _add_prop_or_const(self, name, carray): + arr = WarpArray(self._get_array(carray), device=self.device) + self._data[name] = arr + setattr(self, name, arr) + + def get_number_of_particles(self, real=False): + if real: + return self.num_real_particles + elif len(self.properties) > 0: + pname = self.properties[0] + stride = self._particle_array.stride.get(pname, 1) + return len(self._data[pname]) // stride + else: + return 0 + + def get_device_array(self, name): + return self._data[name] + + def add_prop(self, prop, carray): + if prop not in self.properties: + self.properties.append(prop) + self._add_prop_or_const(prop, carray) + + def add_const(self, prop, carray): + if prop not in self.constants: + self.constants.append(prop) + self._add_prop_or_const(prop, carray) + + def update_prop(self, prop, array): + if not isinstance(array, WarpArray): + array = WarpArray(array, device=self.device) + if prop not in self.properties: + self.properties.append(prop) + self._data[prop] = array + setattr(self, prop, array) + + def update_const(self, prop, array): + if not isinstance(array, WarpArray): + array = WarpArray(array, device=self.device) + if prop not in self.constants: + self.constants.append(prop) + self._data[prop] = array + setattr(self, prop, array) + + def remove_prop(self, prop): + if prop in self.properties: + self.properties.remove(prop) + self._data.pop(prop, None) + if hasattr(self, prop): + delattr(self, prop) + + def push(self, *props): + if len(props) == 0: + props = list(self.properties) + list(self.constants) + for prop in props: + ary = self._get_prop_or_const(prop) + self._data[prop].set(self._get_array(ary)) + + def pull(self, *props): + pa = self._particle_array + if len(props) == 0: + props = list(self.properties) + list(self.constants) + for prop in props: + data = self._data[prop].get() + if prop in pa.properties: + ary = pa.properties[prop] + if ary.length != data.size: + ary.resize(data.size) + ary.set_data(data) + elif prop in pa.constants: + ary = pa.constants[prop] + if ary.length != data.size: + ary.resize(data.size) + ary.set_data(data) + pa.set_num_real_particles(self.num_real_particles) + + def max(self, prop): + return self._data[prop].get().max() + + def resize(self, size): + for prop in self.properties: + stride = self._particle_array.stride.get(prop, 1) + self._data[prop].resize(size * stride) + + def extend(self, num_particles): + if num_particles <= 0: + return + + old_size = self.get_number_of_particles() + new_size = old_size + num_particles + + for prop in self.properties: + arr = self._data[prop] + stride = self._particle_array.stride.get(prop, 1) + data = arr.get() + new_data = np.empty(new_size * stride, dtype=arr.dtype) + new_data[:old_size * stride] = data + new_data[old_size * stride:] = \ + self._particle_array.default_values[prop] + arr.set(new_data) + self.update_prop(prop, arr) + + def align(self, indices): + for prop in self.properties: + stride = self._particle_array.stride.get(prop, 1) + self._data[prop] = self._data[prop].aligned(indices, stride) + setattr(self, prop, self._data[prop]) + + def align_particles(self): + tags = self._data['tag'].get() + local = np.flatnonzero(tags == 0).astype(np.int32) + other = np.flatnonzero(tags != 0).astype(np.int32) + indices = np.concatenate([local, other]) + self.num_real_particles = local.size + if indices.size > 0: + self.align(indices) + + def remove_particles(self, indices, align=True): + indices = _as_indices(indices) + num_particles = self.get_number_of_particles() + if len(indices) > num_particles: + msg = 'Number of particles to be removed is greater than' + msg += 'number of particles in array' + raise ValueError(msg) + + indices = np.unique(indices[(indices >= 0) & (indices < num_particles)]) + if indices.size == 0: + return + + keep = np.ones(num_particles, dtype=bool) + keep[indices] = False + self.align(np.flatnonzero(keep).astype(np.int32)) + + if align: + self.align_particles() + + def remove_tagged_particles(self, tag, align=True): + indices = np.flatnonzero(self._data['tag'].get() == tag) + self.remove_particles(indices, align=align) + + def add_particles(self, align=True, **particle_props): + if len(particle_props) == 0: + return 0 + + for prop in particle_props: + if prop not in self._particle_array.properties and \ + prop not in self._particle_array.constants: + raise AttributeError('property %s not present' % prop) + + first_prop = next(iter(particle_props)) + stride = self._particle_array.stride.get(first_prop, 1) + num_extra_particles = len(_as_numpy_array(particle_props[first_prop])) // stride + old_num_particles = self.get_number_of_particles() + new_num_particles = old_num_particles + num_extra_particles + + for prop in self.properties: + arr = self._data[prop] + stride = self._particle_array.stride.get(prop, 1) + if prop in particle_props: + extra = _as_numpy_array(particle_props[prop], dtype=arr.dtype) + arr.set(np.concatenate([arr.get(), extra])) + else: + data = np.empty(new_num_particles * stride, dtype=arr.dtype) + data[:old_num_particles * stride] = arr.get() + data[old_num_particles * stride:] = \ + self._particle_array.default_values[prop] + arr.set(data) + self.update_prop(prop, arr) + + if num_extra_particles > 0 and align: + self.align_particles() + + return 0 + + def empty_clone(self, props=None): + import pysph.base.particle_array + + prop_names = self.properties if props is None else props + result_array = pysph.base.particle_array.ParticleArray( + backend=self._particle_array.backend + ) + result_array.set_name(self._particle_array.name) + + for prop_name in prop_names: + src_arr = self._data[prop_name] + stride = self._particle_array.stride.get(prop_name, 1) + prop_type = _dtype_to_ctype(src_arr.dtype) + prop_default = self._particle_array.default_values[prop_name] + result_array.add_property( + name=prop_name, type=prop_type, + default=prop_default, stride=stride + ) + + for const in self.constants: + result_array.gpu.update_const(const, self._data[const].copy()) + + if props is None: + output_arrays = list(self._particle_array.output_property_arrays) + else: + output_arrays = list( + set(props).intersection( + self._particle_array.output_property_arrays + ) + ) + result_array.set_output_arrays(output_arrays) + return result_array + + def append_parray(self, parray, align=True, update_constants=False): + if parray.get_number_of_particles() == 0: + return + + if parray.gpu is not None and parray.backend == 'warp': + source_props = { + prop: parray.gpu.get_device_array(prop).get() + for prop in parray.gpu.properties + } + else: + source_props = parray.get_property_arrays(all=True, + only_real=False) + + old_num_particles = self.get_number_of_particles() + num_extra_particles = parray.get_number_of_particles() + new_num_particles = old_num_particles + num_extra_particles + pa = self._particle_array + + for prop_name in parray.properties: + stride = parray.stride.get(prop_name, 1) + if prop_name not in pa.properties: + pa.add_property( + name=prop_name, + type=parray.properties[prop_name].get_c_type(), + default=parray.default_values[prop_name], + stride=stride + ) + + arr = self._data[prop_name] + current = arr.get() + data = np.empty(new_num_particles * stride, dtype=arr.dtype) + data[:old_num_particles * stride] = \ + current[:old_num_particles * stride] + data[old_num_particles * stride:] = \ + _as_numpy_array(source_props[prop_name], dtype=arr.dtype) + arr.set(data) + self.update_prop(prop_name, arr) + + if update_constants: + for const in parray.constants: + if const not in pa.constants: + pa.add_constant( + const, parray.constants[const].get_npy_array() + ) + + if num_extra_particles > 0 and align: + self.align_particles() + + def extract_particles(self, indices, dest_array=None, align=True, + props=None): + if not dest_array: + dest_array = self.empty_clone(props=props) + + indices = _as_indices(indices).astype(np.int32) + if props is None: + prop_names = list(self.properties) + else: + prop_names = props + + if len(indices) == 0: + return dest_array + + start_idx = dest_array.gpu.get_number_of_particles() + dest_array.gpu.extend(len(indices)) + + for prop in prop_names: + stride = self._particle_array.stride.get(prop, 1) + extracted = self._data[prop].aligned(indices, stride).get() + dest = dest_array.gpu.get_device_array(prop) + data = dest.get() + data[start_idx * stride:(start_idx + len(indices)) * stride] = extracted + dest.set(data) + dest_array.gpu.update_prop(prop, dest) + + if align: + dest_array.gpu.align_particles() + + return dest_array diff --git a/pysph/base/warp_multilevel_nnps.py b/pysph/base/warp_multilevel_nnps.py new file mode 100644 index 000000000..769d9632f --- /dev/null +++ b/pysph/base/warp_multilevel_nnps.py @@ -0,0 +1,859 @@ +"""Multilevel (adaptive-resolution) GPU NNPS for the Warp backend. + +Kept in a SEPARATE module from ``warp_nnps`` on purpose: Warp compiles and +loads an entire Python module's kernels together on the first launch of any of +them. If these multilevel kernels lived in ``warp_nnps`` they would be JIT-ed +onto the device by every consumer of ``UniformGridWarpNNPS`` (e.g. the WCSPH +suite), inflating the process's device module footprint. On WSL2 that extra PTX +tips the in-process PTX-JIT compiler into a hang while loading a later large +generated kernel. Housing them here means they load lazily only when +``MultilevelGridWarpNNPS`` is actually used. +""" + +import numpy as np + +try: + import warp as wp +except ImportError: # pragma: no cover + wp = None + +from pysph.base.warp_nnps import ( + UniformGridWarpNNPS, _copy_i32, _scatter_cell_particles, +) + + +if wp is not None: + # --- Multilevel (adaptive-resolution) cell-list kernels --------------- + # + # Each source particle lives in exactly one level; per-level grids are + # flattened into one global cell space via cell_offset[k]. Traversal loops + # over levels and, per level, converts the query radius + # max(radius_scale*h_i, support[k]) into a variable cell-index range (not a + # fixed +/-1 stencil) with a +/-1 guard band, then applies the exact + # symmetric cutoff. The lengths and fill kernels are structurally identical + # so their counts can never diverge (unlike the separate brute/grid passes). + + @wp.kernel + def _ml_assign_reduce_f64( + h: wp.array(dtype=wp.float64), + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + edges: wp.array(dtype=wp.float64), + nlevels: wp.int32, + dim: wp.int32, + level_of: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + hmax: wp.array(dtype=wp.float64), + xmin: wp.array(dtype=wp.float64), + xmax: wp.array(dtype=wp.float64), + ymin: wp.array(dtype=wp.float64), + ymax: wp.array(dtype=wp.float64), + zmin: wp.array(dtype=wp.float64), + zmax: wp.array(dtype=wp.float64), + oob: wp.array(dtype=wp.int32), + ): + # Assign each particle to its half-open level and reduce per-level + # count, max-h and AABB on the device, so x/y/z/h never leave the GPU. + i = wp.tid() + hi = h[i] + k = wp.int32(-1) + for m in range(nlevels): + if hi >= edges[m] and hi < edges[m + 1]: + k = m + if hi == edges[nlevels]: # inclusive top edge -> top level + k = nlevels - wp.int32(1) + if k < wp.int32(0): # below finest or above top -> loud + wp.atomic_add(oob, 0, wp.int32(1)) + level_of[i] = wp.int32(0) + return + level_of[i] = k + wp.atomic_add(counts, k, wp.int32(1)) + wp.atomic_max(hmax, k, hi) + wp.atomic_min(xmin, k, x[i]) + wp.atomic_max(xmax, k, x[i]) + if dim > 1: + wp.atomic_min(ymin, k, y[i]) + wp.atomic_max(ymax, k, y[i]) + if dim > 2: + wp.atomic_min(zmin, k, z[i]) + wp.atomic_max(zmax, k, z[i]) + + + @wp.kernel + def _ml_assign_reduce_f32( + h: wp.array(dtype=wp.float32), + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + edges: wp.array(dtype=wp.float32), + nlevels: wp.int32, + dim: wp.int32, + level_of: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + hmax: wp.array(dtype=wp.float32), + xmin: wp.array(dtype=wp.float32), + xmax: wp.array(dtype=wp.float32), + ymin: wp.array(dtype=wp.float32), + ymax: wp.array(dtype=wp.float32), + zmin: wp.array(dtype=wp.float32), + zmax: wp.array(dtype=wp.float32), + oob: wp.array(dtype=wp.int32), + ): + i = wp.tid() + hi = h[i] + k = wp.int32(-1) + for m in range(nlevels): + if hi >= edges[m] and hi < edges[m + 1]: + k = m + if hi == edges[nlevels]: + k = nlevels - wp.int32(1) + if k < wp.int32(0): + wp.atomic_add(oob, 0, wp.int32(1)) + level_of[i] = wp.int32(0) + return + level_of[i] = k + wp.atomic_add(counts, k, wp.int32(1)) + wp.atomic_max(hmax, k, hi) + wp.atomic_min(xmin, k, x[i]) + wp.atomic_max(xmax, k, x[i]) + if dim > 1: + wp.atomic_min(ymin, k, y[i]) + wp.atomic_max(ymax, k, y[i]) + if dim > 2: + wp.atomic_min(zmin, k, z[i]) + wp.atomic_max(zmax, k, z[i]) + + + @wp.kernel + def _multilevel_cell_ids_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + level_of: wp.array(dtype=wp.int32), + origin_x: wp.array(dtype=wp.float64), + origin_y: wp.array(dtype=wp.float64), + origin_z: wp.array(dtype=wp.float64), + cell_size: wp.array(dtype=wp.float64), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + dim: wp.int32, + cell_ids: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + ): + i = wp.tid() + k = level_of[i] + cs = cell_size[k] + ix = wp.int32(wp.floor((x[i] - origin_x[k]) / cs)) + iy = wp.int32(0) + iz = wp.int32(0) + if dim > 1: + iy = wp.int32(wp.floor((y[i] - origin_y[k]) / cs)) + if dim > 2: + iz = wp.int32(wp.floor((z[i] - origin_z[k]) / cs)) + ix = wp.clamp(ix, wp.int32(0), nx[k] - wp.int32(1)) + iy = wp.clamp(iy, wp.int32(0), ny[k] - wp.int32(1)) + iz = wp.clamp(iz, wp.int32(0), nz[k] - wp.int32(1)) + cid = cell_offset[k] + ix + iy * nx[k] + iz * nx[k] * ny[k] + cell_ids[i] = cid + wp.atomic_add(counts, cid, wp.int32(1)) + + + @wp.kernel + def _multilevel_cell_ids_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + level_of: wp.array(dtype=wp.int32), + origin_x: wp.array(dtype=wp.float32), + origin_y: wp.array(dtype=wp.float32), + origin_z: wp.array(dtype=wp.float32), + cell_size: wp.array(dtype=wp.float32), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + dim: wp.int32, + cell_ids: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + ): + i = wp.tid() + k = level_of[i] + cs = cell_size[k] + ix = wp.int32(wp.floor((x[i] - origin_x[k]) / cs)) + iy = wp.int32(0) + iz = wp.int32(0) + if dim > 1: + iy = wp.int32(wp.floor((y[i] - origin_y[k]) / cs)) + if dim > 2: + iz = wp.int32(wp.floor((z[i] - origin_z[k]) / cs)) + ix = wp.clamp(ix, wp.int32(0), nx[k] - wp.int32(1)) + iy = wp.clamp(iy, wp.int32(0), ny[k] - wp.int32(1)) + iz = wp.clamp(iz, wp.int32(0), nz[k] - wp.int32(1)) + cid = cell_offset[k] + ix + iy * nx[k] + iz * nx[k] * ny[k] + cell_ids[i] = cid + wp.atomic_add(counts, cid, wp.int32(1)) + + + @wp.func + def _ml_cell_range( + dq: wp.float64, qr: wp.float64, origin: wp.float64, + cs: wp.float64, n: wp.int32): + lo = wp.int32(wp.floor((dq - qr - origin) / cs)) - wp.int32(1) + hi = wp.int32(wp.floor((dq + qr - origin) / cs)) + wp.int32(1) + lo = wp.clamp(lo, wp.int32(0), n - wp.int32(1)) + hi = wp.clamp(hi, wp.int32(0), n - wp.int32(1)) + return wp.vec2i(lo, hi) + + + @wp.func + def _ml_cell_range_f32( + dq: wp.float32, qr: wp.float32, origin: wp.float32, + cs: wp.float32, n: wp.int32): + lo = wp.int32(wp.floor((dq - qr - origin) / cs)) - wp.int32(1) + hi = wp.int32(wp.floor((dq + qr - origin) / cs)) + wp.int32(1) + lo = wp.clamp(lo, wp.int32(0), n - wp.int32(1)) + hi = wp.clamp(hi, wp.int32(0), n - wp.int32(1)) + return wp.vec2i(lo, hi) + + + @wp.kernel + def _multilevel_neighbor_lengths_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + origin_x: wp.array(dtype=wp.float64), + origin_y: wp.array(dtype=wp.float64), + origin_z: wp.array(dtype=wp.float64), + cell_size: wp.array(dtype=wp.float64), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + support: wp.array(dtype=wp.float64), + nlevels: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + count = wp.int32(0) + for k in range(nlevels): + nxk = nx[k] + if nxk > 0: + cs = cell_size[k] + qr = radius_scale * d_h[i] + if support[k] > qr: + qr = support[k] + rx = _ml_cell_range(d_x[i], qr, origin_x[k], cs, nxk) + iylo = wp.int32(0) + iyhi = wp.int32(0) + nyk = ny[k] + if dim > 1: + ry = _ml_cell_range( + d_y[i], qr, origin_y[k], cs, nyk) + iylo = ry[0] + iyhi = ry[1] + izlo = wp.int32(0) + izhi = wp.int32(0) + nzk = nz[k] + if dim > 2: + rz = _ml_cell_range( + d_z[i], qr, origin_z[k], cs, nzk) + izlo = rz[0] + izhi = rz[1] + off = cell_offset[k] + for iz in range(izlo, izhi + 1): + for iy in range(iylo, iyhi + 1): + for ix in range(rx[0], rx[1] + 1): + cid = off + ix + iy * nxk + iz * nxk * nyk + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = wp.int32(cell_particles[pos]) + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx * dx + dy * dy + dz * dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi * hi or dist2 < hj * hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _multilevel_neighbor_fill_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + out_starts: wp.array(dtype=wp.int32), + origin_x: wp.array(dtype=wp.float64), + origin_y: wp.array(dtype=wp.float64), + origin_z: wp.array(dtype=wp.float64), + cell_size: wp.array(dtype=wp.float64), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + support: wp.array(dtype=wp.float64), + nlevels: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + out = out_starts[i] + count = wp.int32(0) + for k in range(nlevels): + nxk = nx[k] + if nxk > 0: + cs = cell_size[k] + qr = radius_scale * d_h[i] + if support[k] > qr: + qr = support[k] + rx = _ml_cell_range(d_x[i], qr, origin_x[k], cs, nxk) + iylo = wp.int32(0) + iyhi = wp.int32(0) + nyk = ny[k] + if dim > 1: + ry = _ml_cell_range( + d_y[i], qr, origin_y[k], cs, nyk) + iylo = ry[0] + iyhi = ry[1] + izlo = wp.int32(0) + izhi = wp.int32(0) + nzk = nz[k] + if dim > 2: + rz = _ml_cell_range( + d_z[i], qr, origin_z[k], cs, nzk) + izlo = rz[0] + izhi = rz[1] + off = cell_offset[k] + for iz in range(izlo, izhi + 1): + for iy in range(iylo, iyhi + 1): + for ix in range(rx[0], rx[1] + 1): + cid = off + ix + iy * nxk + iz * nxk * nyk + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = wp.int32(cell_particles[pos]) + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx * dx + dy * dy + dz * dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi * hi or dist2 < hj * hj: + neighbors[out + count] = wp.uint32(j) + count += wp.int32(1) + + + @wp.kernel + def _multilevel_neighbor_lengths_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + origin_x: wp.array(dtype=wp.float32), + origin_y: wp.array(dtype=wp.float32), + origin_z: wp.array(dtype=wp.float32), + cell_size: wp.array(dtype=wp.float32), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + support: wp.array(dtype=wp.float32), + nlevels: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + count = wp.int32(0) + for k in range(nlevels): + nxk = nx[k] + if nxk > 0: + cs = cell_size[k] + qr = radius_scale * d_h[i] + if support[k] > qr: + qr = support[k] + rx = _ml_cell_range_f32( + d_x[i], qr, origin_x[k], cs, nxk) + iylo = wp.int32(0) + iyhi = wp.int32(0) + nyk = ny[k] + if dim > 1: + ry = _ml_cell_range_f32( + d_y[i], qr, origin_y[k], cs, nyk) + iylo = ry[0] + iyhi = ry[1] + izlo = wp.int32(0) + izhi = wp.int32(0) + nzk = nz[k] + if dim > 2: + rz = _ml_cell_range_f32( + d_z[i], qr, origin_z[k], cs, nzk) + izlo = rz[0] + izhi = rz[1] + off = cell_offset[k] + for iz in range(izlo, izhi + 1): + for iy in range(iylo, iyhi + 1): + for ix in range(rx[0], rx[1] + 1): + cid = off + ix + iy * nxk + iz * nxk * nyk + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = wp.int32(cell_particles[pos]) + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx * dx + dy * dy + dz * dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi * hi or dist2 < hj * hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _multilevel_neighbor_fill_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + out_starts: wp.array(dtype=wp.int32), + origin_x: wp.array(dtype=wp.float32), + origin_y: wp.array(dtype=wp.float32), + origin_z: wp.array(dtype=wp.float32), + cell_size: wp.array(dtype=wp.float32), + nx: wp.array(dtype=wp.int32), + ny: wp.array(dtype=wp.int32), + nz: wp.array(dtype=wp.int32), + cell_offset: wp.array(dtype=wp.int32), + support: wp.array(dtype=wp.float32), + nlevels: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + out = out_starts[i] + count = wp.int32(0) + for k in range(nlevels): + nxk = nx[k] + if nxk > 0: + cs = cell_size[k] + qr = radius_scale * d_h[i] + if support[k] > qr: + qr = support[k] + rx = _ml_cell_range_f32( + d_x[i], qr, origin_x[k], cs, nxk) + iylo = wp.int32(0) + iyhi = wp.int32(0) + nyk = ny[k] + if dim > 1: + ry = _ml_cell_range_f32( + d_y[i], qr, origin_y[k], cs, nyk) + iylo = ry[0] + iyhi = ry[1] + izlo = wp.int32(0) + izhi = wp.int32(0) + nzk = nz[k] + if dim > 2: + rz = _ml_cell_range_f32( + d_z[i], qr, origin_z[k], cs, nzk) + izlo = rz[0] + izhi = rz[1] + off = cell_offset[k] + for iz in range(izlo, izhi + 1): + for iy in range(iylo, iyhi + 1): + for ix in range(rx[0], rx[1] + 1): + cid = off + ix + iy * nxk + iz * nxk * nyk + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = wp.int32(cell_particles[pos]) + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx * dx + dy * dy + dz * dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi * hi or dist2 < hj * hj: + neighbors[out + count] = wp.uint32(j) + count += wp.int32(1) + + +class MultilevelGridWarpNNPS(UniformGridWarpNNPS): + """Exact device-built multilevel cell-list NNPS for adaptive resolution. + + Sources are binned into discrete smoothing-length levels (see + ``assign_particle_levels``). Each populated level gets its own padded + origin, cell size (its conservative support bound) and dimensions; all + levels are flattened into one global cell space via ``cell_offset``. + Neighbor traversal loops over levels, converting each level's query radius + ``max(radius_scale*h_i, support[k])`` into a variable cell-index range (not + a fixed 3x3x3 stencil), then applies the exact symmetric cutoff. The + accepted set therefore matches brute force while candidate work drops for + localized refinement, because a small number of coarse particles no longer + forces coarse cells over dense fine regions. + + This prototype builds the per-level metadata on the host (reading device + coordinates back once per ``update()``); moving level assignment and the + per-level AABB reductions onto the GPU for full device residency is a + separate step that preserves these kernels and only changes ``update()``. + """ + + def __init__(self, dim, particles, radius_scale=2.0, h_ref=None, + level_ratio=2.0, nlevels=1, ghost_layers=1, domain=None, + cache=True, sort_gids=False, backend='warp', device=None): + if h_ref is None: + raise ValueError("MultilevelGridWarpNNPS requires an h_ref") + if nlevels < 1: + raise ValueError("nlevels must be >= 1; got %r" % (nlevels,)) + if h_ref <= 0.0: + raise ValueError("h_ref must be > 0; got %r" % (h_ref,)) + if level_ratio <= 1.0: + raise ValueError("level_ratio must be > 1 so level edges are " + "strictly ascending; got %r" % (level_ratio,)) + self.h_ref = h_ref + self.level_ratio = level_ratio + self.nlevels = nlevels + self._ml = {} + super(MultilevelGridWarpNNPS, self).__init__( + dim=dim, particles=particles, radius_scale=radius_scale, + ghost_layers=ghost_layers, domain=domain, cache=cache, + sort_gids=sort_gids, backend=backend, device=device + ) + + def update(self, push=True): + if push: + for pa in self.particles: + pa.gpu.push('x', 'y', 'z', 'h') + self._flags.clear() + self._cache.clear() + self._ml.clear() + + def _ml_kernels_for(self, gpu): + if gpu.x.dtype == np.float32: + return ( + _multilevel_cell_ids_f32, + _multilevel_neighbor_lengths_f32, + _multilevel_neighbor_fill_f32, + np.float32(self.radius_scale), wp.float32, np.float32, + _ml_assign_reduce_f32, + ) + return ( + _multilevel_cell_ids_f64, + _multilevel_neighbor_lengths_f64, + _multilevel_neighbor_fill_f64, + np.float64(self.radius_scale), wp.float64, np.float64, + _ml_assign_reduce_f64, + ) + + def _build_multilevel(self, src_index): + ml = self._ml.get(src_index) + if ml is not None: + return ml + + gpu = self.particles[src_index].gpu + nsrc = gpu.get_number_of_particles() + dim = self.dim + nlevels = self.nlevels + dev = self.device + cell_ids_k, _, _, _, wpf, npf, assign_k = self._ml_kernels_for(gpu) + + # Level edges in the device float precision; an fp32 h sitting exactly + # on an edge then bins like the edge instead of tripping the guard. + edges_host = (self.h_ref + * self.level_ratio ** np.arange(nlevels + 1)).astype(npf) + + # GPU level assignment + per-level reductions. Only O(nlevels) scalar + # metadata is read back below -- x/y/z/h never leave the device. + level_of = wp.zeros(nsrc if nsrc > 0 else 1, dtype=wp.int32, device=dev) + counts_l = wp.zeros(nlevels, dtype=wp.int32, device=dev) + hmax_l = wp.zeros(nlevels, dtype=wpf, device=dev) + xmin_l = wp.array(np.full(nlevels, np.inf, npf), dtype=wpf, device=dev) + xmax_l = wp.array(np.full(nlevels, -np.inf, npf), dtype=wpf, device=dev) + ymin_l = wp.array(np.full(nlevels, np.inf, npf), dtype=wpf, device=dev) + ymax_l = wp.array(np.full(nlevels, -np.inf, npf), dtype=wpf, device=dev) + zmin_l = wp.array(np.full(nlevels, np.inf, npf), dtype=wpf, device=dev) + zmax_l = wp.array(np.full(nlevels, -np.inf, npf), dtype=wpf, device=dev) + oob = wp.zeros(1, dtype=wp.int32, device=dev) + edges_dev = wp.array(edges_host, dtype=wpf, device=dev) + if nsrc > 0: + wp.launch( + assign_k, dim=nsrc, + inputs=[ + gpu.h.dev, gpu.x.dev, gpu.y.dev, gpu.z.dev, edges_dev, + np.int32(nlevels), np.int32(dim), level_of, counts_l, + hmax_l, xmin_l, xmax_l, ymin_l, ymax_l, zmin_l, zmax_l, + oob, + ], + device=dev, + ) + wp.synchronize_device(dev) + + # O(nlevels) metadata readback (permitted; not the coordinate arrays). + counts = counts_l.numpy() + hmax = hmax_l.numpy() + xmn, xmx = xmin_l.numpy(), xmax_l.numpy() + ymn, ymx = ymin_l.numpy(), ymax_l.numpy() + zmn, zmx = zmin_l.numpy(), zmax_l.numpy() + if nsrc > 0 and int(oob.numpy()[0]) > 0: + raise ValueError( + "smoothing length outside configured level range " + "[%g, %g]; particles are not silently clipped" + % (float(edges_host[0]), float(edges_host[-1])) + ) + + support = np.zeros(nlevels, dtype=np.float64) + ox = np.zeros(nlevels, dtype=np.float64) + oy = np.zeros(nlevels, dtype=np.float64) + oz = np.zeros(nlevels, dtype=np.float64) + cell_size = np.ones(nlevels, dtype=np.float64) + nx = np.zeros(nlevels, dtype=np.int32) # 0 => empty level, no cells + ny = np.ones(nlevels, dtype=np.int32) + nz = np.ones(nlevels, dtype=np.int32) + for k in range(nlevels): + if counts[k] <= 0: + continue + cs = float(self.radius_scale * hmax[k]) + support[k] = cs + cell_size[k] = cs + ox[k] = float(xmn[k]) - cs + nx[k] = max(1, int(np.ceil((float(xmx[k]) + cs - ox[k]) / cs))) + if dim > 1: + oy[k] = float(ymn[k]) - cs + ny[k] = max(1, int(np.ceil((float(ymx[k]) + cs - oy[k]) / cs))) + if dim > 2: + oz[k] = float(zmn[k]) - cs + nz[k] = max(1, int(np.ceil((float(zmx[k]) + cs - oz[k]) / cs))) + + sizes = nx.astype(np.int64) * ny.astype(np.int64) * nz.astype(np.int64) + cell_offset = np.zeros(nlevels, dtype=np.int32) + if nlevels > 1: + cell_offset[1:] = np.cumsum(sizes)[:-1].astype(np.int32) + total_cells = int(sizes.sum()) + + ml = { + 'level_of': level_of, # device, per-particle levels (GPU-computed) + 'origin_x': wp.array(ox.astype(npf), dtype=wpf, device=dev), + 'origin_y': wp.array(oy.astype(npf), dtype=wpf, device=dev), + 'origin_z': wp.array(oz.astype(npf), dtype=wpf, device=dev), + 'cell_size': wp.array(cell_size.astype(npf), dtype=wpf, device=dev), + 'nx': wp.array(nx, dtype=wp.int32, device=dev), + 'ny': wp.array(ny, dtype=wp.int32, device=dev), + 'nz': wp.array(nz, dtype=wp.int32, device=dev), + 'cell_offset': wp.array(cell_offset, dtype=wp.int32, device=dev), + 'support': wp.array(support.astype(npf), dtype=wpf, device=dev), + 'total_cells': total_cells, + 'nsrc': nsrc, + 'support_host': support, + # Host copies in the SAME precision the GPU sees (npf), for the + # device-residency-free diagnostics used by boundary tests. + 'ox_host': ox.astype(npf), 'oy_host': oy.astype(npf), + 'oz_host': oz.astype(npf), 'cs_host': cell_size.astype(npf), + 'nx_host': nx, 'ny_host': ny, 'nz_host': nz, + } + + ncells_alloc = total_cells if total_cells > 0 else 1 + nsrc_alloc = nsrc if nsrc > 0 else 1 + counts_g = wp.zeros(ncells_alloc, dtype=wp.int32, device=dev) + starts = wp.zeros(ncells_alloc, dtype=wp.int32, device=dev) + cursor = wp.zeros(ncells_alloc, dtype=wp.int32, device=dev) + cell_particles = wp.zeros(nsrc_alloc, dtype=wp.uint32, device=dev) + if nsrc > 0 and total_cells > 0: + cell_ids = wp.zeros(nsrc, dtype=wp.int32, device=dev) + wp.launch( + cell_ids_k, + dim=nsrc, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, ml['level_of'], + ml['origin_x'], ml['origin_y'], ml['origin_z'], + ml['cell_size'], ml['nx'], ml['ny'], ml['nz'], + ml['cell_offset'], np.int32(dim), cell_ids, counts_g, + ], + device=dev, + ) + wp.utils.array_scan(counts_g, starts, inclusive=False) + wp.launch(_copy_i32, dim=total_cells, inputs=[starts, cursor], + device=dev) + wp.launch(_scatter_cell_particles, dim=nsrc, + inputs=[cell_ids, cursor, cell_particles], device=dev) + wp.synchronize_device(dev) + ml['counts'] = counts_g + ml['starts'] = starts + ml['cell_particles'] = cell_particles + + self._ml[src_index] = ml + return ml + + def build_neighbor_cache_gpu(self, src_index, dst_index): + """Device-resident multilevel neighbor cache (test/diagnostic oracle). + + Retains only the small lengths/total-size readback used to allocate the + packed neighbor output; the level cell lists and traversal stay on the + GPU. Generated equation kernels walk the level cells directly rather + than materializing this cache. + """ + ml = self._build_multilevel(src_index) + src = self.particles[src_index].gpu + dst = self.particles[dst_index].gpu + dev = self.device + ndst = dst.get_number_of_particles() + _, lengths_k, fill_k, radius_scale, wpf, npf, _ = \ + self._ml_kernels_for(src) + + lengths = wp.zeros(ndst if ndst > 0 else 1, dtype=wp.int32, device=dev) + starts = wp.zeros(ndst if ndst > 0 else 1, dtype=wp.int32, device=dev) + base_inputs = [ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + ml['starts'], ml['counts'], ml['cell_particles'], + ml['origin_x'], ml['origin_y'], ml['origin_z'], ml['cell_size'], + ml['nx'], ml['ny'], ml['nz'], ml['cell_offset'], ml['support'], + np.int32(self.nlevels), np.int32(self.dim), radius_scale, + ] + active = ndst > 0 and ml['total_cells'] > 0 + if active: + wp.launch(lengths_k, dim=ndst, inputs=base_inputs + [lengths], + device=dev) + wp.utils.array_scan(lengths, starts, inclusive=False) + wp.synchronize_device(dev) + + lengths_cpu = lengths.numpy() if ndst > 0 else np.array([], np.int32) + total = int(np.sum(lengths_cpu, dtype=np.int64)) + neighbors = wp.zeros(total if total > 0 else 1, dtype=wp.uint32, + device=dev) + if active and total > 0: + fill_inputs = ( + base_inputs[:11] + [starts] + base_inputs[11:] + [neighbors] + ) + wp.launch(fill_k, dim=ndst, inputs=fill_inputs, device=dev) + wp.synchronize_device(dev) + + return { + 'lengths': lengths_cpu, + 'lengths_dev': lengths, + 'starts_dev': starts, + 'neighbors_dev': neighbors, + 'total_neighbors': total, + } + + def level_grid_info(self, src_index): + """Per-level grid metadata for a source array (test diagnostic). + + Returns host arrays (in the device float precision) so tests can verify + the per-level padding directly -- e.g. that a particle on a level's + far/origin edge floors to a cell index inside ``[0, n)`` *before* the + binning kernel's clamp, which would otherwise mask a padding defect. + """ + ml = self._build_multilevel(src_index) + # Per-particle levels are read back from the device here (diagnostic + # path only -- NOT on the warm update/traversal path). + levels = (ml['level_of'].numpy()[:ml['nsrc']] if ml['nsrc'] > 0 + else np.zeros(0, dtype=np.int32)) + return { + 'levels': levels, + 'support': ml['support_host'], + 'origin_x': ml['ox_host'], 'origin_y': ml['oy_host'], + 'origin_z': ml['oz_host'], 'cell_size': ml['cs_host'], + 'nx': ml['nx_host'], 'ny': ml['ny_host'], 'nz': ml['nz_host'], + } + + def candidate_pairs(self, src_index, dst_index): + """Total candidate pairs the multilevel traversal distance-tests. + + Host-side diagnostic (benchmark path, NOT the residency-constrained + query path): mirrors the traversal's per-level variable cell-range scan + and sums the source-particle counts in every scanned cell. Used to show + the candidate-work reduction versus the global-hmax uniform grid. The + accepted set is always a subset of the candidate set. + """ + ml = self._build_multilevel(src_index) + dst = self.particles[dst_index].gpu + nlevels, dim, rs = self.nlevels, self.dim, self.radius_scale + d_x, d_y, d_z, d_h = (dst.x.get(), dst.y.get(), dst.z.get(), + dst.h.get()) + counts = ml['counts'].numpy() + ox, oy, oz = ml['ox_host'], ml['oy_host'], ml['oz_host'] + cs, sup = ml['cs_host'], ml['support_host'] + nx, ny, nz = ml['nx_host'], ml['ny_host'], ml['nz_host'] + sizes = (nx.astype(np.int64) * ny.astype(np.int64) + * nz.astype(np.int64)) + offset = np.zeros(nlevels, dtype=np.int64) + if nlevels > 1: + offset[1:] = np.cumsum(sizes)[:-1] + + def _rng(c, o, csk, n): + lo = int(np.floor((c - qr - o) / csk)) - 1 + hi = int(np.floor((c + qr - o) / csk)) + 1 + return max(0, lo), min(int(n) - 1, hi) + + total = 0 + for i in range(len(d_x)): + for k in range(nlevels): + if nx[k] <= 0: + continue + csk = float(cs[k]) + qr = max(rs * float(d_h[i]), float(sup[k])) + ixlo, ixhi = _rng(float(d_x[i]), float(ox[k]), csk, nx[k]) + iylo, iyhi = ((0, 0) if dim < 2 else + _rng(float(d_y[i]), float(oy[k]), csk, ny[k])) + izlo, izhi = ((0, 0) if dim < 3 else + _rng(float(d_z[i]), float(oz[k]), csk, nz[k])) + for iz in range(izlo, izhi + 1): + for iy in range(iylo, iyhi + 1): + base = offset[k] + iz * nx[k] * ny[k] + iy * nx[k] + total += int(counts[base + ixlo: base + ixhi + 1].sum()) + return total diff --git a/pysph/base/warp_nnps.py b/pysph/base/warp_nnps.py new file mode 100644 index 000000000..4e693c24b --- /dev/null +++ b/pysph/base/warp_nnps.py @@ -0,0 +1,1307 @@ +"""NVIDIA Warp nearest-neighbor particle search prototypes.""" + +import numpy as np + +try: + import warp as wp +except ImportError: # pragma: no cover + wp = None + +from cyarray.carray import UIntArray + +from pysph.base.warp_device_helper import WarpDeviceHelper + + +def assign_particle_levels(h, h_ref, level_ratio, nlevels, radius_scale): + """Bin smoothing lengths into discrete resolution levels (multilevel NNPS). + + Range-bin, half-open contract: level ``k`` covers ``h`` in + ``[h_ref*level_ratio**k, h_ref*level_ratio**(k+1))`` with level 0 the + finest. The overall top edge ``h_ref*level_ratio**nlevels`` is inclusive. + + Returns ``(levels, support)``: ``levels`` is an ``int32`` array of + per-particle level indices; ``support`` is a length-``nlevels`` array (its + per-level conservative support bound is driven by a later test). + """ + if nlevels < 1: + raise ValueError("nlevels must be >= 1; got %r" % (nlevels,)) + if h_ref <= 0.0: + raise ValueError("h_ref must be > 0; got %r" % (h_ref,)) + if level_ratio <= 1.0: + raise ValueError( + "level_ratio must be > 1 so level edges h_ref*level_ratio**k are " + "strictly ascending; got %r" % (level_ratio,) + ) + h = np.asarray(h) + if h.dtype.kind != 'f': + h = h.astype(np.float64) + # Compute the level edges in the same float precision as h. When h comes + # from an fp32 device array, an h sitting exactly on a level edge (e.g. the + # inclusive top edge) rounds to the same fp32 value as the edge, so it bins + # correctly instead of tripping the range guard by one fp32 ULP. + edges = h_ref * level_ratio ** np.arange(nlevels + 1, dtype=np.float64) + edges = edges.astype(h.dtype) + if h.size and (np.min(h) < edges[0] or np.max(h) > edges[-1]): + raise ValueError( + "smoothing length outside configured level range " + "[%g, %g]; particles are not silently clipped" % ( + edges[0], edges[-1]) + ) + levels = np.searchsorted(edges, h, side='right') - 1 + # Fold the inclusive top edge (which searchsorted maps to nlevels) down + # into the top level. + levels = np.where(h == edges[-1], nlevels - 1, levels) + support = np.zeros(nlevels, dtype=np.float64) + if h.size: + np.maximum.at(support, levels, radius_scale * h) + return levels.astype(np.int32), support + + +def brute_force_neighbor_sets(dst, src, radius_scale, dim): + """Exact symmetric neighbor sets by an O(N*M) host scan (test oracle). + + ``dst`` and ``src`` are ``(x, y, z, h)`` tuples of numpy arrays. Returns a + list with one sorted ``int`` array per destination, containing every source + index ``j`` satisfying the symmetric contract + + rij^2 < (radius_scale*h_i)^2 OR rij^2 < (radius_scale*h_j)^2 + + matching ``_neighbor_flags``. Self is included when ``src is dst`` (rij=0). + This is an independent reference used to triangulate ``BruteForceWarpNNPS`` + and the multilevel NNPS; it is not on any runtime path. + """ + d_x, d_y, d_z, d_h = (np.asarray(a, dtype=np.float64) for a in dst) + s_x, s_y, s_z, s_h = (np.asarray(a, dtype=np.float64) for a in src) + sets = [] + hj = radius_scale * s_h + for i in range(d_x.size): + dist2 = (d_x[i] - s_x) ** 2 + if dim > 1: + dist2 = dist2 + (d_y[i] - s_y) ** 2 + if dim > 2: + dist2 = dist2 + (d_z[i] - s_z) ** 2 + hi = radius_scale * d_h[i] + mask = (dist2 < hi * hi) | (dist2 < hj * hj) + sets.append(np.nonzero(mask)[0].astype(np.int32)) + return sets + + +def accepted_level_pair_counts(neighbor_sets, d_levels, s_levels, nlevels): + """Bin accepted neighbor pairs by ``(destination-level, source-level)``. + + ``neighbor_sets[i]`` holds the accepted source indices for destination + ``i``. Returns an ``(nlevels, nlevels)`` integer matrix whose ``[ld, ls]`` + entry counts accepted pairs whose destination is at level ``ld`` and source + at level ``ls`` -- the breakdown the decision gate uses to see where + traversal work concentrates (coarse-destination x fine-level). + """ + d_levels = np.asarray(d_levels) + s_levels = np.asarray(s_levels) + counts = np.zeros((nlevels, nlevels), dtype=np.int64) + for i, nbrs in enumerate(neighbor_sets): + nbrs = np.asarray(nbrs, dtype=np.intp) + if nbrs.size: + np.add.at(counts[int(d_levels[i])], s_levels[nbrs], 1) + return counts + + +if wp is not None: + @wp.kernel + def _copy_i32(src: wp.array(dtype=wp.int32), + dst: wp.array(dtype=wp.int32)): + i = wp.tid() + dst[i] = src[i] + + + @wp.kernel + def _zero_i32(dst: wp.array(dtype=wp.int32)): + i = wp.tid() + dst[i] = wp.int32(0) + + + @wp.kernel + def _neighbor_flags_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + d_idx: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + flags: wp.array(dtype=wp.uint8), + ): + j = wp.tid() + dx = d_x[d_idx] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[d_idx] - s_y[j] + if dim > 2: + dz = d_z[d_idx] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[d_idx] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + flags[j] = wp.uint8(1) + else: + flags[j] = wp.uint8(0) + + + @wp.kernel + def _neighbor_flags_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + d_idx: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + flags: wp.array(dtype=wp.uint8), + ): + j = wp.tid() + dx = d_x[d_idx] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[d_idx] - s_y[j] + if dim > 2: + dz = d_z[d_idx] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[d_idx] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + flags[j] = wp.uint8(1) + else: + flags[j] = wp.uint8(0) + + + @wp.kernel + def _neighbor_lengths_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + nsrc: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + count = wp.int32(0) + for j in range(nsrc): + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _neighbor_lengths_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + nsrc: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + count = wp.int32(0) + for j in range(nsrc): + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _neighbor_fill_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + starts: wp.array(dtype=wp.int32), + nsrc: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + k = wp.int32(0) + start = starts[i] + for j in range(nsrc): + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + neighbors[start + k] = wp.uint32(j) + k += wp.int32(1) + + + @wp.kernel + def _neighbor_fill_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + starts: wp.array(dtype=wp.int32), + nsrc: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + k = wp.int32(0) + start = starts[i] + for j in range(nsrc): + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + neighbors[start + k] = wp.uint32(j) + k += wp.int32(1) + + + @wp.kernel + def _cell_ids_counts_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + xmin: wp.float64, + ymin: wp.float64, + zmin: wp.float64, + cell_size: wp.float64, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + dim: wp.int32, + periodic_x: wp.int32, + periodic_y: wp.int32, + periodic_z: wp.int32, + cell_ids: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + ): + i = wp.tid() + ix = wp.int32(wp.floor((x[i] - xmin) / cell_size)) + iy = wp.int32(0) + iz = wp.int32(0) + if dim > 1: + iy = wp.int32(wp.floor((y[i] - ymin) / cell_size)) + if dim > 2: + iz = wp.int32(wp.floor((z[i] - zmin) / cell_size)) + # Periodic dims wrap into the canonical [0, n) cell (so positions just + # outside the box bin into their periodic image cell, consistent with + # the wrapped cell walk); non-periodic dims clamp to the edge cell. + if periodic_x == wp.int32(1): + ix = ((ix % nx) + nx) % nx + else: + ix = wp.clamp(ix, wp.int32(0), nx - wp.int32(1)) + if periodic_y == wp.int32(1): + iy = ((iy % ny) + ny) % ny + else: + iy = wp.clamp(iy, wp.int32(0), ny - wp.int32(1)) + if periodic_z == wp.int32(1): + iz = ((iz % nz) + nz) % nz + else: + iz = wp.clamp(iz, wp.int32(0), nz - wp.int32(1)) + cid = ix + iy * nx + iz * nx * ny + cell_ids[i] = cid + wp.atomic_add(counts, cid, wp.int32(1)) + + + @wp.kernel + def _cell_ids_counts_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + xmin: wp.float32, + ymin: wp.float32, + zmin: wp.float32, + cell_size: wp.float32, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + dim: wp.int32, + periodic_x: wp.int32, + periodic_y: wp.int32, + periodic_z: wp.int32, + cell_ids: wp.array(dtype=wp.int32), + counts: wp.array(dtype=wp.int32), + ): + i = wp.tid() + ix = wp.int32(wp.floor((x[i] - xmin) / cell_size)) + iy = wp.int32(0) + iz = wp.int32(0) + if dim > 1: + iy = wp.int32(wp.floor((y[i] - ymin) / cell_size)) + if dim > 2: + iz = wp.int32(wp.floor((z[i] - zmin) / cell_size)) + # Periodic dims wrap into the canonical [0, n) cell (so positions just + # outside the box bin into their periodic image cell, consistent with + # the wrapped cell walk); non-periodic dims clamp to the edge cell. + if periodic_x == wp.int32(1): + ix = ((ix % nx) + nx) % nx + else: + ix = wp.clamp(ix, wp.int32(0), nx - wp.int32(1)) + if periodic_y == wp.int32(1): + iy = ((iy % ny) + ny) % ny + else: + iy = wp.clamp(iy, wp.int32(0), ny - wp.int32(1)) + if periodic_z == wp.int32(1): + iz = ((iz % nz) + nz) % nz + else: + iz = wp.clamp(iz, wp.int32(0), nz - wp.int32(1)) + cid = ix + iy * nx + iz * nx * ny + cell_ids[i] = cid + wp.atomic_add(counts, cid, wp.int32(1)) + + + @wp.kernel + def _scatter_cell_particles( + cell_ids: wp.array(dtype=wp.int32), + cursor: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + cid = cell_ids[i] + out = wp.atomic_add(cursor, cid, wp.int32(1)) + cell_particles[out] = wp.uint32(i) + + + @wp.kernel + def _grid_neighbor_lengths_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + xmin: wp.float64, + ymin: wp.float64, + zmin: wp.float64, + cell_size: wp.float64, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + ncells: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + ix0 = wp.int32(wp.floor((d_x[i] - xmin) / cell_size)) + iy0 = wp.int32(0) + iz0 = wp.int32(0) + if dim > 1: + iy0 = wp.int32(wp.floor((d_y[i] - ymin) / cell_size)) + if dim > 2: + iz0 = wp.int32(wp.floor((d_z[i] - zmin) / cell_size)) + count = wp.int32(0) + for dzc in range(-1, 2): + for dyc in range(-1, 2): + for dxc in range(-1, 2): + ix = ix0 + wp.int32(dxc) + iy = iy0 + wp.int32(dyc) + iz = iz0 + wp.int32(dzc) + if ix >= 0 and ix < nx and iy >= 0 and iy < ny and iz >= 0 and iz < nz: + cid = ix + iy * nx + iz * nx * ny + if cid >= 0 and cid < ncells: + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = cell_particles[pos] + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _grid_neighbor_lengths_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + xmin: wp.float32, + ymin: wp.float32, + zmin: wp.float32, + cell_size: wp.float32, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + ncells: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + lengths: wp.array(dtype=wp.int32), + ): + i = wp.tid() + ix0 = wp.int32(wp.floor((d_x[i] - xmin) / cell_size)) + iy0 = wp.int32(0) + iz0 = wp.int32(0) + if dim > 1: + iy0 = wp.int32(wp.floor((d_y[i] - ymin) / cell_size)) + if dim > 2: + iz0 = wp.int32(wp.floor((d_z[i] - zmin) / cell_size)) + count = wp.int32(0) + for dzc in range(-1, 2): + for dyc in range(-1, 2): + for dxc in range(-1, 2): + ix = ix0 + wp.int32(dxc) + iy = iy0 + wp.int32(dyc) + iz = iz0 + wp.int32(dzc) + if ix >= 0 and ix < nx and iy >= 0 and iy < ny and iz >= 0 and iz < nz: + cid = ix + iy * nx + iz * nx * ny + if cid >= 0 and cid < ncells: + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = cell_particles[pos] + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + count += wp.int32(1) + lengths[i] = count + + + @wp.kernel + def _grid_neighbor_fill_f64( + s_x: wp.array(dtype=wp.float64), + s_y: wp.array(dtype=wp.float64), + s_z: wp.array(dtype=wp.float64), + s_h: wp.array(dtype=wp.float64), + d_x: wp.array(dtype=wp.float64), + d_y: wp.array(dtype=wp.float64), + d_z: wp.array(dtype=wp.float64), + d_h: wp.array(dtype=wp.float64), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + starts: wp.array(dtype=wp.int32), + xmin: wp.float64, + ymin: wp.float64, + zmin: wp.float64, + cell_size: wp.float64, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + ncells: wp.int32, + dim: wp.int32, + radius_scale: wp.float64, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + ix0 = wp.int32(wp.floor((d_x[i] - xmin) / cell_size)) + iy0 = wp.int32(0) + iz0 = wp.int32(0) + if dim > 1: + iy0 = wp.int32(wp.floor((d_y[i] - ymin) / cell_size)) + if dim > 2: + iz0 = wp.int32(wp.floor((d_z[i] - zmin) / cell_size)) + k = wp.int32(0) + out_start = starts[i] + for dzc in range(-1, 2): + for dyc in range(-1, 2): + for dxc in range(-1, 2): + ix = ix0 + wp.int32(dxc) + iy = iy0 + wp.int32(dyc) + iz = iz0 + wp.int32(dzc) + if ix >= 0 and ix < nx and iy >= 0 and iy < ny and iz >= 0 and iz < nz: + cid = ix + iy * nx + iz * nx * ny + if cid >= 0 and cid < ncells: + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = cell_particles[pos] + dx = d_x[i] - s_x[j] + dy = wp.float64(0.0) + dz = wp.float64(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + neighbors[out_start + k] = j + k += wp.int32(1) + + + @wp.kernel + def _grid_neighbor_fill_f32( + s_x: wp.array(dtype=wp.float32), + s_y: wp.array(dtype=wp.float32), + s_z: wp.array(dtype=wp.float32), + s_h: wp.array(dtype=wp.float32), + d_x: wp.array(dtype=wp.float32), + d_y: wp.array(dtype=wp.float32), + d_z: wp.array(dtype=wp.float32), + d_h: wp.array(dtype=wp.float32), + cell_starts: wp.array(dtype=wp.int32), + cell_counts: wp.array(dtype=wp.int32), + cell_particles: wp.array(dtype=wp.uint32), + starts: wp.array(dtype=wp.int32), + xmin: wp.float32, + ymin: wp.float32, + zmin: wp.float32, + cell_size: wp.float32, + nx: wp.int32, + ny: wp.int32, + nz: wp.int32, + ncells: wp.int32, + dim: wp.int32, + radius_scale: wp.float32, + neighbors: wp.array(dtype=wp.uint32), + ): + i = wp.tid() + ix0 = wp.int32(wp.floor((d_x[i] - xmin) / cell_size)) + iy0 = wp.int32(0) + iz0 = wp.int32(0) + if dim > 1: + iy0 = wp.int32(wp.floor((d_y[i] - ymin) / cell_size)) + if dim > 2: + iz0 = wp.int32(wp.floor((d_z[i] - zmin) / cell_size)) + k = wp.int32(0) + out_start = starts[i] + for dzc in range(-1, 2): + for dyc in range(-1, 2): + for dxc in range(-1, 2): + ix = ix0 + wp.int32(dxc) + iy = iy0 + wp.int32(dyc) + iz = iz0 + wp.int32(dzc) + if ix >= 0 and ix < nx and iy >= 0 and iy < ny and iz >= 0 and iz < nz: + cid = ix + iy * nx + iz * nx * ny + if cid >= 0 and cid < ncells: + start = cell_starts[cid] + stop = start + cell_counts[cid] + for pos in range(start, stop): + j = cell_particles[pos] + dx = d_x[i] - s_x[j] + dy = wp.float32(0.0) + dz = wp.float32(0.0) + if dim > 1: + dy = d_y[i] - s_y[j] + if dim > 2: + dz = d_z[i] - s_z[j] + dist2 = dx*dx + dy*dy + dz*dz + hi = radius_scale * d_h[i] + hj = radius_scale * s_h[j] + if dist2 < hi*hi or dist2 < hj*hj: + neighbors[out_start + k] = j + k += wp.int32(1) + + + @wp.kernel + def _neighbor_sum_f64( + values: wp.array(dtype=wp.float64), + starts: wp.array(dtype=wp.int32), + lengths: wp.array(dtype=wp.int32), + neighbors: wp.array(dtype=wp.uint32), + out: wp.array(dtype=wp.float64), + ): + i = wp.tid() + total = wp.float64(0.0) + start = starts[i] + stop = start + lengths[i] + for pos in range(start, stop): + j = wp.int32(neighbors[pos]) + total += values[j] + out[i] = total + + + @wp.kernel + def _neighbor_sum_f32( + values: wp.array(dtype=wp.float32), + starts: wp.array(dtype=wp.int32), + lengths: wp.array(dtype=wp.int32), + neighbors: wp.array(dtype=wp.uint32), + out: wp.array(dtype=wp.float32), + ): + i = wp.tid() + total = wp.float32(0.0) + start = starts[i] + stop = start + lengths[i] + for pos in range(start, stop): + j = wp.int32(neighbors[pos]) + total += values[j] + out[i] = total + + +class BruteForceWarpNNPS(object): + """Brute-force NNPS using Warp arrays for the distance test. + + This class is intentionally small and compatibility-oriented. It provides + the public query contract used by PySPH's NNPS tests while establishing a + Warp-native correctness baseline before a cell-list implementation. + """ + + def __init__(self, dim, particles, radius_scale=2.0, ghost_layers=1, + domain=None, cache=False, sort_gids=False, backend='warp', + device=None): + if wp is None: # pragma: no cover + raise ImportError("warp is required for BruteForceWarpNNPS") + self.dim = dim + self.particles = particles + self.radius_scale = radius_scale + self.ghost_layers = ghost_layers + self.domain = domain + self.use_cache = cache + self.sort_gids = sort_gids + self.backend = backend + self.device = wp.get_device(device) + self.narrays = len(particles) + self.src_index = -1 + self.dst_index = -1 + self.src = None + self.dst = None + self._flags = {} + self._cache = {} + + for pa in self.particles: + if pa.gpu is None or getattr(pa.gpu, 'backend', None) != 'warp': + pa.set_device_helper( + WarpDeviceHelper(pa, backend='warp', device=self.device) + ) + + self.update_domain() + self.update() + + def update_domain(self): + if self.domain is not None: + self.domain.update() + + def update(self, push=True): + if push: + for pa in self.particles: + pa.gpu.push('x', 'y', 'z', 'h') + self._flags.clear() + self._cache.clear() + + def set_use_cache(self, use_cache): + self.use_cache = use_cache + + def set_context(self, src_index, dst_index): + self.src_index = src_index + self.dst_index = dst_index + self.src = self.particles[src_index] + self.dst = self.particles[dst_index] + + def _get_flags(self, src_index): + src = self.particles[src_index] + size = src.gpu.get_number_of_particles() + flags = self._flags.get(src_index) + if flags is None or flags.shape[0] != size: + flags = wp.empty(size, dtype=wp.uint8, device=self.device) + self._flags[src_index] = flags + return flags + + def _launch_flags(self, src_index, dst_index, d_idx): + src = self.particles[src_index].gpu + dst = self.particles[dst_index].gpu + flags = self._get_flags(src_index) + nsrc = src.get_number_of_particles() + if nsrc == 0: + return np.array([], dtype=np.uint8) + + if src.x.dtype == np.float32: + kernel = _neighbor_flags_f32 + radius_scale = np.float32(self.radius_scale) + else: + kernel = _neighbor_flags_f64 + radius_scale = np.float64(self.radius_scale) + wp.launch( + kernel, + dim=nsrc, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + np.int32(d_idx), np.int32(self.dim), radius_scale, flags + ], + device=self.device, + ) + wp.synchronize_device(self.device) + return flags.numpy() + + def _kernels_for(self, src): + if src.x.dtype == np.float32: + return ( + _neighbor_flags_f32, + _neighbor_lengths_f32, + _neighbor_fill_f32, + np.float32(self.radius_scale), + ) + else: + return ( + _neighbor_flags_f64, + _neighbor_lengths_f64, + _neighbor_fill_f64, + np.float64(self.radius_scale), + ) + + def _build_cache(self, src_index, dst_index): + src = self.particles[src_index].gpu + dst = self.particles[dst_index].gpu + nsrc = src.get_number_of_particles() + ndst = dst.get_number_of_particles() + lengths = wp.empty(ndst, dtype=wp.int32, device=self.device) + starts = wp.empty(ndst, dtype=wp.int32, device=self.device) + _, lengths_kernel, fill_kernel, radius_scale = self._kernels_for(src) + + if ndst > 0: + wp.launch( + lengths_kernel, + dim=ndst, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + np.int32(nsrc), np.int32(self.dim), radius_scale, lengths + ], + device=self.device, + ) + wp.utils.array_scan(lengths, starts, inclusive=False) + wp.synchronize_device(self.device) + + lengths_cpu = lengths.numpy() if ndst > 0 else np.array([], np.int32) + starts_cpu = starts.numpy() if ndst > 0 else np.array([], np.int32) + total = 0 + if ndst > 0: + total = int(starts_cpu[-1] + lengths_cpu[-1]) + neighbors = wp.empty(total, dtype=wp.uint32, device=self.device) + if ndst > 0 and total > 0: + wp.launch( + fill_kernel, + dim=ndst, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + starts, np.int32(nsrc), np.int32(self.dim), radius_scale, + neighbors + ], + device=self.device, + ) + wp.synchronize_device(self.device) + neighbors_cpu = ( + neighbors.numpy() if total > 0 else np.array([], dtype=np.uint32) + ) + cache = { + 'lengths': lengths_cpu, + 'starts': starts_cpu, + 'neighbors': neighbors_cpu, + } + self._cache[(src_index, dst_index)] = cache + return cache + + def _get_cached_neighbors(self, src_index, dst_index, d_idx): + cache = self._cache.get((src_index, dst_index)) + if cache is None: + cache = self._build_cache(src_index, dst_index) + start = int(cache['starts'][d_idx]) + stop = start + int(cache['lengths'][d_idx]) + return cache['neighbors'][start:stop].astype(np.uint32, copy=False) + + def get_nearest_particles(self, src_index, dst_index, d_idx, nbrs): + if self.use_cache: + indices = self._get_cached_neighbors(src_index, dst_index, d_idx) + else: + flags = self._launch_flags(src_index, dst_index, d_idx) + indices = np.nonzero(flags)[0].astype(np.uint32) + if self.sort_gids and len(indices) > 0: + gids = self.particles[src_index].properties['gid'].get_npy_array() + if gids[0] == np.iinfo(np.uint32).max: + indices.sort() + else: + order = np.argsort(gids[indices], kind='stable') + indices = indices[order].astype(np.uint32) + nbrs.reset() + for index in indices: + nbrs.append(int(index)) + + def brute_force_neighbors(self, src_index, dst_index, d_idx, nbrs): + self.get_nearest_particles(src_index, dst_index, d_idx, nbrs) + + def get_nearest_particles_gpu(self, src_index, dst_index): + raise NotImplementedError( + "BruteForceWarpNNPS does not yet build cached GPU neighbor lists" + ) + + def spatially_order_particles(self, pa_index): + raise NotImplementedError( + "BruteForceWarpNNPS does not define a spatial ordering" + ) + + +class UniformGridWarpNNPS(BruteForceWarpNNPS): + """Uniform-grid Warp NNPS using device-side cell lists. + + The implementation builds a per-source flat cell list on the device and + then builds cached flat neighbor lists by scanning adjacent cells. It keeps + the same host-facing `get_nearest_particles()` contract as other PySPH NNPS + implementations. + """ + + def __init__(self, dim, particles, radius_scale=2.0, ghost_layers=1, + domain=None, cache=True, sort_gids=False, backend='warp', + device=None): + self._grid = {} + self._bounds = None + self.cell_size = 1.0 + self._periodic_box = None + super(UniformGridWarpNNPS, self).__init__( + dim=dim, particles=particles, radius_scale=radius_scale, + ghost_layers=ghost_layers, domain=domain, cache=cache, + sort_gids=sort_gids, backend=backend, device=device + ) + self.use_cache = True + + def update(self, push=True): + if push: + for pa in self.particles: + pa.gpu.push('x', 'y', 'z', 'h') + self._flags.clear() + self._cache.clear() + self._grid.clear() + self._compute_bounds_and_cell_size() + + def set_use_cache(self, use_cache): + if not use_cache: + raise ValueError("UniformGridWarpNNPS requires cached queries") + self.use_cache = True + + def set_periodic_box(self, bounds): + """Set the periodic domain box for minimum-image neighbor handling. + + ``bounds`` is a dict with ``xmin``/``xmax`` (and ``ymin``/``ymax``, + ``zmin``/``zmax``) plus optional ``periodic_in_{x,y,z}`` flags (a dim is + periodic by default if both its min/max are present), or ``None`` to + disable. In periodic dimensions the grid tiles the box exactly so the + cell-index wrap is consistent; the MVP requires equal-length periodic + dimensions (a cubic periodic box). Recomputed on the next ``update()``. + """ + self._periodic_box = bounds + self._grid.clear() + if self._bounds is not None: + self._compute_bounds_and_cell_size() + + def _periodic_flag(self, axis, dim_ok): + box = self._periodic_box + if box is None: + return False + key = 'periodic_in_' + axis + if key in box: + return bool(box[key]) and dim_ok + return (axis + 'min' in box and axis + 'max' in box) and dim_ok + + def _compute_bounds_and_cell_size(self): + xmin = ymin = zmin = np.inf + xmax = ymax = zmax = -np.inf + hmax = 0.0 + for pa in self.particles: + x = pa.gpu.x.get() + y = pa.gpu.y.get() + z = pa.gpu.z.get() + h = pa.gpu.h.get() + if len(x) == 0: + continue + xmin = min(xmin, float(np.min(x))) + xmax = max(xmax, float(np.max(x))) + if self.dim > 1: + ymin = min(ymin, float(np.min(y))) + ymax = max(ymax, float(np.max(y))) + else: + ymin = ymax = 0.0 + if self.dim > 2: + zmin = min(zmin, float(np.min(z))) + zmax = max(zmax, float(np.max(z))) + else: + zmin = zmax = 0.0 + hmax = max(hmax, float(np.max(h))) + + if not np.isfinite(xmin): + xmin = ymin = zmin = -0.5 + xmax = ymax = zmax = 0.5 + cell_min = self.radius_scale * hmax + if cell_min <= 1e-14: + cell_min = 1.0 + + box = self._periodic_box + px = self._periodic_flag('x', True) + py = self._periodic_flag('y', self.dim > 1) + pz = self._periodic_flag('z', self.dim > 2) + + # A periodic dim must supply its min/max so the box can tile. + for axis, p in (('x', px), ('y', py), ('z', pz)): + if p and not (axis + 'min' in box and axis + 'max' in box): + raise ValueError( + "periodic_in_%s is set but %smin/%smax are missing from " + "the periodic box" % (axis, axis, axis) + ) + + # A single cell size is used for binning in all dimensions; periodic + # dimensions must tile their box exactly with it. Requirements: + # equal-length periodic dims (one cell size tiles them), and + # n = floor(L/cell_min) >= 3 -- so cell_size = L/n >= cell_min (the + # 3-cell stencil covers the support) and L >= 3*support > 2*support (so + # minimum image is unique). Otherwise raise rather than silently clamp. + periodic_lengths = [] + if px: + periodic_lengths.append(float(box['xmax']) - float(box['xmin'])) + if py: + periodic_lengths.append(float(box['ymax']) - float(box['ymin'])) + if pz: + periodic_lengths.append(float(box['zmax']) - float(box['zmin'])) + if periodic_lengths: + if (max(periodic_lengths) - min(periodic_lengths) + > 1e-6 * max(periodic_lengths)): + raise ValueError( + "periodic minimum-image requires equal-length periodic " + "dimensions (cubic box); got lengths %r" % periodic_lengths + ) + length = periodic_lengths[0] + ncell = int(np.floor(length / cell_min)) + if ncell < 3: + raise ValueError( + "periodic box length %g is too small for support " + "radius_scale*h=%g; minimum-image neighbors need " + "L >= 3*radius_scale*h" % (length, cell_min) + ) + self.cell_size = length / ncell + else: + self.cell_size = cell_min + + box_lx = box_ly = box_lz = 0.0 + if px: + xmin = float(box['xmin']) + box_lx = float(box['xmax']) - xmin + nx = max(3, int(round(box_lx / self.cell_size))) + else: + xmin -= self.cell_size + xmax += self.cell_size + nx = max(1, int(np.ceil((xmax - xmin) / self.cell_size))) + ny = 1 + if self.dim > 1: + if py: + ymin = float(box['ymin']) + box_ly = float(box['ymax']) - ymin + ny = max(3, int(round(box_ly / self.cell_size))) + else: + ymin -= self.cell_size + ymax += self.cell_size + ny = max(1, int(np.ceil((ymax - ymin) / self.cell_size))) + nz = 1 + if self.dim > 2: + if pz: + zmin = float(box['zmin']) + box_lz = float(box['zmax']) - zmin + nz = max(3, int(round(box_lz / self.cell_size))) + else: + zmin -= self.cell_size + zmax += self.cell_size + nz = max(1, int(np.ceil((zmax - zmin) / self.cell_size))) + + self._bounds = { + 'xmin': xmin, 'ymin': ymin, 'zmin': zmin, + 'nx': nx, 'ny': ny, 'nz': nz, + 'ncells': nx * ny * nz, + 'box_lx': box_lx, 'box_ly': box_ly, 'box_lz': box_lz, + 'periodic_x': px, 'periodic_y': py, 'periodic_z': pz, + } + + def _scalar(self, value, gpu): + if gpu.x.dtype == np.float32: + return np.float32(value) + return np.float64(value) + + def _grid_kernels_for(self, gpu): + if gpu.x.dtype == np.float32: + return ( + _cell_ids_counts_f32, + _grid_neighbor_lengths_f32, + _grid_neighbor_fill_f32, + np.float32(self.radius_scale), + ) + return ( + _cell_ids_counts_f64, + _grid_neighbor_lengths_f64, + _grid_neighbor_fill_f64, + np.float64(self.radius_scale), + ) + + def _build_grid(self, src_index): + grid = self._grid.get(src_index) + if grid is not None: + return grid + + src = self.particles[src_index].gpu + nsrc = src.get_number_of_particles() + bounds = self._bounds + ncells = bounds['ncells'] + cell_ids = wp.empty(nsrc, dtype=wp.int32, device=self.device) + counts = wp.empty(ncells, dtype=wp.int32, device=self.device) + starts = wp.empty(ncells, dtype=wp.int32, device=self.device) + cursor = wp.empty(ncells, dtype=wp.int32, device=self.device) + cell_particles = wp.empty(nsrc, dtype=wp.uint32, device=self.device) + ids_kernel, _, _, _ = self._grid_kernels_for(src) + + if ncells > 0: + wp.launch(_zero_i32, dim=ncells, inputs=[counts], + device=self.device) + if nsrc > 0: + wp.launch( + ids_kernel, + dim=nsrc, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, + self._scalar(bounds['xmin'], src), + self._scalar(bounds['ymin'], src), + self._scalar(bounds['zmin'], src), + self._scalar(self.cell_size, src), + np.int32(bounds['nx']), np.int32(bounds['ny']), + np.int32(bounds['nz']), np.int32(self.dim), + np.int32(1 if bounds.get('periodic_x') else 0), + np.int32(1 if bounds.get('periodic_y') else 0), + np.int32(1 if bounds.get('periodic_z') else 0), + cell_ids, counts + ], + device=self.device, + ) + if ncells > 0: + wp.utils.array_scan(counts, starts, inclusive=False) + wp.launch(_copy_i32, dim=ncells, inputs=[starts, cursor], + device=self.device) + if nsrc > 0: + wp.launch( + _scatter_cell_particles, + dim=nsrc, + inputs=[cell_ids, cursor, cell_particles], + device=self.device, + ) + wp.synchronize_device(self.device) + + grid = { + 'cell_ids': cell_ids, + 'counts': counts, + 'starts': starts, + 'cell_particles': cell_particles, + } + self._grid[src_index] = grid + return grid + + def build_neighbor_cache_gpu(self, src_index, dst_index): + """Build and return a device-resident neighbor cache. + + This avoids the host-facing per-particle `UIntArray` query path. A + small lengths readback is retained to size the flat neighbor array and + report average neighbor count. + """ + src = self.particles[src_index].gpu + dst = self.particles[dst_index].gpu + grid = self._build_grid(src_index) + bounds = self._bounds + ndst = dst.get_number_of_particles() + lengths = wp.empty(ndst, dtype=wp.int32, device=self.device) + starts = wp.empty(ndst, dtype=wp.int32, device=self.device) + _, lengths_kernel, fill_kernel, radius_scale = \ + self._grid_kernels_for(src) + + if ndst > 0: + wp.launch( + lengths_kernel, + dim=ndst, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + grid['starts'], grid['counts'], grid['cell_particles'], + self._scalar(bounds['xmin'], src), + self._scalar(bounds['ymin'], src), + self._scalar(bounds['zmin'], src), + self._scalar(self.cell_size, src), + np.int32(bounds['nx']), np.int32(bounds['ny']), + np.int32(bounds['nz']), np.int32(bounds['ncells']), + np.int32(self.dim), radius_scale, lengths + ], + device=self.device, + ) + wp.utils.array_scan(lengths, starts, inclusive=False) + wp.synchronize_device(self.device) + + lengths_cpu = lengths.numpy() if ndst > 0 else np.array([], np.int32) + total = int(np.sum(lengths_cpu, dtype=np.int64)) + neighbors = wp.empty(total, dtype=wp.uint32, device=self.device) + if ndst > 0 and total > 0: + wp.launch( + fill_kernel, + dim=ndst, + inputs=[ + src.x.dev, src.y.dev, src.z.dev, src.h.dev, + dst.x.dev, dst.y.dev, dst.z.dev, dst.h.dev, + grid['starts'], grid['counts'], grid['cell_particles'], + starts, + self._scalar(bounds['xmin'], src), + self._scalar(bounds['ymin'], src), + self._scalar(bounds['zmin'], src), + self._scalar(self.cell_size, src), + np.int32(bounds['nx']), np.int32(bounds['ny']), + np.int32(bounds['nz']), np.int32(bounds['ncells']), + np.int32(self.dim), radius_scale, neighbors + ], + device=self.device, + ) + wp.synchronize_device(self.device) + return { + 'lengths_dev': lengths, + 'starts_dev': starts, + 'neighbors_dev': neighbors, + 'lengths': lengths_cpu, + 'total_neighbors': total, + } + + def compute_neighbor_sum(self, src_index, dst_index, prop): + """Sum a scalar source property over neighbors on the device. + + This is a minimal equation-like consumer for the device-resident + neighbor cache. It returns one Warp array with a value per destination + particle and does not materialize per-particle neighbors on the host. + """ + src_pa = self.particles[src_index] + dst = self.particles[dst_index].gpu + if prop not in src_pa.properties: + raise KeyError("Unknown source particle property: %s" % prop) + if src_pa.stride.get(prop, 1) != 1: + raise ValueError( + "compute_neighbor_sum only supports scalar properties" + ) + + src_pa.gpu.push(prop) + values = src_pa.gpu.get_device_array(prop) + cache = self.build_neighbor_cache_gpu(src_index, dst_index) + ndst = dst.get_number_of_particles() + + if values.dtype == np.float32: + kernel = _neighbor_sum_f32 + out = wp.empty(ndst, dtype=wp.float32, device=self.device) + elif values.dtype == np.float64: + kernel = _neighbor_sum_f64 + out = wp.empty(ndst, dtype=wp.float64, device=self.device) + else: + raise TypeError( + "compute_neighbor_sum only supports float properties" + ) + + if ndst > 0: + wp.launch( + kernel, + dim=ndst, + inputs=[ + values.dev, cache['starts_dev'], cache['lengths_dev'], + cache['neighbors_dev'], out + ], + device=self.device, + ) + wp.synchronize_device(self.device) + return out + + def _build_cache(self, src_index, dst_index): + device_cache = self.build_neighbor_cache_gpu(src_index, dst_index) + starts = device_cache['starts_dev'] + neighbors = device_cache['neighbors_dev'] + starts_cpu = ( + starts.numpy() if len(device_cache['lengths']) > 0 + else np.array([], np.int32) + ) + total = device_cache['total_neighbors'] + neighbors_cpu = ( + neighbors.numpy() if total > 0 else np.array([], dtype=np.uint32) + ) + cache = { + 'lengths': device_cache['lengths'], + 'starts': starts_cpu, + 'neighbors': neighbors_cpu, + } + self._cache[(src_index, dst_index)] = cache + return cache diff --git a/pysph/base/warp_sph.py b/pysph/base/warp_sph.py new file mode 100644 index 000000000..2c137961f --- /dev/null +++ b/pysph/base/warp_sph.py @@ -0,0 +1,3432 @@ +"""Small Warp SPH equation kernels used by the GPU migration prototype.""" + +import numpy as np + +try: + import warp as wp +except ImportError: # pragma: no cover + wp = None + +from pysph.base.warp_device_helper import WarpDeviceHelper +from pysph.base.warp_codegen import WarpEquation, build_group_kernel + + +if wp is not None: + @wp.func + def _cubic_spline_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(2.0) / wp.float64(3.0) + if dim == wp.int32(2): + fac = wp.float64(10.0) / ( + wp.float64(7.0) * wp.float64(3.141592653589793) + ) + elif dim == wp.int32(3): + fac = wp.float64(1.0) / wp.float64(3.141592653589793) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + tmp = wp.float64(2.0) - q + if q > wp.float64(2.0): + val = wp.float64(0.0) + elif q > wp.float64(1.0): + val = wp.float64(0.25) * tmp * tmp * tmp + else: + val = ( + wp.float64(1.0) - + wp.float64(1.5) * q * q * + (wp.float64(1.0) - wp.float64(0.5) * q) + ) + return val * fac + + + @wp.func + def _cubic_spline_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(2.0) / wp.float32(3.0) + if dim == wp.int32(2): + fac = wp.float32(10.0) / ( + wp.float32(7.0) * wp.float32(3.141592653589793) + ) + elif dim == wp.int32(3): + fac = wp.float32(1.0) / wp.float32(3.141592653589793) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + tmp = wp.float32(2.0) - q + if q > wp.float32(2.0): + val = wp.float32(0.0) + elif q > wp.float32(1.0): + val = wp.float32(0.25) * tmp * tmp * tmp + else: + val = ( + wp.float32(1.0) - + wp.float32(1.5) * q * q * + (wp.float32(1.0) - wp.float32(0.5) * q) + ) + return val * fac + + + @wp.func + def _cubic_dwdq_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(2.0) / wp.float64(3.0) + if dim == wp.int32(2): + fac = wp.float64(10.0) / ( + wp.float64(7.0) * wp.float64(3.141592653589793) + ) + elif dim == wp.int32(3): + fac = wp.float64(1.0) / wp.float64(3.141592653589793) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + tmp = wp.float64(2.0) - q + if rij > wp.float64(1.0e-12): + if q > wp.float64(2.0): + val = wp.float64(0.0) + elif q > wp.float64(1.0): + val = -wp.float64(0.75) * tmp * tmp + else: + val = ( + -wp.float64(3.0) * q * + (wp.float64(1.0) - wp.float64(0.75) * q) + ) + return val * fac + + + @wp.func + def _cubic_dwdq_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(2.0) / wp.float32(3.0) + if dim == wp.int32(2): + fac = wp.float32(10.0) / ( + wp.float32(7.0) * wp.float32(3.141592653589793) + ) + elif dim == wp.int32(3): + fac = wp.float32(1.0) / wp.float32(3.141592653589793) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + tmp = wp.float32(2.0) - q + if rij > wp.float32(1.0e-12): + if q > wp.float32(2.0): + val = wp.float32(0.0) + elif q > wp.float32(1.0): + val = -wp.float32(0.75) * tmp * tmp + else: + val = ( + -wp.float32(3.0) * q * + (wp.float32(1.0) - wp.float32(0.75) * q) + ) + return val * fac + + + @wp.func + def _gaussian_spline_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(0.5641895835477563) + if dim == wp.int32(2): + fac = fac * wp.float64(0.5641895835477563) + elif dim == wp.int32(3): + fac = fac * wp.float64(0.5641895835477563) * wp.float64(0.5641895835477563) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + if q < wp.float64(3.0): + val = wp.exp(-q*q) + return val * fac + + + @wp.func + def _gaussian_spline_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(0.5641895835477563) + if dim == wp.int32(2): + fac = fac * wp.float32(0.5641895835477563) + elif dim == wp.int32(3): + fac = fac * wp.float32(0.5641895835477563) * wp.float32(0.5641895835477563) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + if q < wp.float32(3.0): + val = wp.exp(-q*q) + return val * fac + + + @wp.func + def _gaussian_dwdq_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(0.5641895835477563) + if dim == wp.int32(2): + fac = fac * wp.float64(0.5641895835477563) + elif dim == wp.int32(3): + fac = fac * wp.float64(0.5641895835477563) * wp.float64(0.5641895835477563) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + if rij > wp.float64(1.0e-12) and q < wp.float64(3.0): + val = -wp.float64(2.0) * q * wp.exp(-q*q) + return val * fac + + + @wp.func + def _gaussian_dwdq_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(0.5641895835477563) + if dim == wp.int32(2): + fac = fac * wp.float32(0.5641895835477563) + elif dim == wp.int32(3): + fac = fac * wp.float32(0.5641895835477563) * wp.float32(0.5641895835477563) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + if rij > wp.float32(1.0e-12) and q < wp.float32(3.0): + val = -wp.float32(2.0) * q * wp.exp(-q*q) + return val * fac + + + @wp.func + def _wendland_quintic_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + # Wendland C2 quintic (PySPH WendlandQuintic), support q < 2. + # alpha_d = 7/(4 pi) (2D), 21/(16 pi) (3D); dim==1 is unsupported in + # PySPH and never used here (left as a harmless 2D-base fallback). + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(7.0) / ( + wp.float64(4.0) * wp.float64(3.141592653589793) + ) + if dim == wp.int32(3): + fac = wp.float64(21.0) / ( + wp.float64(16.0) * wp.float64(3.141592653589793) + ) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + tmp = wp.float64(1.0) - wp.float64(0.5) * q + if q < wp.float64(2.0): + val = tmp * tmp * tmp * tmp * (wp.float64(2.0) * q + wp.float64(1.0)) + return val * fac + + + @wp.func + def _wendland_quintic_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(7.0) / ( + wp.float32(4.0) * wp.float32(3.141592653589793) + ) + if dim == wp.int32(3): + fac = wp.float32(21.0) / ( + wp.float32(16.0) * wp.float32(3.141592653589793) + ) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + tmp = wp.float32(1.0) - wp.float32(0.5) * q + if q < wp.float32(2.0): + val = tmp * tmp * tmp * tmp * (wp.float32(2.0) * q + wp.float32(1.0)) + return val * fac + + + @wp.func + def _wendland_dwdq_f64(rij: wp.float64, h: wp.float64, dim: wp.int32): + h1 = wp.float64(1.0) / h + q = rij * h1 + fac = wp.float64(7.0) / ( + wp.float64(4.0) * wp.float64(3.141592653589793) + ) + if dim == wp.int32(3): + fac = wp.float64(21.0) / ( + wp.float64(16.0) * wp.float64(3.141592653589793) + ) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float64(0.0) + tmp = wp.float64(1.0) - wp.float64(0.5) * q + if rij > wp.float64(1.0e-12) and q < wp.float64(2.0): + val = -wp.float64(5.0) * q * tmp * tmp * tmp + return val * fac + + + @wp.func + def _wendland_dwdq_f32(rij: wp.float32, h: wp.float32, dim: wp.int32): + h1 = wp.float32(1.0) / h + q = rij * h1 + fac = wp.float32(7.0) / ( + wp.float32(4.0) * wp.float32(3.141592653589793) + ) + if dim == wp.int32(3): + fac = wp.float32(21.0) / ( + wp.float32(16.0) * wp.float32(3.141592653589793) + ) + + if dim == wp.int32(1): + fac = fac * h1 + elif dim == wp.int32(2): + fac = fac * h1 * h1 + else: + fac = fac * h1 * h1 * h1 + + val = wp.float32(0.0) + tmp = wp.float32(1.0) - wp.float32(0.5) * q + if rij > wp.float32(1.0e-12) and q < wp.float32(2.0): + val = -wp.float32(5.0) * q * tmp * tmp * tmp + return val * fac + + + @wp.func + def _kernel_value_f64( + rij: wp.float64, h: wp.float64, dim: wp.int32, + kernel_id: wp.int32, + ): + if kernel_id == wp.int32(1): + return _gaussian_spline_f64(rij, h, dim) + if kernel_id == wp.int32(2): + return _wendland_quintic_f64(rij, h, dim) + return _cubic_spline_f64(rij, h, dim) + + + @wp.func + def _kernel_value_f32( + rij: wp.float32, h: wp.float32, dim: wp.int32, + kernel_id: wp.int32, + ): + if kernel_id == wp.int32(1): + return _gaussian_spline_f32(rij, h, dim) + if kernel_id == wp.int32(2): + return _wendland_quintic_f32(rij, h, dim) + return _cubic_spline_f32(rij, h, dim) + + + @wp.func + def _kernel_dwdq_f64( + rij: wp.float64, h: wp.float64, dim: wp.int32, + kernel_id: wp.int32, + ): + if kernel_id == wp.int32(1): + return _gaussian_dwdq_f64(rij, h, dim) + if kernel_id == wp.int32(2): + return _wendland_dwdq_f64(rij, h, dim) + return _cubic_dwdq_f64(rij, h, dim) + + + @wp.func + def _kernel_dwdq_f32( + rij: wp.float32, h: wp.float32, dim: wp.int32, + kernel_id: wp.int32, + ): + if kernel_id == wp.int32(1): + return _gaussian_dwdq_f32(rij, h, dim) + if kernel_id == wp.int32(2): + return _wendland_dwdq_f32(rij, h, dim) + return _cubic_dwdq_f32(rij, h, dim) + + + @wp.kernel + def _isothermal_eos_f64( + rho: wp.array(dtype=wp.float64), + p: wp.array(dtype=wp.float64), + rho0: wp.float64, + c02: wp.float64, + p0: wp.float64, + ): + i = wp.tid() + p[i] = p0 + c02 * (rho[i] - rho0) + + + @wp.kernel + def _isothermal_eos_f32( + rho: wp.array(dtype=wp.float32), + p: wp.array(dtype=wp.float32), + rho0: wp.float32, + c02: wp.float32, + p0: wp.float32, + ): + i = wp.tid() + p[i] = p0 + c02 * (rho[i] - rho0) + + + @wp.kernel + def _tait_eos_f64( + rho: wp.array(dtype=wp.float64), + p: wp.array(dtype=wp.float64), + cs: wp.array(dtype=wp.float64), + rho0: wp.float64, + rho01: wp.float64, + c0: wp.float64, + gamma: wp.float64, + gamma1: wp.float64, + b: wp.float64, + p0: wp.float64, + ): + i = wp.tid() + ratio = rho[i] * rho01 + tmp = wp.pow(ratio, gamma) + p[i] = p0 + b * (tmp - wp.float64(1.0)) + cs[i] = c0 * wp.pow(ratio, gamma1) + + + @wp.kernel + def _tait_eos_f32( + rho: wp.array(dtype=wp.float32), + p: wp.array(dtype=wp.float32), + cs: wp.array(dtype=wp.float32), + rho0: wp.float32, + rho01: wp.float32, + c0: wp.float32, + gamma: wp.float32, + gamma1: wp.float32, + b: wp.float32, + p0: wp.float32, + ): + i = wp.tid() + ratio = rho[i] * rho01 + tmp = wp.pow(ratio, gamma) + p[i] = p0 + b * (tmp - wp.float32(1.0)) + cs[i] = c0 * wp.pow(ratio, gamma1) + + + @wp.kernel + def _tait_eos_hg_correction_f64( + rho: wp.array(dtype=wp.float64), + p: wp.array(dtype=wp.float64), + cs: wp.array(dtype=wp.float64), + rho0: wp.float64, + rho01: wp.float64, + c0: wp.float64, + gamma: wp.float64, + gamma1: wp.float64, + b: wp.float64, + ): + # PySPH TaitEOSHGCorrection: clamp wall density to >= rho0 (so wall + # pressure is always >= 0 and repels), then standard Tait. + i = wp.tid() + if rho[i] < rho0: + rho[i] = rho0 + ratio = rho[i] * rho01 + tmp = wp.pow(ratio, gamma) + p[i] = b * (tmp - wp.float64(1.0)) + cs[i] = c0 * wp.pow(ratio, gamma1) + + + @wp.kernel + def _tait_eos_hg_correction_f32( + rho: wp.array(dtype=wp.float32), + p: wp.array(dtype=wp.float32), + cs: wp.array(dtype=wp.float32), + rho0: wp.float32, + rho01: wp.float32, + c0: wp.float32, + gamma: wp.float32, + gamma1: wp.float32, + b: wp.float32, + ): + i = wp.tid() + if rho[i] < rho0: + rho[i] = rho0 + ratio = rho[i] * rho01 + tmp = wp.pow(ratio, gamma) + p[i] = b * (tmp - wp.float32(1.0)) + cs[i] = c0 * wp.pow(ratio, gamma1) + + + @wp.kernel + def _euler_step_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + au: wp.array(dtype=wp.float64), + av: wp.array(dtype=wp.float64), + aw: wp.array(dtype=wp.float64), + dt: wp.float64, + dim: wp.int32, + ): + i = wp.tid() + u[i] = u[i] + dt * au[i] + v[i] = v[i] + dt * av[i] + w[i] = w[i] + dt * aw[i] + x[i] = x[i] + dt * u[i] + if dim > wp.int32(1): + y[i] = y[i] + dt * v[i] + if dim > wp.int32(2): + z[i] = z[i] + dt * w[i] + + + @wp.kernel + def _euler_step_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + au: wp.array(dtype=wp.float32), + av: wp.array(dtype=wp.float32), + aw: wp.array(dtype=wp.float32), + dt: wp.float32, + dim: wp.int32, + ): + i = wp.tid() + u[i] = u[i] + dt * au[i] + v[i] = v[i] + dt * av[i] + w[i] = w[i] + dt * aw[i] + x[i] = x[i] + dt * u[i] + if dim > wp.int32(1): + y[i] = y[i] + dt * v[i] + if dim > wp.int32(2): + z[i] = z[i] + dt * w[i] + + + @wp.kernel + def _leapfrog_kick_f64( + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + au: wp.array(dtype=wp.float64), + av: wp.array(dtype=wp.float64), + aw: wp.array(dtype=wp.float64), + dt: wp.float64, + dim: wp.int32, + ): + i = wp.tid() + u[i] = u[i] + dt * au[i] + if dim > wp.int32(1): + v[i] = v[i] + dt * av[i] + if dim > wp.int32(2): + w[i] = w[i] + dt * aw[i] + + + @wp.kernel + def _leapfrog_kick_f32( + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + au: wp.array(dtype=wp.float32), + av: wp.array(dtype=wp.float32), + aw: wp.array(dtype=wp.float32), + dt: wp.float32, + dim: wp.int32, + ): + i = wp.tid() + u[i] = u[i] + dt * au[i] + if dim > wp.int32(1): + v[i] = v[i] + dt * av[i] + if dim > wp.int32(2): + w[i] = w[i] + dt * aw[i] + + + @wp.kernel + def _apply_body_force_f64( + au: wp.array(dtype=wp.float64), + av: wp.array(dtype=wp.float64), + aw: wp.array(dtype=wp.float64), + gx: wp.float64, + gy: wp.float64, + gz: wp.float64, + dim: wp.int32, + ): + i = wp.tid() + au[i] = au[i] + gx + if dim > wp.int32(1): + av[i] = av[i] + gy + if dim > wp.int32(2): + aw[i] = aw[i] + gz + + + @wp.kernel + def _apply_body_force_f32( + au: wp.array(dtype=wp.float32), + av: wp.array(dtype=wp.float32), + aw: wp.array(dtype=wp.float32), + gx: wp.float32, + gy: wp.float32, + gz: wp.float32, + dim: wp.int32, + ): + i = wp.tid() + au[i] = au[i] + gx + if dim > wp.int32(1): + av[i] = av[i] + gy + if dim > wp.int32(2): + aw[i] = aw[i] + gz + + + @wp.kernel + def _leapfrog_drift_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + dt: wp.float64, + dim: wp.int32, + ): + i = wp.tid() + x[i] = x[i] + dt * u[i] + if dim > wp.int32(1): + y[i] = y[i] + dt * v[i] + if dim > wp.int32(2): + z[i] = z[i] + dt * w[i] + + + @wp.kernel + def _leapfrog_drift_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + dt: wp.float32, + dim: wp.int32, + ): + i = wp.tid() + x[i] = x[i] + dt * u[i] + if dim > wp.int32(1): + y[i] = y[i] + dt * v[i] + if dim > wp.int32(2): + z[i] = z[i] + dt * w[i] + + + @wp.kernel + def _leapfrog_drift_xsph_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + ax: wp.array(dtype=wp.float64), + ay: wp.array(dtype=wp.float64), + az: wp.array(dtype=wp.float64), + dt: wp.float64, + dim: wp.int32, + ): + i = wp.tid() + x[i] = x[i] + dt * (u[i] + ax[i]) + if dim > wp.int32(1): + y[i] = y[i] + dt * (v[i] + ay[i]) + if dim > wp.int32(2): + z[i] = z[i] + dt * (w[i] + az[i]) + + + @wp.kernel + def _leapfrog_drift_xsph_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + ax: wp.array(dtype=wp.float32), + ay: wp.array(dtype=wp.float32), + az: wp.array(dtype=wp.float32), + dt: wp.float32, + dim: wp.int32, + ): + i = wp.tid() + x[i] = x[i] + dt * (u[i] + ax[i]) + if dim > wp.int32(1): + y[i] = y[i] + dt * (v[i] + ay[i]) + if dim > wp.int32(2): + z[i] = z[i] + dt * (w[i] + az[i]) + + + @wp.kernel + def _wcsph_save_state_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + rho: wp.array(dtype=wp.float64), + x0: wp.array(dtype=wp.float64), + y0: wp.array(dtype=wp.float64), + z0: wp.array(dtype=wp.float64), + u0: wp.array(dtype=wp.float64), + v0: wp.array(dtype=wp.float64), + w0: wp.array(dtype=wp.float64), + rho0: wp.array(dtype=wp.float64), + dim: wp.int32, + ): + i = wp.tid() + x0[i] = x[i] + u0[i] = u[i] + rho0[i] = rho[i] + if dim > wp.int32(1): + y0[i] = y[i] + v0[i] = v[i] + if dim > wp.int32(2): + z0[i] = z[i] + w0[i] = w[i] + + + @wp.kernel + def _wcsph_save_state_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + rho: wp.array(dtype=wp.float32), + x0: wp.array(dtype=wp.float32), + y0: wp.array(dtype=wp.float32), + z0: wp.array(dtype=wp.float32), + u0: wp.array(dtype=wp.float32), + v0: wp.array(dtype=wp.float32), + w0: wp.array(dtype=wp.float32), + rho0: wp.array(dtype=wp.float32), + dim: wp.int32, + ): + i = wp.tid() + x0[i] = x[i] + u0[i] = u[i] + rho0[i] = rho[i] + if dim > wp.int32(1): + y0[i] = y[i] + v0[i] = v[i] + if dim > wp.int32(2): + z0[i] = z[i] + w0[i] = w[i] + + + @wp.kernel + def _wcsph_pec_stage_f64( + x0: wp.array(dtype=wp.float64), + y0: wp.array(dtype=wp.float64), + z0: wp.array(dtype=wp.float64), + u0: wp.array(dtype=wp.float64), + v0: wp.array(dtype=wp.float64), + w0: wp.array(dtype=wp.float64), + rho0: wp.array(dtype=wp.float64), + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + rho: wp.array(dtype=wp.float64), + au: wp.array(dtype=wp.float64), + av: wp.array(dtype=wp.float64), + aw: wp.array(dtype=wp.float64), + ax: wp.array(dtype=wp.float64), + ay: wp.array(dtype=wp.float64), + az: wp.array(dtype=wp.float64), + arho: wp.array(dtype=wp.float64), + dt_factor: wp.float64, + dim: wp.int32, + use_xsph: wp.int32, + ): + i = wp.tid() + adv_x = u[i] + adv_y = v[i] + adv_z = w[i] + if use_xsph: + adv_x = adv_x + ax[i] + adv_y = adv_y + ay[i] + adv_z = adv_z + az[i] + u[i] = u0[i] + dt_factor * au[i] + rho[i] = rho0[i] + dt_factor * arho[i] + x[i] = x0[i] + dt_factor * adv_x + if dim > wp.int32(1): + v[i] = v0[i] + dt_factor * av[i] + y[i] = y0[i] + dt_factor * adv_y + if dim > wp.int32(2): + w[i] = w0[i] + dt_factor * aw[i] + z[i] = z0[i] + dt_factor * adv_z + + + @wp.kernel + def _wcsph_pec_stage_f32( + x0: wp.array(dtype=wp.float32), + y0: wp.array(dtype=wp.float32), + z0: wp.array(dtype=wp.float32), + u0: wp.array(dtype=wp.float32), + v0: wp.array(dtype=wp.float32), + w0: wp.array(dtype=wp.float32), + rho0: wp.array(dtype=wp.float32), + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + rho: wp.array(dtype=wp.float32), + au: wp.array(dtype=wp.float32), + av: wp.array(dtype=wp.float32), + aw: wp.array(dtype=wp.float32), + ax: wp.array(dtype=wp.float32), + ay: wp.array(dtype=wp.float32), + az: wp.array(dtype=wp.float32), + arho: wp.array(dtype=wp.float32), + dt_factor: wp.float32, + dim: wp.int32, + use_xsph: wp.int32, + ): + i = wp.tid() + adv_x = u[i] + adv_y = v[i] + adv_z = w[i] + if use_xsph: + adv_x = adv_x + ax[i] + adv_y = adv_y + ay[i] + adv_z = adv_z + az[i] + u[i] = u0[i] + dt_factor * au[i] + rho[i] = rho0[i] + dt_factor * arho[i] + x[i] = x0[i] + dt_factor * adv_x + if dim > wp.int32(1): + v[i] = v0[i] + dt_factor * av[i] + y[i] = y0[i] + dt_factor * adv_y + if dim > wp.int32(2): + w[i] = w0[i] + dt_factor * aw[i] + z[i] = z0[i] + dt_factor * adv_z + + + @wp.kernel + def _wcsph_dt_init_f64( + max_cfl: wp.array(dtype=wp.float64), + max_force: wp.array(dtype=wp.float64), + min_h: wp.array(dtype=wp.float64), + out_dt: wp.array(dtype=wp.float64), + ): + max_cfl[0] = wp.float64(0.0) + max_force[0] = wp.float64(0.0) + min_h[0] = wp.float64(1.0e30) + out_dt[0] = wp.float64(0.0) + + + @wp.kernel + def _wcsph_dt_init_f32( + max_cfl: wp.array(dtype=wp.float32), + max_force: wp.array(dtype=wp.float32), + min_h: wp.array(dtype=wp.float32), + out_dt: wp.array(dtype=wp.float32), + ): + max_cfl[0] = wp.float32(0.0) + max_force[0] = wp.float32(0.0) + min_h[0] = wp.float32(1.0e30) + out_dt[0] = wp.float32(0.0) + + + @wp.kernel + def _wcsph_dt_reduce_f64( + h: wp.array(dtype=wp.float64), + dt_cfl: wp.array(dtype=wp.float64), + dt_force: wp.array(dtype=wp.float64), + max_cfl: wp.array(dtype=wp.float64), + max_force: wp.array(dtype=wp.float64), + min_h: wp.array(dtype=wp.float64), + ): + i = wp.tid() + wp.atomic_max(max_cfl, 0, dt_cfl[i]) + wp.atomic_max(max_force, 0, dt_force[i]) + wp.atomic_min(min_h, 0, h[i]) + + + @wp.kernel + def _wcsph_dt_reduce_f32( + h: wp.array(dtype=wp.float32), + dt_cfl: wp.array(dtype=wp.float32), + dt_force: wp.array(dtype=wp.float32), + max_cfl: wp.array(dtype=wp.float32), + max_force: wp.array(dtype=wp.float32), + min_h: wp.array(dtype=wp.float32), + ): + i = wp.tid() + wp.atomic_max(max_cfl, 0, dt_cfl[i]) + wp.atomic_max(max_force, 0, dt_force[i]) + wp.atomic_min(min_h, 0, h[i]) + + + @wp.kernel + def _wcsph_dt_finalize_f64( + max_cfl: wp.array(dtype=wp.float64), + max_force: wp.array(dtype=wp.float64), + min_h: wp.array(dtype=wp.float64), + cfl: wp.float64, + dt_min: wp.float64, + dt_max: wp.float64, + out_dt: wp.array(dtype=wp.float64), + ): + dt = dt_max + if max_cfl[0] > wp.float64(0.0): + dt = wp.min(dt, cfl * min_h[0] / max_cfl[0]) + if max_force[0] > wp.float64(0.0): + dt_force = wp.sqrt(min_h[0] / wp.sqrt(max_force[0])) + dt = wp.min(dt, cfl * dt_force) + dt = wp.max(dt, dt_min) + dt = wp.min(dt, dt_max) + out_dt[0] = dt + + + @wp.kernel + def _wcsph_dt_finalize_f32( + max_cfl: wp.array(dtype=wp.float32), + max_force: wp.array(dtype=wp.float32), + min_h: wp.array(dtype=wp.float32), + cfl: wp.float32, + dt_min: wp.float32, + dt_max: wp.float32, + out_dt: wp.array(dtype=wp.float32), + ): + dt = dt_max + if max_cfl[0] > wp.float32(0.0): + dt = wp.min(dt, cfl * min_h[0] / max_cfl[0]) + if max_force[0] > wp.float32(0.0): + dt_force = wp.sqrt(min_h[0] / wp.sqrt(max_force[0])) + dt = wp.min(dt, cfl * dt_force) + dt = wp.max(dt, dt_min) + dt = wp.min(dt, dt_max) + out_dt[0] = dt + + + @wp.func + def _wrap_value_f64(value: wp.float64, lower: wp.float64, + upper: wp.float64): + length = upper - lower + result = value + if length > wp.float64(0.0): + offset = result - lower + result = lower + offset - wp.floor(offset / length) * length + return result + + + @wp.func + def _wrap_value_f32(value: wp.float32, lower: wp.float32, + upper: wp.float32): + length = upper - lower + result = value + if length > wp.float32(0.0): + offset = result - lower + result = lower + offset - wp.floor(offset / length) * length + return result + + + @wp.kernel + def _wrap_periodic_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + xmin: wp.float64, + xmax: wp.float64, + ymin: wp.float64, + ymax: wp.float64, + zmin: wp.float64, + zmax: wp.float64, + periodic_x: wp.int32, + periodic_y: wp.int32, + periodic_z: wp.int32, + dim: wp.int32, + ): + i = wp.tid() + if periodic_x: + x[i] = _wrap_value_f64(x[i], xmin, xmax) + if dim > wp.int32(1) and periodic_y: + y[i] = _wrap_value_f64(y[i], ymin, ymax) + if dim > wp.int32(2) and periodic_z: + z[i] = _wrap_value_f64(z[i], zmin, zmax) + + + @wp.kernel + def _wrap_periodic_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + xmin: wp.float32, + xmax: wp.float32, + ymin: wp.float32, + ymax: wp.float32, + zmin: wp.float32, + zmax: wp.float32, + periodic_x: wp.int32, + periodic_y: wp.int32, + periodic_z: wp.int32, + dim: wp.int32, + ): + i = wp.tid() + if periodic_x: + x[i] = _wrap_value_f32(x[i], xmin, xmax) + if dim > wp.int32(1) and periodic_y: + y[i] = _wrap_value_f32(y[i], ymin, ymax) + if dim > wp.int32(2) and periodic_z: + z[i] = _wrap_value_f32(z[i], zmin, zmax) + + + @wp.kernel + def _rigid_moments_reduce_f64( + body_id: wp.array(dtype=wp.int32), + m: wp.array(dtype=wp.float64), + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + fx: wp.array(dtype=wp.float64), + fy: wp.array(dtype=wp.float64), + fz: wp.array(dtype=wp.float64), + mi: wp.array(dtype=wp.float64), + ): + # ADR-0006: per-body SUM-reduction matching RigidBodyMoments.reduce + # (rigid_body.py:90-122) -- 16 slots per body: total mass, m*x/y/z (for + # COM), 6 second-moments about the ORIGIN, total force, torque about the + # origin. The device finalize shifts to the COM (the host helper is the + # validation oracle). Accumulators are f64 even + # on the f32 path (P0: fp32 atomic_add is non-associative); ``mi`` is + # pre-zeroed by wp.zeros. + i = wp.tid() + b = body_id[i] * wp.int32(16) + mm = m[i] + xi = x[i] + yi = y[i] + zi = z[i] + fxi = fx[i] + fyi = fy[i] + fzi = fz[i] + wp.atomic_add(mi, b + 0, mm) + wp.atomic_add(mi, b + 1, mm * xi) + wp.atomic_add(mi, b + 2, mm * yi) + wp.atomic_add(mi, b + 3, mm * zi) + wp.atomic_add(mi, b + 4, mm * (yi * yi + zi * zi)) + wp.atomic_add(mi, b + 5, mm * (xi * xi + zi * zi)) + wp.atomic_add(mi, b + 6, mm * (xi * xi + yi * yi)) + wp.atomic_add(mi, b + 7, -mm * xi * yi) + wp.atomic_add(mi, b + 8, -mm * xi * zi) + wp.atomic_add(mi, b + 9, -mm * yi * zi) + wp.atomic_add(mi, b + 10, fxi) + wp.atomic_add(mi, b + 11, fyi) + wp.atomic_add(mi, b + 12, fzi) + wp.atomic_add(mi, b + 13, yi * fzi - zi * fyi) + wp.atomic_add(mi, b + 14, zi * fxi - xi * fzi) + wp.atomic_add(mi, b + 15, xi * fyi - yi * fxi) + + + @wp.kernel + def _rigid_moments_reduce_f32( + body_id: wp.array(dtype=wp.int32), + m: wp.array(dtype=wp.float32), + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + fx: wp.array(dtype=wp.float32), + fy: wp.array(dtype=wp.float32), + fz: wp.array(dtype=wp.float32), + mi: wp.array(dtype=wp.float64), + ): + # f32 particle data, but cast to f64 and accumulate in f64 (the locked + # decision -- see _rigid_moments_reduce_f64). ``mi`` is f64. + i = wp.tid() + b = body_id[i] * wp.int32(16) + mm = wp.float64(m[i]) + xi = wp.float64(x[i]) + yi = wp.float64(y[i]) + zi = wp.float64(z[i]) + fxi = wp.float64(fx[i]) + fyi = wp.float64(fy[i]) + fzi = wp.float64(fz[i]) + wp.atomic_add(mi, b + 0, mm) + wp.atomic_add(mi, b + 1, mm * xi) + wp.atomic_add(mi, b + 2, mm * yi) + wp.atomic_add(mi, b + 3, mm * zi) + wp.atomic_add(mi, b + 4, mm * (yi * yi + zi * zi)) + wp.atomic_add(mi, b + 5, mm * (xi * xi + zi * zi)) + wp.atomic_add(mi, b + 6, mm * (xi * xi + yi * yi)) + wp.atomic_add(mi, b + 7, -mm * xi * yi) + wp.atomic_add(mi, b + 8, -mm * xi * zi) + wp.atomic_add(mi, b + 9, -mm * yi * zi) + wp.atomic_add(mi, b + 10, fxi) + wp.atomic_add(mi, b + 11, fyi) + wp.atomic_add(mi, b + 12, fzi) + wp.atomic_add(mi, b + 13, yi * fzi - zi * fyi) + wp.atomic_add(mi, b + 14, zi * fxi - xi * fzi) + wp.atomic_add(mi, b + 15, xi * fyi - yi * fxi) + + + @wp.kernel + def _rigid_finalize_device( + mi: wp.array(dtype=wp.float64), + omega: wp.array(dtype=wp.float64), + total_mass: wp.array(dtype=wp.float64), + cm: wp.array(dtype=wp.float64), + inertia: wp.array(dtype=wp.float64), + force: wp.array(dtype=wp.float64), + ac: wp.array(dtype=wp.float64), + torque: wp.array(dtype=wp.float64), + omega_dot: wp.array(dtype=wp.float64), + error: wp.array(dtype=wp.int32), + ): + """Finalize one reduced rigid body and solve its angular acceleration.""" + body = wp.tid() + base16 = body * wp.int32(16) + base3 = body * wp.int32(3) + base9 = body * wp.int32(9) + mass = mi[base16 + 0] + error[body] = wp.int32(0) + if mass <= wp.float64(0.0): + error[body] = wp.int32(1) + total_mass[body] = mass + cm[base3 + 0] = wp.float64(0.0) + cm[base3 + 1] = wp.float64(0.0) + cm[base3 + 2] = wp.float64(0.0) + force[base3 + 0] = wp.float64(0.0) + force[base3 + 1] = wp.float64(0.0) + force[base3 + 2] = wp.float64(0.0) + ac[base3 + 0] = wp.float64(0.0) + ac[base3 + 1] = wp.float64(0.0) + ac[base3 + 2] = wp.float64(0.0) + torque[base3 + 0] = wp.float64(0.0) + torque[base3 + 1] = wp.float64(0.0) + torque[base3 + 2] = wp.float64(0.0) + omega_dot[base3 + 0] = wp.float64(0.0) + omega_dot[base3 + 1] = wp.float64(0.0) + omega_dot[base3 + 2] = wp.float64(0.0) + else: + cx = mi[base16 + 1] / mass + cy = mi[base16 + 2] / mass + cz = mi[base16 + 3] / mass + ixx = mi[base16 + 4] - (cy * cy + cz * cz) * mass + iyy = mi[base16 + 5] - (cx * cx + cz * cz) * mass + izz = mi[base16 + 6] - (cx * cx + cy * cy) * mass + ixy = mi[base16 + 7] + cx * cy * mass + ixz = mi[base16 + 8] + cx * cz * mass + iyz = mi[base16 + 9] + cy * cz * mass + + fx = mi[base16 + 10] + fy = mi[base16 + 11] + fz = mi[base16 + 12] + tx = mi[base16 + 13] - (cy * fz - cz * fy) + ty = mi[base16 + 14] - (-cx * fz + cz * fx) + tz = mi[base16 + 15] - (cx * fy - cy * fx) + + total_mass[body] = mass + cm[base3 + 0] = cx + cm[base3 + 1] = cy + cm[base3 + 2] = cz + inertia[base9 + 0] = ixx + inertia[base9 + 1] = ixy + inertia[base9 + 2] = ixz + inertia[base9 + 3] = ixy + inertia[base9 + 4] = iyy + inertia[base9 + 5] = iyz + inertia[base9 + 6] = ixz + inertia[base9 + 7] = iyz + inertia[base9 + 8] = izz + force[base3 + 0] = fx + force[base3 + 1] = fy + force[base3 + 2] = fz + ac[base3 + 0] = fx / mass + ac[base3 + 1] = fy / mass + ac[base3 + 2] = fz / mass + torque[base3 + 0] = tx + torque[base3 + 1] = ty + torque[base3 + 2] = tz + + wx = omega[base3 + 0] + wy = omega[base3 + 1] + wz = omega[base3 + 2] + iwx = ixx * wx + ixy * wy + ixz * wz + iwy = ixy * wx + iyy * wy + iyz * wz + iwz = ixz * wx + iyz * wy + izz * wz + rx = tx - (wy * iwz - wz * iwy) + ry = ty - (wz * iwx - wx * iwz) + rz = tz - (wx * iwy - wy * iwx) + + # Explicit inverse of the symmetric 3x3 inertia tensor. Keeping + # this compact solve on the device removes the P1 host barrier. + c00 = iyy * izz - iyz * iyz + c01 = ixz * iyz - ixy * izz + c02 = ixy * iyz - ixz * iyy + c11 = ixx * izz - ixz * ixz + c12 = ixy * ixz - ixx * iyz + c22 = ixx * iyy - ixy * ixy + det = ixx * c00 + ixy * c01 + ixz * c02 + if wp.abs(det) <= wp.float64(1.0e-30): + error[body] = wp.int32(2) + omega_dot[base3 + 0] = wp.float64(0.0) + omega_dot[base3 + 1] = wp.float64(0.0) + omega_dot[base3 + 2] = wp.float64(0.0) + else: + inv_det = wp.float64(1.0) / det + omega_dot[base3 + 0] = ( + c00 * rx + c01 * ry + c02 * rz) * inv_det + omega_dot[base3 + 1] = ( + c01 * rx + c11 * ry + c12 * rz) * inv_det + omega_dot[base3 + 2] = ( + c02 * rx + c12 * ry + c22 * rz) * inv_det + + + @wp.kernel + def _rigid_save_body_state( + vc: wp.array(dtype=wp.float64), + omega: wp.array(dtype=wp.float64), + vc0: wp.array(dtype=wp.float64), + omega0: wp.array(dtype=wp.float64), + ): + i = wp.tid() + vc0[i] = vc[i] + omega0[i] = omega[i] + + + @wp.kernel + def _rigid_update_body_state( + ac: wp.array(dtype=wp.float64), + omega_dot: wp.array(dtype=wp.float64), + vc0: wp.array(dtype=wp.float64), + omega0: wp.array(dtype=wp.float64), + vc: wp.array(dtype=wp.float64), + omega: wp.array(dtype=wp.float64), + dt_factor: wp.float64, + ): + i = wp.tid() + vc[i] = vc0[i] + dt_factor * ac[i] + omega[i] = omega0[i] + dt_factor * omega_dot[i] + + + @wp.kernel + def _rigid_save_particle_state_f64( + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + x0: wp.array(dtype=wp.float64), + y0: wp.array(dtype=wp.float64), + z0: wp.array(dtype=wp.float64), + ): + i = wp.tid() + x0[i] = x[i] + y0[i] = y[i] + z0[i] = z[i] + + + @wp.kernel + def _rigid_save_particle_state_f32( + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + x0: wp.array(dtype=wp.float32), + y0: wp.array(dtype=wp.float32), + z0: wp.array(dtype=wp.float32), + ): + i = wp.tid() + x0[i] = x[i] + y0[i] = y[i] + z0[i] = z[i] + + + @wp.kernel + def _rigid_motion_stage_f64( + body_id: wp.array(dtype=wp.int32), + cm: wp.array(dtype=wp.float64), + vc: wp.array(dtype=wp.float64), + omega: wp.array(dtype=wp.float64), + x0: wp.array(dtype=wp.float64), + y0: wp.array(dtype=wp.float64), + z0: wp.array(dtype=wp.float64), + x: wp.array(dtype=wp.float64), + y: wp.array(dtype=wp.float64), + z: wp.array(dtype=wp.float64), + u: wp.array(dtype=wp.float64), + v: wp.array(dtype=wp.float64), + w: wp.array(dtype=wp.float64), + dt_factor: wp.float64, + ): + i = wp.tid() + base = body_id[i] * wp.int32(3) + rx = x[i] - cm[base + 0] + ry = y[i] - cm[base + 1] + rz = z[i] - cm[base + 2] + wx = omega[base + 0] + wy = omega[base + 1] + wz = omega[base + 2] + ui = vc[base + 0] + wy * rz - wz * ry + vi = vc[base + 1] + wz * rx - wx * rz + wi = vc[base + 2] + wx * ry - wy * rx + u[i] = ui + v[i] = vi + w[i] = wi + x[i] = x0[i] + dt_factor * ui + y[i] = y0[i] + dt_factor * vi + z[i] = z0[i] + dt_factor * wi + + + @wp.kernel + def _rigid_motion_stage_f32( + body_id: wp.array(dtype=wp.int32), + cm: wp.array(dtype=wp.float64), + vc: wp.array(dtype=wp.float64), + omega: wp.array(dtype=wp.float64), + x0: wp.array(dtype=wp.float32), + y0: wp.array(dtype=wp.float32), + z0: wp.array(dtype=wp.float32), + x: wp.array(dtype=wp.float32), + y: wp.array(dtype=wp.float32), + z: wp.array(dtype=wp.float32), + u: wp.array(dtype=wp.float32), + v: wp.array(dtype=wp.float32), + w: wp.array(dtype=wp.float32), + dt_factor: wp.float64, + ): + i = wp.tid() + base = body_id[i] * wp.int32(3) + rx = wp.float64(x[i]) - cm[base + 0] + ry = wp.float64(y[i]) - cm[base + 1] + rz = wp.float64(z[i]) - cm[base + 2] + wx = omega[base + 0] + wy = omega[base + 1] + wz = omega[base + 2] + ui = vc[base + 0] + wy * rz - wz * ry + vi = vc[base + 1] + wz * rx - wx * rz + wi = vc[base + 2] + wx * ry - wy * rx + u[i] = wp.float32(ui) + v[i] = wp.float32(vi) + w[i] = wp.float32(wi) + x[i] = x0[i] + wp.float32(dt_factor * ui) + y[i] = y0[i] + wp.float32(dt_factor * vi) + z[i] = z0[i] + wp.float32(dt_factor * wi) + + + @wp.kernel + def _rigid_save_density_f64( + rho: wp.array(dtype=wp.float64), + rho0: wp.array(dtype=wp.float64), + ): + i = wp.tid() + rho0[i] = rho[i] + + + @wp.kernel + def _rigid_save_density_f32( + rho: wp.array(dtype=wp.float32), + rho0: wp.array(dtype=wp.float32), + ): + i = wp.tid() + rho0[i] = rho[i] + + + @wp.kernel + def _rigid_density_stage_f64( + rho0: wp.array(dtype=wp.float64), + arho: wp.array(dtype=wp.float64), + rho: wp.array(dtype=wp.float64), + dt_factor: wp.float64, + ): + i = wp.tid() + rho[i] = rho0[i] + dt_factor * arho[i] + + + @wp.kernel + def _rigid_density_stage_f32( + rho0: wp.array(dtype=wp.float32), + arho: wp.array(dtype=wp.float32), + rho: wp.array(dtype=wp.float32), + dt_factor: wp.float32, + ): + i = wp.tid() + rho[i] = rho0[i] + dt_factor * arho[i] + + + @wp.kernel + def _rigid_body_force_f64( + m: wp.array(dtype=wp.float64), + fx: wp.array(dtype=wp.float64), + fy: wp.array(dtype=wp.float64), + fz: wp.array(dtype=wp.float64), + gx: wp.float64, + gy: wp.float64, + gz: wp.float64, + ): + i = wp.tid() + fx[i] = m[i] * gx + fy[i] = m[i] * gy + fz[i] = m[i] * gz + + + @wp.kernel + def _rigid_body_force_f32( + m: wp.array(dtype=wp.float32), + fx: wp.array(dtype=wp.float32), + fy: wp.array(dtype=wp.float32), + fz: wp.array(dtype=wp.float32), + gx: wp.float32, + gy: wp.float32, + gz: wp.float32, + ): + i = wp.tid() + fx[i] = m[i] * gx + fy[i] = m[i] * gy + fz[i] = m[i] * gz + + +if wp is not None: + # Device wp.func objects referenced by generated group kernels. Seeded into + # the generated kernels' namespace so Warp can resolve them (ADR-0003). + _WARP_DEVICE_FUNCS = { + '_kernel_dwdq_f32': _kernel_dwdq_f32, + '_kernel_dwdq_f64': _kernel_dwdq_f64, + '_kernel_value_f32': _kernel_value_f32, + '_kernel_value_f64': _kernel_value_f64, + '_cubic_spline_f32': _cubic_spline_f32, + '_cubic_spline_f64': _cubic_spline_f64, + '_cubic_dwdq_f32': _cubic_dwdq_f32, + '_cubic_dwdq_f64': _cubic_dwdq_f64, + '_gaussian_spline_f32': _gaussian_spline_f32, + '_gaussian_spline_f64': _gaussian_spline_f64, + '_gaussian_dwdq_f32': _gaussian_dwdq_f32, + '_gaussian_dwdq_f64': _gaussian_dwdq_f64, + # Wendland leaves are reachable transitively via the routers' + # __globals__ (so kernel_id==2 already resolves), but are listed here + # for parity with cubic/gaussian and robustness against a future + # generated kernel that calls a leaf directly. Not part of the cache key + # or any generated source, so this does not perturb the 2D path. + '_wendland_quintic_f32': _wendland_quintic_f32, + '_wendland_quintic_f64': _wendland_quintic_f64, + '_wendland_dwdq_f32': _wendland_dwdq_f32, + '_wendland_dwdq_f64': _wendland_dwdq_f64, + } +else: # pragma: no cover + _WARP_DEVICE_FUNCS = {} + + +class ContinuityEquation(WarpEquation): + """PySPH ``ContinuityEquation`` as a composable Warp block.""" + src_arrays = ('m',) + out_arrays = ('arho',) + requires = ('dx', 'dy', 'dz', 'grad', 'vijx', 'vijy', 'vijz') + + def loop(self): + return ( + " _acc_arho += s_m[j] * (vijx*(grad*dx) + vijy*(grad*dy)" + " + vijz*(grad*dz))" + ) + + +class PressureGradient(WarpEquation): + """Inviscid WCSPH pressure-gradient acceleration as a Warp block.""" + src_arrays = ('m', 'rho', 'p') + dst_arrays = ('rho', 'p') + out_arrays = ('au', 'av', 'aw') + requires = ('dx', 'dy', 'dz', 'grad') + + def initialize(self): + return ( + " rhoi21_ = TYPE(1.0) / (d_rho[i] * d_rho[i])\n" + " tmpi_ = d_p[i] * rhoi21_" + ) + + def loop(self): + return ( + " rhoj21_ = TYPE(1.0) / (s_rho[j] * s_rho[j])\n" + " pg_tmp_ = tmpi_ + s_p[j] * rhoj21_\n" + " pg_fac_ = -s_m[j] * pg_tmp_\n" + " _acc_au += pg_fac_ * (grad * dx)\n" + " _acc_av += pg_fac_ * (grad * dy)\n" + " _acc_aw += pg_fac_ * (grad * dz)" + ) + + +class ArtificialViscosity(WarpEquation): + """Monaghan artificial viscosity (pair-averaged ``cs``) as a Warp block.""" + src_arrays = ('m', 'rho', 'cs') + dst_arrays = ('rho', 'cs') + out_arrays = ('au', 'av', 'aw') + scalars = ('alpha', 'beta') + requires = ('dx', 'dy', 'dz', 'rij2', 'hij', 'grad', + 'vijx', 'vijy', 'vijz') + + def loop(self): + return ( + " av_vdotx_ = vijx*dx + vijy*dy + vijz*dz\n" + " if av_vdotx_ < TYPE(0.0):\n" + " av_mu_ = hij * av_vdotx_" + " / (rij2 + TYPE(0.01)*hij*hij)\n" + " av_rhoij1_ = TYPE(2.0) / (d_rho[i] + s_rho[j])\n" + " av_cij_ = TYPE(0.5) * (d_cs[i] + s_cs[j])\n" + " av_piij_ = (-alpha*av_cij_*av_mu_" + " + beta*av_mu_*av_mu_) * av_rhoij1_\n" + " av_fac_ = -s_m[j] * av_piij_\n" + " _acc_au += av_fac_ * grad * dx\n" + " _acc_av += av_fac_ * grad * dy\n" + " _acc_aw += av_fac_ * grad * dz" + ) + + +class XSPHCorrection(WarpEquation): + """PySPH leapfrog XSPH position correction as a Warp block.""" + src_arrays = ('m', 'rho') + dst_arrays = ('rho',) + out_arrays = ('ax', 'ay', 'az') + scalars = ('eps',) + requires = ('rij', 'hij', 'wij', 'vijx', 'vijy', 'vijz') + + def loop(self): + return ( + " xs_rhoij1_ = TYPE(2.0) / (d_rho[i] + s_rho[j])\n" + " xs_tmp_ = -eps * s_m[j] * wij * xs_rhoij1_\n" + " _acc_ax += xs_tmp_ * vijx\n" + " _acc_ay += xs_tmp_ * vijy\n" + " _acc_az += xs_tmp_ * vijz" + ) + + +class RigidNumberDensity(WarpEquation): + """Static rigid-particle volume denominator ``V = sum_j W_ij``.""" + out_arrays = ('V',) + requires = ('hij', 'wij') + + def loop(self): + return " _acc_V += wij" + + +class LiuFluidAcceleration(WarpEquation): + """Liu pressure coupling: rigid source acceleration on fluid dest.""" + src_arrays = ('m', 'p', 'rho') + dst_arrays = ('p', 'rho') + out_arrays = ('au', 'av', 'aw') + requires = ('dx', 'dy', 'dz', 'rij', 'hij', 'grad') + + def loop(self): + return ( + " liu_t1_ = s_p[j] / (s_rho[j] * s_rho[j]) + " + "d_p[i] / (d_rho[i] * d_rho[i])\n" + " liu_fac_ = -s_m[j] * liu_t1_ * grad\n" + " _acc_au += liu_fac_ * dx\n" + " _acc_av += liu_fac_ * dy\n" + " _acc_aw += liu_fac_ * dz" + ) + + +class LiuBodyReaction(WarpEquation): + """Equal-and-opposite Liu force: fluid source onto rigid destination.""" + src_arrays = ('m', 'p', 'rho') + dst_arrays = ('m', 'p', 'rho') + out_arrays = ('fx', 'fy', 'fz') + requires = ('dx', 'dy', 'dz', 'rij', 'hij', 'grad') + + def loop(self): + # Here dx = x_body - x_fluid, the opposite of LiuFluidAcceleration's + # pair vector. The leading minus restores the body reaction direction. + return ( + " liur_t1_ = d_p[i] / (d_rho[i] * d_rho[i]) + " + "s_p[j] / (s_rho[j] * s_rho[j])\n" + " liur_fac_ = -d_m[i] * s_m[j] * liur_t1_ * grad\n" + " _acc_fx += liur_fac_ * dx\n" + " _acc_fy += liur_fac_ * dy\n" + " _acc_fz += liur_fac_ * dz" + ) + + +# The fused continuity-density acceleration group: pressure gradient, then +# Monaghan viscosity (both into au/av/aw), continuity (arho), XSPH (ax/ay/az). +# Block order fixes the per-pair accumulation order for the shared au/av/aw +# accumulators (pressure gradient before viscosity). +_WCSPH_CONTINUITY_BLOCKS = ( + PressureGradient(), ArtificialViscosity(), ContinuityEquation(), + XSPHCorrection(), +) + +# The dam-break fluid acceleration+density group (multi-array): pressure +# gradient, Monaghan viscosity (both -> au/av/aw), continuity (-> arho), FUSED so +# the per-pair geometry (dx/dy/dz, grad, vij, rij2, hij) is computed once per +# neighbour instead of three times. Run once per source array with +# accumulate_outputs=True. Excludes XSPHCorrection -- XSPH sums over the fluid +# only (not the walls), so it stays a separate launch with a different source +# set. Pressure-before-viscosity fixes the au/av/aw accumulation order, matching +# the single-array fused group above. +_WCSPH_DAM_BREAK_FLUID_BLOCKS = ( + PressureGradient(), ArtificialViscosity(), ContinuityEquation(), +) + + +class SummationDensity(WarpEquation): + """PySPH ``SummationDensity`` as a composable Warp block.""" + src_arrays = ('m',) + out_arrays = ('rho',) + requires = ('rij', 'hij', 'wij') + + def loop(self): + return " _acc_rho += s_m[j] * wij" + + +class WcsphCflFactor(WarpEquation): + """WCSPH adaptive-timestep per-particle factors as a Warp block. + + ``dt_cfl`` is the neighbor max-reduction of the viscous CFL factor + ``|hij * (vij . xij) / rij^2| + c0`` (expressed via a free-form ``wp.max`` + accumulation, valid because the factor is non-negative so a zero seed is the + max identity); ``dt_force`` is the neighbor-independent squared acceleration, + written in ``post_loop``. Replaces the hand-written ``_wcsph_dt_factors`` + kernels (both flat and grid, via ``neighbor_mode``). + """ + dst_arrays = ('au', 'av', 'aw') + out_arrays = ('dt_cfl', 'dt_force') + scalars = ('c0',) + requires = ('dx', 'dy', 'dz', 'rij2', 'hij', 'vijx', 'vijy', 'vijz') + + def loop(self): + return ( + " if rij2 > TYPE(1.0e-12):\n" + " cfl_vdotx_ = vijx*dx + vijy*dy + vijz*dz\n" + " cfl_factor_ = wp.abs(hij * cfl_vdotx_ / rij2) + c0\n" + " _acc_dt_cfl = wp.max(_acc_dt_cfl, cfl_factor_)" + ) + + def post_loop(self): + return ( + " d_dt_force[i] = d_au[i]*d_au[i] + d_av[i]*d_av[i]" + " + d_aw[i]*d_aw[i]" + ) + + +def _run_equation_group(nnps, src_index, dst_index, blocks, scalar_values=None, + kernel='cubic', cache=None, neighbor_mode='flat', + accumulate_outputs=False): + """Build (or fetch) the generated group kernel for ``blocks`` and launch it. + + Binds device arrays/scalars in the generator's canonical order and writes + into the destination ``out_arrays``. ``neighbor_mode='grid'`` walks the + uniform-grid cell list (ignores ``cache``); ``'flat'`` uses the CSR cache + (built here if ``None``). ``accumulate_outputs=True`` adds to the existing + destination arrays (read-modify-write). Self- and cross-array (``src != + dst``) are both supported because the generator emits ``s_``/``d_`` arrays + separately. EOS / property / push handling stays with the caller. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for _run_equation_group") + scalar_values = scalar_values or {} + src = nnps.particles[src_index].gpu + dst = nnps.particles[dst_index].gpu + ndst = dst.get_number_of_particles() + if ndst <= 0: + return + dtype = np.float32 if src.x.dtype == np.float32 else np.float64 + # Periodicity is a property of the NNPS (set_periodic_box): when the grid + # bounds carry a periodic dimension, build the minimum-image kernel variant + # and pass the box. Only meaningful in grid mode. + periodic = bool( + neighbor_mode == 'grid' and getattr(nnps, '_bounds', None) and ( + nnps._bounds.get('periodic_x') or nnps._bounds.get('periodic_y') + or nnps._bounds.get('periodic_z') + ) + ) + group = build_group_kernel( + blocks, dtype, _WARP_DEVICE_FUNCS, neighbor_mode=neighbor_mode, + accumulate_outputs=accumulate_outputs, periodic=periodic, + ) + inputs = [src.get_device_array(n).dev for n in group.src_names] + inputs += [dst.get_device_array(n).dev for n in group.dst_names] + if neighbor_mode == 'grid': + inputs += _grid_launch_args(nnps, src_index, dtype, periodic=periodic) + elif neighbor_mode == 'multilevel': + # Correct per-level periodic tiling is deferred (ADR-0007); refuse + # rather than silently walk a non-periodic domain. + if getattr(nnps, '_periodic_box', None) is not None: + raise ValueError( + "multilevel neighbor_mode does not support periodic domains; " + "per-level periodic tiling is deferred (ADR-0007)" + ) + inputs += _multilevel_grid_launch_args(nnps, src_index, dtype) + else: + if cache is None: + cache = nnps.build_neighbor_cache_gpu(src_index, dst_index) + inputs += [ + cache['starts_dev'], cache['lengths_dev'], cache['neighbors_dev'], + ] + inputs += [np.int32(nnps.dim), _kernel_id(kernel)] + inputs += [dtype(scalar_values[n]) for n in group.scalar_names] + inputs += [dst.get_device_array(n).dev for n in group.out_names] + wp.launch(group.kernel, dim=ndst, inputs=inputs, device=nnps.device) + wp.synchronize_device(nnps.device) + + +def _ensure_warp_helper(pa, device): + if pa.gpu is None or getattr(pa.gpu, 'backend', None) != 'warp': + pa.set_device_helper(WarpDeviceHelper(pa, backend='warp', + device=device)) + + +def _ensure_property(pa, prop, device): + if prop not in pa.properties: + pa.add_property(prop) + if pa.gpu is not None and getattr(pa.gpu, 'backend', None) == 'warp': + pa.gpu.add_prop(prop, pa.properties[prop]) + _ensure_warp_helper(pa, device) + + +def _ensure_sound_speed(pa, c0, device): + if 'cs' not in pa.properties: + n = pa.get_number_of_particles() + pa.add_property('cs', data=np.ones(n) * c0) + if pa.gpu is not None and getattr(pa.gpu, 'backend', None) == 'warp': + pa.gpu.add_prop('cs', pa.properties['cs']) + _ensure_warp_helper(pa, device) + + +def _kernel_id(kernel): + if isinstance(kernel, (int, np.integer)): + if int(kernel) in (0, 1, 2): + return np.int32(kernel) + raise ValueError( + "kernel id must be 0 (cubic), 1 (gaussian), or 2 (wendland)") + name = str(kernel).lower().replace('-', '_') + if name in ('cubic', 'cubic_spline', 'cubicspline'): + return np.int32(0) + if name == 'gaussian': + return np.int32(1) + if name in ('wendland', 'wendland_quintic', 'wendlandquintic'): + return np.int32(2) + raise ValueError("kernel must be 'cubic', 'gaussian', or 'wendland'") + + +def compute_summation_density(nnps, src_index=0, dst_index=0, + out_prop='rho', push=True, kernel='cubic', + cache=None, neighbor_mode='flat'): + """Compute standard SPH summation density with Warp. + + This mirrors ``pysph.sph.basic_equations.SummationDensity`` for one + source/destination pair using PySPH's standard ``HIJ`` convention: + ``HIJ = 0.5*(d_h[d_idx] + s_h[s_idx])``. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_summation_density") + if out_prop != 'rho': + raise ValueError( + "compute_summation_density writes the generated SummationDensity " + "block's canonical 'rho' array; a custom out_prop is not supported." + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + _ensure_property(dst_pa, out_prop, nnps.device) + + if push: + src_pa.gpu.push('x', 'y', 'z', 'h', 'm') + dst_pa.gpu.push('x', 'y', 'z', 'h', out_prop) + _run_equation_group( + nnps, src_index, dst_index, [SummationDensity()], + kernel=kernel, cache=cache, neighbor_mode=neighbor_mode, + ) + return dst_pa.gpu.get_device_array(out_prop) + + +def compute_isothermal_eos(pa, rho0, c0, p0=0.0, out_prop='p', + device=None, push=True): + """Compute PySPH ``IsothermalEOS`` on a Warp ParticleArray.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_isothermal_eos") + + device = wp.get_device(device) + _ensure_property(pa, out_prop, device) + if push: + pa.gpu.push('rho', out_prop) + rho = pa.gpu.get_device_array('rho') + out = pa.gpu.get_device_array(out_prop) + n = pa.gpu.get_number_of_particles() + if rho.dtype == np.float32: + kernel = _isothermal_eos_f32 + rho0 = np.float32(rho0) + c02 = np.float32(c0*c0) + p0 = np.float32(p0) + else: + kernel = _isothermal_eos_f64 + rho0 = np.float64(rho0) + c02 = np.float64(c0*c0) + p0 = np.float64(p0) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[rho.dev, out.dev, rho0, c02, p0], + device=device, + ) + wp.synchronize_device(device) + return out + + +def compute_tait_eos(pa, rho0, c0, gamma=7.0, p0=0.0, out_prop='p', + cs_prop='cs', device=None, push=True): + """Compute PySPH ``TaitEOS`` pressure and sound speed with Warp.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_tait_eos") + + device = wp.get_device(device) + _ensure_property(pa, out_prop, device) + _ensure_property(pa, cs_prop, device) + if push: + pa.gpu.push('rho', out_prop, cs_prop) + rho = pa.gpu.get_device_array('rho') + out = pa.gpu.get_device_array(out_prop) + cs = pa.gpu.get_device_array(cs_prop) + n = pa.gpu.get_number_of_particles() + if rho.dtype == np.float32: + kernel = _tait_eos_f32 + rho0 = np.float32(rho0) + rho01 = np.float32(1.0 / rho0) + c0 = np.float32(c0) + gamma = np.float32(gamma) + gamma1 = np.float32(0.5 * (gamma - np.float32(1.0))) + b = np.float32(rho0*c0*c0/gamma) + p0 = np.float32(p0) + else: + kernel = _tait_eos_f64 + rho0 = np.float64(rho0) + rho01 = np.float64(1.0 / rho0) + c0 = np.float64(c0) + gamma = np.float64(gamma) + gamma1 = np.float64(0.5 * (gamma - np.float64(1.0))) + b = np.float64(rho0*c0*c0/gamma) + p0 = np.float64(p0) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + rho.dev, out.dev, cs.dev, rho0, rho01, c0, gamma, + gamma1, b, p0 + ], + device=device, + ) + wp.synchronize_device(device) + return out, cs + + +def compute_tait_eos_hg_correction(pa, rho0, c0, gamma=7.0, out_prop='p', + cs_prop='cs', device=None, push=True): + """PySPH ``TaitEOSHGCorrection`` for solid walls (ADR-0005). + + Clamps density to ``>= rho0`` in place (so wall pressure stays ``>= 0`` and + repels approaching fluid) and then applies Tait EOS for ``p`` and ``cs``. + Used on boundary/solid arrays in the dam-break step; the fluid uses the + regular :func:`compute_tait_eos`. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_tait_eos_hg_correction") + + device = wp.get_device(device) + _ensure_property(pa, out_prop, device) + _ensure_property(pa, cs_prop, device) + if push: + pa.gpu.push('rho', out_prop, cs_prop) + rho = pa.gpu.get_device_array('rho') + out = pa.gpu.get_device_array(out_prop) + cs = pa.gpu.get_device_array(cs_prop) + n = pa.gpu.get_number_of_particles() + if rho.dtype == np.float32: + kernel = _tait_eos_hg_correction_f32 + rho0 = np.float32(rho0) + rho01 = np.float32(1.0 / rho0) + c0 = np.float32(c0) + gamma = np.float32(gamma) + gamma1 = np.float32(0.5 * (gamma - np.float32(1.0))) + b = np.float32(rho0 * c0 * c0 / gamma) + else: + kernel = _tait_eos_hg_correction_f64 + rho0 = np.float64(rho0) + rho01 = np.float64(1.0 / rho0) + c0 = np.float64(c0) + gamma = np.float64(gamma) + gamma1 = np.float64(0.5 * (gamma - np.float64(1.0))) + b = np.float64(rho0 * c0 * c0 / gamma) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[rho.dev, out.dev, cs.dev, rho0, rho01, c0, gamma, + gamma1, b], + device=device, + ) + wp.synchronize_device(device) + return out, cs + + +def compute_continuity(nnps, src_index=0, dst_index=0, out_prop='arho', + push=True, kernel='cubic', cache=None, + neighbor_mode='flat'): + """Compute PySPH ``ContinuityEquation`` with Warp.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_continuity") + if out_prop != 'arho': + raise ValueError( + "compute_continuity writes the generated ContinuityEquation " + "block's canonical 'arho' array; a custom out_prop is not supported." + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + _ensure_property(dst_pa, out_prop, nnps.device) + + if push: + src_pa.gpu.push('x', 'y', 'z', 'h', 'm', 'u', 'v', 'w') + dst_pa.gpu.push('x', 'y', 'z', 'h', 'u', 'v', 'w', out_prop) + _run_equation_group( + nnps, src_index, dst_index, [ContinuityEquation()], + kernel=kernel, cache=cache, neighbor_mode=neighbor_mode, + ) + return dst_pa.gpu.get_device_array(out_prop) + + +def compute_pressure_gradient(nnps, src_index=0, dst_index=0, + out_props=('au', 'av', 'aw'), push=True, + kernel='cubic', cache=None, + neighbor_mode='flat'): + """Compute the inviscid pressure-gradient part of WCSPH momentum.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_pressure_gradient") + if tuple(out_props) != ('au', 'av', 'aw'): + raise ValueError( + "compute_pressure_gradient writes the generated PressureGradient " + "block's canonical ('au','av','aw'); custom out_props unsupported." + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + for prop in out_props: + _ensure_property(dst_pa, prop, nnps.device) + + if push: + src_pa.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'p') + dst_pa.gpu.push('x', 'y', 'z', 'h', 'rho', 'p', *out_props) + _run_equation_group( + nnps, src_index, dst_index, [PressureGradient()], + kernel=kernel, cache=cache, neighbor_mode=neighbor_mode, + ) + dst = dst_pa.gpu + return ( + dst.get_device_array(out_props[0]), + dst.get_device_array(out_props[1]), + dst.get_device_array(out_props[2]), + ) + + +def compute_artificial_viscosity(nnps, src_index=0, dst_index=0, alpha=0.1, + beta=0.0, c0=20.0, + out_props=('au', 'av', 'aw'), push=True, + kernel='cubic', cache=None, + neighbor_mode='flat'): + """Add Monaghan artificial viscosity to WCSPH acceleration arrays.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_artificial_viscosity") + if tuple(out_props) != ('au', 'av', 'aw'): + raise ValueError( + "compute_artificial_viscosity writes the generated " + "ArtificialViscosity block's canonical ('au','av','aw'); custom " + "out_props unsupported." + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + _ensure_sound_speed(src_pa, c0, nnps.device) + if dst_pa is not src_pa: + _ensure_sound_speed(dst_pa, c0, nnps.device) + for prop in out_props: + _ensure_property(dst_pa, prop, nnps.device) + + if push: + src_pa.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'cs', 'u', 'v', 'w') + dst_pa.gpu.push( + 'x', 'y', 'z', 'h', 'rho', 'cs', 'u', 'v', 'w', *out_props + ) + # Monaghan viscosity composes onto any prior pressure-gradient + # acceleration, so the generated group must add to the existing au/av/aw + # (read-modify-write) rather than overwrite -- matching the hand kernel. + _run_equation_group( + nnps, src_index, dst_index, [ArtificialViscosity()], + scalar_values={'alpha': alpha, 'beta': beta}, + kernel=kernel, cache=cache, neighbor_mode=neighbor_mode, + accumulate_outputs=True, + ) + dst = dst_pa.gpu + return ( + dst.get_device_array(out_props[0]), + dst.get_device_array(out_props[1]), + dst.get_device_array(out_props[2]), + ) + + +def compute_xsph_correction(nnps, src_index=0, dst_index=0, eps=0.5, + out_props=('ax', 'ay', 'az'), push=True, + kernel='cubic', cache=None, + neighbor_mode='flat'): + """Compute PySPH leapfrog XSPH position correction on the device.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_xsph_correction") + if tuple(out_props) != ('ax', 'ay', 'az'): + raise ValueError( + "compute_xsph_correction writes the generated XSPHCorrection " + "block's canonical ('ax','ay','az'); custom out_props unsupported." + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + for prop in out_props: + _ensure_property(dst_pa, prop, nnps.device) + + if push: + src_pa.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'u', 'v', 'w') + dst_pa.gpu.push( + 'x', 'y', 'z', 'h', 'rho', 'u', 'v', 'w', *out_props + ) + _run_equation_group( + nnps, src_index, dst_index, [XSPHCorrection()], + scalar_values={'eps': eps}, kernel=kernel, cache=cache, + neighbor_mode=neighbor_mode, + ) + dst = dst_pa.gpu + return ( + dst.get_device_array(out_props[0]), + dst.get_device_array(out_props[1]), + dst.get_device_array(out_props[2]), + ) + + +def euler_step(pa, dt, dim=3, device=None, push=True): + """Advance position and velocity using already-computed acceleration.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for euler_step") + + device = wp.get_device(device) + _ensure_warp_helper(pa, device) + if push: + pa.gpu.push('x', 'y', 'z', 'u', 'v', 'w', 'au', 'av', 'aw') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if gpu.x.dtype == np.float32: + kernel = _euler_step_f32 + dt = np.float32(dt) + else: + kernel = _euler_step_f64 + dt = np.float64(dt) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, + gpu.au.dev, gpu.av.dev, gpu.aw.dev, + dt, np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x, gpu.y, gpu.z, gpu.u, gpu.v, gpu.w + + +def leapfrog_kick(pa, dt, dim=3, device=None, push=True): + """Kick velocity with the current acceleration.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for leapfrog_kick") + + device = wp.get_device(device) + _ensure_warp_helper(pa, device) + if push: + pa.gpu.push('u', 'v', 'w', 'au', 'av', 'aw') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if gpu.u.dtype == np.float32: + kernel = _leapfrog_kick_f32 + dt = np.float32(dt) + else: + kernel = _leapfrog_kick_f64 + dt = np.float64(dt) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.u.dev, gpu.v.dev, gpu.w.dev, + gpu.au.dev, gpu.av.dev, gpu.aw.dev, + dt, np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.u, gpu.v, gpu.w + + +def apply_body_force(pa, gx=0.0, gy=0.0, gz=0.0, dim=3, ramp=1.0, + device=None, push=True): + """Add a (ramped) constant body-force acceleration to ``au``/``av``/``aw``. + + Gravity is a body force, i.e. an acceleration; this adds ``ramp*g`` to the + acceleration arrays the integrator integrates -- matching PySPH's + ``MomentumEquation`` ``gz`` term -- so it folds consistently into the PEC + predictor and corrector. Standalone additive kernel (ADR-0005): it does not + touch any generated equation kernel or the elliptical-drop step, so with the + default ``gx=gy=gz=0`` it is a no-op and the 2D path is unchanged. ``ramp`` + in ``[0, 1]`` applies the WCSPH ``n_damp`` gravity startup ramp. Components + are applied under the same ``dim>1``/``dim>2`` guards as the integrator. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for apply_body_force") + + device = wp.get_device(device) + _ensure_property(pa, 'au', device) + _ensure_property(pa, 'av', device) + _ensure_property(pa, 'aw', device) + if push: + pa.gpu.push('au', 'av', 'aw') + gpu = pa.gpu + n = gpu.get_number_of_particles() + gx_e, gy_e, gz_e = ramp * gx, ramp * gy, ramp * gz + if gpu.au.dtype == np.float32: + kernel = _apply_body_force_f32 + gx_e, gy_e, gz_e = ( + np.float32(gx_e), np.float32(gy_e), np.float32(gz_e) + ) + else: + kernel = _apply_body_force_f64 + gx_e, gy_e, gz_e = ( + np.float64(gx_e), np.float64(gy_e), np.float64(gz_e) + ) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.au.dev, gpu.av.dev, gpu.aw.dev, + gx_e, gy_e, gz_e, np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.au, gpu.av, gpu.aw + + +def leapfrog_drift(pa, dt, dim=3, device=None, push=True): + """Drift position with the current velocity.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for leapfrog_drift") + + device = wp.get_device(device) + _ensure_warp_helper(pa, device) + if push: + pa.gpu.push('x', 'y', 'z', 'u', 'v', 'w') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if gpu.x.dtype == np.float32: + kernel = _leapfrog_drift_f32 + dt = np.float32(dt) + else: + kernel = _leapfrog_drift_f64 + dt = np.float64(dt) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, + dt, np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x, gpu.y, gpu.z + + +def leapfrog_drift_xsph(pa, dt, dim=3, device=None, push=True): + """Drift position with velocity plus precomputed XSPH correction.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for leapfrog_drift_xsph") + + device = wp.get_device(device) + _ensure_property(pa, 'ax', device) + _ensure_property(pa, 'ay', device) + _ensure_property(pa, 'az', device) + if push: + pa.gpu.push('x', 'y', 'z', 'u', 'v', 'w', 'ax', 'ay', 'az') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if gpu.x.dtype == np.float32: + kernel = _leapfrog_drift_xsph_f32 + dt = np.float32(dt) + else: + kernel = _leapfrog_drift_xsph_f64 + dt = np.float64(dt) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, + gpu.ax.dev, gpu.ay.dev, gpu.az.dev, + dt, np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x, gpu.y, gpu.z + + +def save_wcsph_state(pa, dim=3, device=None, push=True): + """Save WCSPH PEC reference position, velocity, and density on device.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for save_wcsph_state") + + device = wp.get_device(device) + for prop in ('x0', 'y0', 'z0', 'u0', 'v0', 'w0', 'rho0'): + _ensure_property(pa, prop, device) + if push: + pa.gpu.push( + 'x', 'y', 'z', 'u', 'v', 'w', 'rho', + 'x0', 'y0', 'z0', 'u0', 'v0', 'w0', 'rho0' + ) + gpu = pa.gpu + n = gpu.get_number_of_particles() + if gpu.x.dtype == np.float32: + kernel = _wcsph_save_state_f32 + else: + kernel = _wcsph_save_state_f64 + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, gpu.rho.dev, + gpu.x0.dev, gpu.y0.dev, gpu.z0.dev, + gpu.u0.dev, gpu.v0.dev, gpu.w0.dev, gpu.rho0.dev, + np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x0, gpu.u0, gpu.rho0 + + +def wcsph_pec_stage(pa, dt, stage=1.0, dim=3, xsph=False, device=None, + push=True): + """Apply one PySPH ``WCSPHStep``-style PEC stage on the device.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for wcsph_pec_stage") + + device = wp.get_device(device) + for prop in ( + 'x0', 'y0', 'z0', 'u0', 'v0', 'w0', 'rho0', + 'au', 'av', 'aw', 'ax', 'ay', 'az', 'arho', + ): + _ensure_property(pa, prop, device) + if push: + pa.gpu.push( + 'x0', 'y0', 'z0', 'u0', 'v0', 'w0', 'rho0', + 'x', 'y', 'z', 'u', 'v', 'w', 'rho', + 'au', 'av', 'aw', 'ax', 'ay', 'az', 'arho' + ) + gpu = pa.gpu + n = gpu.get_number_of_particles() + use_xsph = np.int32(bool(xsph)) + if gpu.x.dtype == np.float32: + kernel = _wcsph_pec_stage_f32 + dt_factor = np.float32(dt * stage) + else: + kernel = _wcsph_pec_stage_f64 + dt_factor = np.float64(dt * stage) + if n > 0: + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x0.dev, gpu.y0.dev, gpu.z0.dev, + gpu.u0.dev, gpu.v0.dev, gpu.w0.dev, gpu.rho0.dev, + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, gpu.rho.dev, + gpu.au.dev, gpu.av.dev, gpu.aw.dev, + gpu.ax.dev, gpu.ay.dev, gpu.az.dev, gpu.arho.dev, + dt_factor, np.int32(dim), use_xsph + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x, gpu.y, gpu.z, gpu.u, gpu.v, gpu.w, gpu.rho + + +def compute_wcsph_adaptive_timestep(nnps, pa_index=0, c0=20.0, cfl=0.25, + dt_min=0.0, dt_max=np.inf, push=True, + cache=None, neighbor_mode='flat'): + """Compute WCSPH adaptive timestep with device reductions. + + Only the final scalar timestep is copied back to the host. Per-particle + ``dt_cfl`` and ``dt_force`` remain on the device unless explicitly pulled. + + ``neighbor_mode`` defaults to ``'flat'`` so callers (including the + summation-density leapfrog path) keep the prebuilt CSR ``cache`` behavior + unchanged. The continuity-density PEC step passes ``'grid'`` (ADR-0004) to + walk the uniform-grid cell list directly for the CFL viscous factor and + avoid building a flat list; in that mode ``cache`` is ignored. Both modes + visit the same neighbor set, and the CFL factor is an order-independent + ``max`` reduction, so the resulting timestep matches to fp32 scale. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_wcsph_adaptive_timestep") + + pa = nnps.particles[pa_index] + _ensure_property(pa, 'dt_cfl', nnps.device) + _ensure_property(pa, 'dt_force', nnps.device) + if push: + pa.gpu.push( + 'x', 'y', 'z', 'h', 'u', 'v', 'w', 'au', 'av', 'aw', + 'dt_cfl', 'dt_force' + ) + if cache is None and neighbor_mode == 'flat': + cache = nnps.build_neighbor_cache_gpu(pa_index, pa_index) + gpu = pa.gpu + n = gpu.get_number_of_particles() + dt_cfl = gpu.get_device_array('dt_cfl') + dt_force = gpu.get_device_array('dt_force') + if gpu.x.dtype == np.float32: + dtype = wp.float32 + init_kernel = _wcsph_dt_init_f32 + reduce_kernel = _wcsph_dt_reduce_f32 + finalize_kernel = _wcsph_dt_finalize_f32 + cfl = np.float32(cfl) + dt_min = np.float32(dt_min) + dt_max = np.float32(dt_max) + else: + dtype = wp.float64 + init_kernel = _wcsph_dt_init_f64 + reduce_kernel = _wcsph_dt_reduce_f64 + finalize_kernel = _wcsph_dt_finalize_f64 + cfl = np.float64(cfl) + dt_min = np.float64(dt_min) + dt_max = np.float64(dt_max) + + max_cfl = wp.zeros(1, dtype=dtype, device=nnps.device) + max_force = wp.zeros(1, dtype=dtype, device=nnps.device) + min_h = wp.zeros(1, dtype=dtype, device=nnps.device) + out_dt = wp.zeros(1, dtype=dtype, device=nnps.device) + if n > 0: + # Per-particle CFL/force factors via the generated WcsphCflFactor group + # (flat or grid). dt_cfl is a neighbor max-reduction, dt_force a + # per-particle term; both written into pa's dt_cfl/dt_force arrays. + _run_equation_group( + nnps, pa_index, pa_index, [WcsphCflFactor()], + scalar_values={'c0': c0}, cache=cache, neighbor_mode=neighbor_mode, + ) + wp.launch( + init_kernel, + dim=1, + inputs=[max_cfl, max_force, min_h, out_dt], + device=nnps.device, + ) + wp.launch( + reduce_kernel, + dim=n, + inputs=[ + gpu.h.dev, dt_cfl.dev, dt_force.dev, + max_cfl, max_force, min_h + ], + device=nnps.device, + ) + wp.launch( + finalize_kernel, + dim=1, + inputs=[max_cfl, max_force, min_h, cfl, dt_min, dt_max, out_dt], + device=nnps.device, + ) + wp.synchronize_device(nnps.device) + return float(out_dt.numpy()[0]) + return float(dt_max) + + +def _rigid_finalize_moments(mi, omega=None, nbody=1): + """Host (numpy) finalize of the device RigidBodyMoments reduction (ADR-0006). + + Given the reduced 16-slot-per-body ``mi`` vector (total mass; ``m*x/y/z``; + the six second-moments/products of inertia about the ORIGIN; total force; + torque about the origin) produced by ``_rigid_moments_reduce_*``, compute + per body: total mass, centre of mass, the moment-of-inertia tensor about the + COM (parallel-axis theorem), total force, COM acceleration, torque about the + COM, and ``omega_dot = inv(I) (tau - omega x (I omega))``. Mirrors + ``RigidBodyMoments`` (rigid_body.py:128-207) exactly. This is the explicit + host-result oracle/debug path; production stepping uses the equivalent + device finalize. ``omega`` is the current per-body angular velocity + (``(nbody, 3)``; defaults to rest). + """ + mi = np.asarray(mi, dtype=np.float64) + if omega is None: + omega = np.zeros((nbody, 3)) + else: + omega = np.asarray(omega, dtype=np.float64).reshape(nbody, 3) + res = { + 'total_mass': np.zeros(nbody), + 'cm': np.zeros((nbody, 3)), + 'inertia': np.zeros((nbody, 3, 3)), + 'force': np.zeros((nbody, 3)), + 'ac': np.zeros((nbody, 3)), + 'torque': np.zeros((nbody, 3)), + 'omega_dot': np.zeros((nbody, 3)), + } + for b in range(nbody): + base = b * 16 + m = mi[base + 0] + cx = mi[base + 1] / m + cy = mi[base + 2] / m + cz = mi[base + 3] / m + # Parallel-axis theorem: moments/products of inertia about the COM. + ixx = mi[base + 4] - (cy * cy + cz * cz) * m + iyy = mi[base + 5] - (cx * cx + cz * cz) * m + izz = mi[base + 6] - (cx * cx + cy * cy) * m + ixy = mi[base + 7] + cx * cy * m + ixz = mi[base + 8] + cx * cz * m + iyz = mi[base + 9] + cy * cz * m + inertia = np.array([[ixx, ixy, ixz], + [ixy, iyy, iyz], + [ixz, iyz, izz]]) + fx = mi[base + 10] + fy = mi[base + 11] + fz = mi[base + 12] + force = np.array([fx, fy, fz]) + # Torque about the COM = torque about origin - (cm x F). + tx = mi[base + 13] - (cy * fz - cz * fy) + ty = mi[base + 14] - (-cx * fz + cz * fx) + tz = mi[base + 15] - (cx * fy - cy * fx) + torque = np.array([tx, ty, tz]) + w = omega[b] + res['total_mass'][b] = m + res['cm'][b] = (cx, cy, cz) + res['inertia'][b] = inertia + res['force'][b] = force + res['ac'][b] = force / m + res['torque'][b] = torque + res['omega_dot'][b] = np.linalg.solve( + inertia, torque - np.cross(w, inertia @ w)) + return res + + +class WarpRigidBodyState: + """Persistent compact device state for ADR-0006 rigid-body stepping.""" + + def __init__(self, pa, nbody=1, vc=None, omega=None, device=None): + if wp is None: # pragma: no cover + raise ImportError("warp is required for WarpRigidBodyState") + if nbody < 1: + raise ValueError("nbody must be at least one") + self.device = wp.get_device(device) + if pa.gpu is not None and wp.get_device(pa.gpu.device) != self.device: + raise ValueError("rigid state and ParticleArray must share a device") + self.nbody = int(nbody) + self.particle_count = pa.get_number_of_particles() + if 'body_id' in pa.properties: + body_id = np.asarray(pa.body_id, dtype=np.int32) + else: + body_id = np.zeros(self.particle_count, dtype=np.int32) + if body_id.size != self.particle_count: + raise ValueError("body_id must contain one value per particle") + if body_id.size and (body_id.min() < 0 or + body_id.max() >= self.nbody): + raise ValueError("body_id values must be in [0, nbody)") + # Geometry is static in a rigid body, so reject empty/zero-mass or + # singular bodies once at setup rather than introducing a host check in + # every device stage. + mass = np.asarray(pa.m, dtype=np.float64) + xyz = np.column_stack((np.asarray(pa.x, dtype=np.float64), + np.asarray(pa.y, dtype=np.float64), + np.asarray(pa.z, dtype=np.float64))) + for body in range(self.nbody): + selected = body_id == body + if not np.any(selected) or mass[selected].sum() <= 0.0: + raise ValueError(f"rigid body {body} has no positive mass") + mb = mass[selected] + rb = xyz[selected] + center = (mb[:, None] * rb).sum(axis=0) / mb.sum() + rel = rb - center + inertia = np.eye(3) * np.sum(mb * np.sum(rel * rel, axis=1)) + inertia -= np.einsum('n,ni,nj->ij', mb, rel, rel) + scale = float(np.linalg.norm(inertia, ord=np.inf)) + if scale <= 0.0 or abs(float(np.linalg.det(inertia))) <= ( + 1.0e-14 * scale ** 3): + raise ValueError(f"rigid body {body} has singular inertia") + self.body_id = wp.array(body_id, dtype=wp.int32, device=self.device) + + def body_vector(value): + if value is None: + value = np.zeros((self.nbody, 3), dtype=np.float64) + value = np.asarray(value, dtype=np.float64) + if value.size != self.nbody * 3: + raise ValueError( + "rigid body vectors must have shape (nbody, 3)") + return wp.array(value.reshape(-1), dtype=wp.float64, + device=self.device) + + self.mi = wp.zeros(self.nbody * 16, dtype=wp.float64, + device=self.device) + self.total_mass = wp.zeros(self.nbody, dtype=wp.float64, + device=self.device) + self.cm = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.inertia = wp.zeros(self.nbody * 9, dtype=wp.float64, + device=self.device) + self.force = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.ac = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.torque = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.omega_dot = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.vc = body_vector(vc) + self.omega = body_vector(omega) + self.vc0 = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.omega0 = wp.zeros(self.nbody * 3, dtype=wp.float64, + device=self.device) + self.error = wp.zeros(self.nbody, dtype=wp.int32, device=self.device) + + +def create_rigid_body_state(pa, nbody=1, vc=None, omega=None, device=None): + """Create reusable device buffers for one or more rigid bodies.""" + device = wp.get_device(device) + for prop in ('x0', 'y0', 'z0', 'u', 'v', 'w', 'fx', 'fy', 'fz'): + _ensure_property(pa, prop, device) + return WarpRigidBodyState(pa, nbody=nbody, vc=vc, omega=omega, + device=device) + + +def _launch_rigid_moment_reduction(pa, mi, body_id_dev, device, push=False): + for prop in ('m', 'x', 'y', 'z', 'fx', 'fy', 'fz'): + _ensure_property(pa, prop, device) + gpu = pa.gpu + n = gpu.get_number_of_particles() + if push: + gpu.push('m', 'x', 'y', 'z', 'fx', 'fy', 'fz') + mi.zero_() + if n > 0: + arrays = [gpu.get_device_array(p).dev + for p in ('m', 'x', 'y', 'z', 'fx', 'fy', 'fz')] + kernel = (_rigid_moments_reduce_f32 + if gpu.get_device_array('x').dtype == np.float32 + else _rigid_moments_reduce_f64) + wp.launch(kernel, dim=n, inputs=[body_id_dev] + arrays + [mi], + device=device) + return mi + + +def compute_rigid_body_moments_device(pa, state, push=False): + """Reduce and finalize rigid moments entirely on the active Warp stream.""" + if state.particle_count != pa.get_number_of_particles(): + raise ValueError("rigid state particle count no longer matches array") + _launch_rigid_moment_reduction( + pa, state.mi, state.body_id, state.device, push=push) + wp.launch( + _rigid_finalize_device, + dim=state.nbody, + inputs=[ + state.mi, state.omega, state.total_mass, state.cm, + state.inertia, state.force, state.ac, state.torque, + state.omega_dot, state.error, + ], + device=state.device, + ) + return state + + +def save_rigid_body_state(pa, state, push=False): + """Save the start-of-step particle and compact body state on the device.""" + gpu = pa.gpu + if push: + gpu.push('x', 'y', 'z', 'x0', 'y0', 'z0') + n = gpu.get_number_of_particles() + if n > 0: + kernel = (_rigid_save_particle_state_f32 + if gpu.x.dtype == np.float32 + else _rigid_save_particle_state_f64) + wp.launch( + kernel, dim=n, + inputs=[gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.x0.dev, gpu.y0.dev, gpu.z0.dev], + device=state.device, + ) + wp.launch( + _rigid_save_body_state, dim=state.nbody * 3, + inputs=[state.vc, state.omega, state.vc0, state.omega0], + device=state.device, + ) + return state + + +def rigid_body_rk2_stage(pa, state, dt, stage, push=False): + """Run one device-resident rigid RK2 stage. + + Call :func:`save_rigid_body_state` once before the midpoint stage. ``stage`` + is ``0.5`` for the midpoint prediction and ``1.0`` for the full correction. + Forces in ``fx/fy/fz`` must correspond to the current particle positions. + The function intentionally performs no device synchronization or host copy. + """ + if stage not in (0.5, 1.0): + raise ValueError("rigid RK2 stage must be 0.5 or 1.0") + compute_rigid_body_moments_device(pa, state, push=push) + gpu = pa.gpu + n = gpu.get_number_of_particles() + dt_factor = np.float64(dt * stage) + if n > 0: + kernel = (_rigid_motion_stage_f32 + if gpu.x.dtype == np.float32 + else _rigid_motion_stage_f64) + wp.launch( + kernel, dim=n, + inputs=[ + state.body_id, state.cm, state.vc, state.omega, + gpu.x0.dev, gpu.y0.dev, gpu.z0.dev, + gpu.x.dev, gpu.y.dev, gpu.z.dev, + gpu.u.dev, gpu.v.dev, gpu.w.dev, dt_factor, + ], + device=state.device, + ) + wp.launch( + _rigid_update_body_state, dim=state.nbody * 3, + inputs=[state.ac, state.omega_dot, state.vc0, state.omega0, + state.vc, state.omega, dt_factor], + device=state.device, + ) + return state + + +def save_rigid_body_density(pa, device=None, push=False): + """Save rigid density for midpoint/full continuity staging on-device.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for save_rigid_body_density") + device = wp.get_device(device) + for prop in ('rho', 'rho0', 'arho'): + _ensure_property(pa, prop, device) + if push: + pa.gpu.push('rho', 'rho0', 'arho') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if n > 0: + kernel = (_rigid_save_density_f32 + if gpu.rho.dtype == np.float32 + else _rigid_save_density_f64) + wp.launch(kernel, dim=n, inputs=[gpu.rho.dev, gpu.rho0.dev], + device=device) + return gpu.rho0 + + +def rigid_body_density_stage(pa, dt, stage, device=None): + """Apply only the continuity-density portion of a rigid EPEC stage.""" + if stage not in (0.5, 1.0): + raise ValueError("rigid density stage must be 0.5 or 1.0") + device = wp.get_device(device) + gpu = pa.gpu + n = gpu.get_number_of_particles() + if n > 0: + if gpu.rho.dtype == np.float32: + kernel = _rigid_density_stage_f32 + factor = np.float32(dt * stage) + else: + kernel = _rigid_density_stage_f64 + factor = np.float64(dt * stage) + wp.launch(kernel, dim=n, + inputs=[gpu.rho0.dev, gpu.arho.dev, gpu.rho.dev, factor], + device=device) + return gpu.rho + + +def initialize_rigid_body_force(pa, gx=0.0, gy=0.0, gz=-9.81, + device=None, push=False): + """Set per-particle rigid force to mass times body acceleration.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for initialize_rigid_body_force") + device = wp.get_device(device) + for prop in ('m', 'fx', 'fy', 'fz'): + _ensure_property(pa, prop, device) + if push: + pa.gpu.push('m', 'fx', 'fy', 'fz') + gpu = pa.gpu + n = gpu.get_number_of_particles() + if n > 0: + if gpu.m.dtype == np.float32: + kernel = _rigid_body_force_f32 + scalars = [np.float32(gx), np.float32(gy), np.float32(gz)] + else: + kernel = _rigid_body_force_f64 + scalars = [np.float64(gx), np.float64(gy), np.float64(gz)] + wp.launch(kernel, dim=n, + inputs=[gpu.m.dev, gpu.fx.dev, gpu.fy.dev, gpu.fz.dev, + *scalars], device=device) + return gpu.fx, gpu.fy, gpu.fz + + +def compute_rigid_number_density(nnps, rigid_index, kernel='wendland', + push=False): + """Compute the static rigid self-neighbor ``V = sum W`` pre-pass.""" + rigid_index = int(rigid_index) + pa = nnps.particles[rigid_index] + _ensure_property(pa, 'V', nnps.device) + if push: + pa.gpu.push('x', 'y', 'z', 'h', 'V') + nnps.update(push=False) + _run_equation_group( + nnps, rigid_index, rigid_index, [RigidNumberDensity()], + kernel=kernel, neighbor_mode='grid') + return pa.gpu.V + + +def compute_liu_fluid_rigid_coupling(nnps, fluid_index, rigid_index, + kernel='wendland', push=False): + """Apply deterministic two-pass Liu fluid/rigid pressure coupling.""" + fluid_index = int(fluid_index) + rigid_index = int(rigid_index) + fluid = nnps.particles[fluid_index] + rigid = nnps.particles[rigid_index] + for prop in ('rho', 'p', 'au', 'av', 'aw'): + _ensure_property(fluid, prop, nnps.device) + for prop in ('rho', 'p', 'fx', 'fy', 'fz'): + _ensure_property(rigid, prop, nnps.device) + if push: + fluid.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'p', + 'au', 'av', 'aw') + rigid.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'p', + 'fx', 'fy', 'fz') + nnps.update(push=False) + _run_equation_group( + nnps, rigid_index, fluid_index, [LiuFluidAcceleration()], + kernel=kernel, neighbor_mode='grid', accumulate_outputs=True) + _run_equation_group( + nnps, fluid_index, rigid_index, [LiuBodyReaction()], + kernel=kernel, neighbor_mode='grid', accumulate_outputs=True) + return fluid.gpu.au, rigid.gpu.fx + + +def compute_rigid_body_moments(pa, nbody=1, omega=None, body_id_dev=None, + device=None, push=False): + """RigidBodyMoments for a rigid-body Warp array, on the device (ADR-0006). + + A device ``atomic_add`` SUM-reduction over the body's particles builds the + 16-slot-per-body ``mi`` vector of PySPH ``RigidBodyMoments.reduce``; the + host then finalizes it (:func:`_rigid_finalize_moments`). Accumulation is in + f64 regardless of the particle dtype because fp32 ``atomic_add`` is + order-dependent / non-associative (P0 kill-test); f64 accumulators make the + reduction ~deterministic. Additive to the backend: a standalone launch + kernel and host code, touching no generated equation source, kernel-id + router, or single-array path -- so the 2D elliptical-drop baseline and its + on-disk cache are unaffected. + + ``omega`` is the current per-body angular velocity (``(nbody, 3)``). + ``body_id_dev`` is an optional precomputed ``int32`` device array (it is + static, so the eventual step driver builds it once); when ``None`` it is + built from ``pa.body_id`` (or all-zeros for a single body). + + Returns the per-body dict from :func:`_rigid_finalize_moments`, plus the raw + host ``mi`` under key ``'mi'``. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for compute_rigid_body_moments") + + device = wp.get_device(device) + _ensure_warp_helper(pa, device) + n = pa.gpu.get_number_of_particles() + if body_id_dev is None: + if 'body_id' in pa.properties: + bid = np.asarray(pa.body_id, dtype=np.int32) + else: + bid = np.zeros(n, dtype=np.int32) + body_id_dev = wp.array(bid, dtype=wp.int32, device=device) + mi = wp.zeros(nbody * 16, dtype=wp.float64, device=device) + _launch_rigid_moment_reduction( + pa, mi, body_id_dev, device, push=push) + if n > 0: + wp.synchronize_device(device) + mi_host = mi.numpy() + result = _rigid_finalize_moments(mi_host, omega=omega, nbody=nbody) + result['mi'] = mi_host + return result + + +def _periodic_bounds(bounds, dim): + if bounds is None: + return None + if not isinstance(bounds, dict): + raise TypeError("periodic bounds must be a dict or None") + + xmin = bounds.get('xmin', 0.0) + xmax = bounds.get('xmax', xmin) + ymin = bounds.get('ymin', 0.0) + ymax = bounds.get('ymax', ymin) + zmin = bounds.get('zmin', 0.0) + zmax = bounds.get('zmax', zmin) + periodic_x = bool(bounds.get('periodic_in_x', 'xmin' in bounds and + 'xmax' in bounds)) + periodic_y = bool(bounds.get('periodic_in_y', 'ymin' in bounds and + 'ymax' in bounds and dim > 1)) + periodic_z = bool(bounds.get('periodic_in_z', 'zmin' in bounds and + 'zmax' in bounds and dim > 2)) + return ( + xmin, xmax, ymin, ymax, zmin, zmax, + periodic_x, periodic_y, periodic_z + ) + + +def wrap_periodic(pa, bounds, dim=3, device=None): + """Wrap particle coordinates into a periodic box on the device.""" + if wp is None: # pragma: no cover + raise ImportError("warp is required for wrap_periodic") + + parsed = _periodic_bounds(bounds, dim) + if parsed is None: + return None + + device = wp.get_device(device) + _ensure_warp_helper(pa, device) + gpu = pa.gpu + n = gpu.get_number_of_particles() + if n == 0: + return gpu.x, gpu.y, gpu.z + + xmin, xmax, ymin, ymax, zmin, zmax, px, py, pz = parsed + if gpu.x.dtype == np.float32: + kernel = _wrap_periodic_f32 + scalars = [ + np.float32(xmin), np.float32(xmax), + np.float32(ymin), np.float32(ymax), + np.float32(zmin), np.float32(zmax), + ] + else: + kernel = _wrap_periodic_f64 + scalars = [ + np.float64(xmin), np.float64(xmax), + np.float64(ymin), np.float64(ymax), + np.float64(zmin), np.float64(zmax), + ] + + wp.launch( + kernel, + dim=n, + inputs=[ + gpu.x.dev, gpu.y.dev, gpu.z.dev, + scalars[0], scalars[1], scalars[2], scalars[3], + scalars[4], scalars[5], + np.int32(px), np.int32(py), np.int32(pz), np.int32(dim) + ], + device=device, + ) + wp.synchronize_device(device) + return gpu.x, gpu.y, gpu.z + + +def _apply_wcsph_eos(nnps, pa, rho0, c0, p0, eos, gamma): + """Apply the equation of state (per-particle, no neighbor loop). + + Shared by the summation-density path and the fused continuity path. + """ + if eos == 'isothermal': + compute_isothermal_eos( + pa, rho0=rho0, c0=c0, p0=p0, device=nnps.device, push=False + ) + _ensure_sound_speed(pa, c0, nnps.device) + elif eos == 'tait': + compute_tait_eos( + pa, rho0=rho0, c0=c0, gamma=gamma, p0=p0, + device=nnps.device, push=False + ) + else: + raise ValueError("EOS must be 'isothermal' or 'tait'") + + +def _grid_launch_args(nnps, src_index, dtype, periodic=False): + """Ordered grid-query launch inputs for a grid-direct kernel (ADR-0004). + + Mirrors the signature emitted by ``warp_codegen`` in ``grid`` mode: the + device cell list from ``_build_grid`` (reused per ``update()``) followed by + the grid bounds and ``radius_scale``. When ``periodic`` is set, the periodic + box lengths and per-dimension periodic flags are appended, matching the + minimum-image kernel variant. + """ + grid = nnps._build_grid(src_index) + b = nnps._bounds + args = [ + grid['starts'], grid['counts'], grid['cell_particles'], + dtype(b['xmin']), dtype(b['ymin']), dtype(b['zmin']), + dtype(nnps.cell_size), + np.int32(b['nx']), np.int32(b['ny']), np.int32(b['nz']), + np.int32(b['ncells']), + dtype(nnps.radius_scale), + ] + if periodic: + args += [ + dtype(b['box_lx']), dtype(b['box_ly']), dtype(b['box_lz']), + np.int32(1 if b['periodic_x'] else 0), + np.int32(1 if b['periodic_y'] else 0), + np.int32(1 if b['periodic_z'] else 0), + ] + return args + + +def _multilevel_grid_launch_args(nnps, src_index, dtype): + """Ordered multilevel-query launch inputs for a multilevel-direct kernel. + + Mirrors the signature emitted by ``warp_codegen`` in ``multilevel`` mode: + the flattened global cell list (``cell_starts/cell_counts/cell_particles``) + followed by the per-level (length ``nlevels``) metadata arrays -- origins, + cell sizes, ``(nx,ny,nz)``, ``cell_offset``, ``support`` -- then ``nlevels`` + and ``radius_scale``. Built once per source array per ``update()`` by + ``MultilevelGridWarpNNPS._build_multilevel`` (ADR-0007). Does not overload + the scalar ``_grid_launch_args`` contract. + """ + ml = nnps._build_multilevel(src_index) + return [ + ml['starts'], ml['counts'], ml['cell_particles'], + ml['origin_x'], ml['origin_y'], ml['origin_z'], ml['cell_size'], + ml['nx'], ml['ny'], ml['nz'], ml['cell_offset'], ml['support'], + np.int32(nnps.nlevels), + dtype(nnps.radius_scale), + ] + + +def compute_wcsph_accel_continuity(nnps, src_index=0, dst_index=0, alpha=0.1, + beta=0.0, eps=0.5, c0=20.0, kernel='cubic', + cache=None, push=True, neighbor_mode='grid'): + """Fused continuity-density acceleration via a generated group kernel. + + One neighbor traversal computes ``ContinuityEquation`` (``arho``), the + inviscid pressure gradient plus Monaghan artificial viscosity + (``au, av, aw``), and the XSPH correction (``ax, ay, az``), replacing four + separate launches over the same neighbor cache (ADR-0003). EOS must have + been applied beforehand because the kernel reads ``p`` and ``cs``. + + ``neighbor_mode='grid'`` (default, ADR-0004) walks the uniform-grid cell + list directly and ignores ``cache``, so no flat neighbor list is built; + ``'flat'`` reads the prebuilt CSR ``cache`` (built here if ``None``) and is + retained for the oracle/host-query path and parity tests. + """ + if wp is None: # pragma: no cover + raise ImportError( + "warp is required for compute_wcsph_accel_continuity" + ) + + src_pa = nnps.particles[src_index] + dst_pa = nnps.particles[dst_index] + _ensure_sound_speed(src_pa, c0, nnps.device) + if dst_pa is not src_pa: + _ensure_sound_speed(dst_pa, c0, nnps.device) + for prop in ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az'): + _ensure_property(dst_pa, prop, nnps.device) + + if push: + src_pa.gpu.push( + 'x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', 'u', 'v', 'w' + ) + dst_pa.gpu.push( + 'x', 'y', 'z', 'h', 'rho', 'p', 'cs', 'u', 'v', 'w', + 'au', 'av', 'aw', 'arho', 'ax', 'ay', 'az' + ) + dst = dst_pa.gpu + if dst.get_number_of_particles() <= 0: + return None + _run_equation_group( + nnps, src_index, dst_index, _WCSPH_CONTINUITY_BLOCKS, + scalar_values={'alpha': alpha, 'beta': beta, 'eps': eps}, + kernel=kernel, cache=cache, neighbor_mode=neighbor_mode, + ) + return ( + dst.get_device_array('au'), dst.get_device_array('av'), + dst.get_device_array('aw'), + ) + + +def _compute_wcsph_acceleration(nnps, pa_index, rho0, c0, p0, alpha, beta, + push, eos, gamma, kernel, + density_mode='summation', cache=None): + # Grid-direct (ADR-0004 extended): all neighbor consumers walk the cell list + # directly, so the summation step builds no flat CSR neighbor cache. The + # pressure-gradient(overwrite) -> viscosity(add) composition is unchanged; + # only the neighbor source (and thus fp32 visitation order) differs. + pa = nnps.particles[pa_index] + if density_mode == 'summation': + compute_summation_density( + nnps, pa_index, pa_index, push=push, kernel=kernel, + neighbor_mode='grid' + ) + elif density_mode == 'continuity': + _ensure_property(pa, 'arho', nnps.device) + if push: + pa.gpu.push('rho', 'arho') + else: + raise ValueError("density_mode must be 'summation' or 'continuity'") + _apply_wcsph_eos(nnps, pa, rho0, c0, p0, eos, gamma) + result = compute_pressure_gradient( + nnps, pa_index, pa_index, push=False, kernel=kernel, + neighbor_mode='grid' + ) + if alpha != 0.0 or beta != 0.0: + result = compute_artificial_viscosity( + nnps, pa_index, pa_index, alpha=alpha, beta=beta, c0=c0, + push=False, kernel=kernel, neighbor_mode='grid' + ) + if density_mode == 'continuity': + compute_continuity( + nnps, pa_index, pa_index, push=False, kernel=kernel, + neighbor_mode='grid' + ) + return result + + +def _wc_sph_pec_continuity_step(nnps, pa_index, dt, rho0, c0, p0, + periodic_bounds, push, alpha, beta, eos, + gamma, kernel, xsph_eps, adaptive_dt, cfl, + dt_min, dt_max, adaptive_dt_scale, + step_dt_max): + pa = nnps.particles[pa_index] + use_xsph = xsph_eps is not None and xsph_eps != 0.0 + eps = 0.0 if xsph_eps is None else xsph_eps + if push: + nnps.update(push=True) + save_wcsph_state(pa, dim=nnps.dim, device=nnps.device, push=push) + + # ADR-0004: both neighbor consumers walk the cell list directly, so the + # grid is the only spatial index built per half-stage (cached per update); + # no flat CSR neighbor list is materialized on the continuity hot path. + _ensure_property(pa, 'arho', nnps.device) + _apply_wcsph_eos(nnps, pa, rho0, c0, p0, eos, gamma) + compute_wcsph_accel_continuity( + nnps, pa_index, pa_index, alpha=alpha, beta=beta, eps=eps, c0=c0, + kernel=kernel, push=False + ) + if adaptive_dt: + dt = compute_wcsph_adaptive_timestep( + nnps, pa_index=pa_index, c0=c0, cfl=cfl, dt_min=dt_min, + dt_max=dt_max, push=False, neighbor_mode='grid' + ) + dt = min(float(dt) * float(adaptive_dt_scale), float(step_dt_max)) + wcsph_pec_stage( + pa, dt=dt, stage=0.5, dim=nnps.dim, xsph=use_xsph, + device=nnps.device, push=False + ) + wrap_periodic(pa, periodic_bounds, dim=nnps.dim, device=nnps.device) + nnps.update(push=False) + + _apply_wcsph_eos(nnps, pa, rho0, c0, p0, eos, gamma) + compute_wcsph_accel_continuity( + nnps, pa_index, pa_index, alpha=alpha, beta=beta, eps=eps, c0=c0, + kernel=kernel, push=False + ) + result = wcsph_pec_stage( + pa, dt=dt, stage=1.0, dim=nnps.dim, xsph=use_xsph, + device=nnps.device, push=False + ) + wrap_periodic(pa, periodic_bounds, dim=nnps.dim, device=nnps.device) + nnps.update(push=False) + return result, dt + + +def wc_sph_leapfrog_step(nnps, pa_index=0, dt=1.0e-4, rho0=1000.0, + c0=20.0, p0=0.0, periodic_bounds=None, + push=False, alpha=0.0, beta=0.0, + eos='isothermal', gamma=7.0, kernel='cubic', + xsph_eps=None, adaptive_dt=False, cfl=0.25, + dt_min=0.0, dt_max=np.inf, return_dt=False, + density_mode='summation', adaptive_dt_scale=1.0, + step_dt_max=np.inf): + """Run one minimal WCSPH KDK leapfrog step on the device. + + ``push`` defaults to ``False`` so repeated calls keep the Warp arrays as the + source of truth. Pass ``push=True`` only when host ParticleArray values were + intentionally changed before the step. + """ + pa = nnps.particles[pa_index] + if density_mode == 'continuity': + result, dt = _wc_sph_pec_continuity_step( + nnps, pa_index, dt, rho0, c0, p0, periodic_bounds, push, + alpha, beta, eos, gamma, kernel, xsph_eps, adaptive_dt, cfl, + dt_min, dt_max, adaptive_dt_scale, step_dt_max + ) + if return_dt: + return result, dt + return result + if density_mode != 'summation': + raise ValueError("density_mode must be 'summation' or 'continuity'") + if push: + nnps.update(push=True) + _compute_wcsph_acceleration( + nnps, pa_index, rho0, c0, p0, alpha, beta, push=push, + eos=eos, gamma=gamma, kernel=kernel, density_mode='summation' + ) + if adaptive_dt: + dt = compute_wcsph_adaptive_timestep( + nnps, pa_index=pa_index, c0=c0, cfl=cfl, dt_min=dt_min, + dt_max=dt_max, push=False, neighbor_mode='grid' + ) + dt = min(float(dt) * float(adaptive_dt_scale), float(step_dt_max)) + leapfrog_kick(pa, dt=0.5*dt, dim=nnps.dim, device=nnps.device, + push=False) + if xsph_eps is None or xsph_eps == 0.0: + leapfrog_drift( + pa, dt=dt, dim=nnps.dim, device=nnps.device, push=False + ) + else: + compute_xsph_correction( + nnps, pa_index, pa_index, eps=xsph_eps, push=False, + kernel=kernel, neighbor_mode='grid' + ) + leapfrog_drift_xsph( + pa, dt=dt, dim=nnps.dim, device=nnps.device, push=False + ) + wrap_periodic(pa, periodic_bounds, dim=nnps.dim, device=nnps.device) + nnps.update(push=False) + _compute_wcsph_acceleration( + nnps, pa_index, rho0, c0, p0, alpha, beta, push=False, + eos=eos, gamma=gamma, kernel=kernel, density_mode='summation' + ) + result = leapfrog_kick(pa, dt=0.5*dt, dim=nnps.dim, device=nnps.device, + push=False) + if return_dt: + return result, dt + return result + + +def _zero_device_props(pa, props): + """Zero the named device arrays in place (no host transfer).""" + for prop in props: + pa.gpu.get_device_array(prop).dev.zero_() + + +def wc_sph_dam_break_step(nnps, fluid_index=0, solid_indices=(1,), dt=1.0e-4, + rho0=1000.0, c0=10.0, p0=0.0, alpha=0.1, beta=0.0, + gamma=7.0, kernel='wendland', xsph_eps=0.5, + gx=0.0, gy=0.0, gz=-9.81, gravity_ramp=1.0, + adaptive_dt=False, cfl=0.25, dt_min=0.0, + dt_max=np.inf, adaptive_dt_scale=1.0, + step_dt_max=np.inf, push=False, return_dt=False): + """One 3D dam-break WCSPH continuity-density PEC step (ADR-0005). + + Multi-array: the fluid's acceleration and density rate sum over the fluid + plus every solid wall array; each wall integrates density from the fluid + only and is otherwise fixed -- walls start at rest with zero acceleration, + so the shared PEC stage leaves their position/velocity unchanged while their + density (hence pressure) responds to approaching fluid. Walls use + ``TaitEOSHGCorrection`` (clamped ``p >= 0``); the fluid uses Tait EOS. + Gravity is added to the fluid acceleration with an optional ``n_damp`` ramp + (``gravity_ramp`` in ``[0, 1]``). All neighbour traversal is grid-direct. + + This is additive to the backend: it composes the existing generated equation + blocks (run with ``accumulate_outputs=True`` over each source) and the + existing PEC stage; it does not modify any single-array path, so the 2D + elliptical-drop step is unchanged. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for wc_sph_dam_break_step") + + fluid_index = int(fluid_index) + solid_indices = [int(i) for i in solid_indices] + fluid = nnps.particles[fluid_index] + solids = [nnps.particles[i] for i in solid_indices] + arrays = [fluid] + solids + sources_for_fluid = [fluid_index] + solid_indices + use_xsph = xsph_eps is not None and xsph_eps != 0.0 + eps = 0.0 if xsph_eps is None else xsph_eps + dim = nnps.dim + device = nnps.device + + out_props = ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + for pa in arrays: + for prop in ('rho', 'p', 'cs') + out_props: + _ensure_property(pa, prop, device) + if push: + for pa in arrays: + pa.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', + 'u', 'v', 'w', *out_props) + nnps.update(push=push) + + for pa in arrays: + save_wcsph_state(pa, dim=dim, device=device, push=False) + + def accel(): + # EOS: fluid Tait; walls Tait-HG (clamp rho>=rho0 so wall p>=0). + compute_tait_eos(fluid, rho0=rho0, c0=c0, gamma=gamma, p0=p0, + device=device, push=False) + for s in solids: + compute_tait_eos_hg_correction(s, rho0=rho0, c0=c0, gamma=gamma, + device=device, push=False) + # Zero accumulators (walls keep zero accel -> they stay fixed). + for pa in arrays: + _zero_device_props(pa, out_props) + # Fluid: pressure + Monaghan AV + continuity summed over fluid + walls, + # fused into ONE kernel per source (single neighbour walk / single + # per-pair geometry for all three blocks instead of three). + for s_index in sources_for_fluid: + _run_equation_group(nnps, s_index, fluid_index, + list(_WCSPH_DAM_BREAK_FLUID_BLOCKS), + scalar_values={'alpha': alpha, 'beta': beta}, + kernel=kernel, neighbor_mode='grid', + accumulate_outputs=True) + # XSPH position correction from fluid neighbours only. + if use_xsph: + _run_equation_group(nnps, fluid_index, fluid_index, + [XSPHCorrection()], + scalar_values={'eps': eps}, kernel=kernel, + neighbor_mode='grid', accumulate_outputs=True) + # Walls: density rate from the fluid only. + for w_index in solid_indices: + _run_equation_group(nnps, fluid_index, w_index, + [ContinuityEquation()], kernel=kernel, + neighbor_mode='grid', accumulate_outputs=True) + # Gravity (ramped) into the fluid acceleration. + apply_body_force(fluid, gx=gx, gy=gy, gz=gz, dim=dim, + ramp=gravity_ramp, device=device, push=False) + + # Predictor half-stage (dt fixed for both stages, set adaptively here). + accel() + if adaptive_dt: + dt = compute_wcsph_adaptive_timestep( + nnps, pa_index=fluid_index, c0=c0, cfl=cfl, dt_min=dt_min, + dt_max=dt_max, push=False, neighbor_mode='grid' + ) + dt = min(float(dt) * float(adaptive_dt_scale), float(step_dt_max)) + for pa in arrays: + wcsph_pec_stage(pa, dt=dt, stage=0.5, dim=dim, + xsph=(use_xsph and pa is fluid), device=device, + push=False) + nnps.update(push=False) + + # Corrector half-stage. + accel() + for pa in arrays: + wcsph_pec_stage(pa, dt=dt, stage=1.0, dim=dim, + xsph=(use_xsph and pa is fluid), device=device, + push=False) + nnps.update(push=False) + + if return_dt: + return dt + return dt + + +def wc_sph_dam_break_rigid_step( + nnps, rigid_state, fluid_index=0, wall_indices=(1,), rigid_index=2, + dt=1.0e-4, rho0=1000.0, c0=10.0, p0=0.0, alpha=0.1, beta=0.0, + gamma=7.0, kernel='wendland', xsph_eps=0.5, gx=0.0, gy=0.0, + gz=-9.81, adaptive_dt=False, cfl=0.25, dt_min=0.0, + dt_max=np.inf, adaptive_dt_scale=1.0, step_dt_max=np.inf, + push=False, return_dt=False): + """One EPEC WCSPH step with deterministic Liu rigid coupling (ADR-0006). + + This is a sibling of :func:`wc_sph_dam_break_step`; fixed walls use the + existing PEC path while the rigid array is advanced only by its density + stage and device-resident 6-DOF RK2 state. + """ + if wp is None: # pragma: no cover + raise ImportError("warp is required for wc_sph_dam_break_rigid_step") + fluid_index = int(fluid_index) + rigid_index = int(rigid_index) + wall_indices = [int(i) for i in wall_indices] + fluid = nnps.particles[fluid_index] + walls = [nnps.particles[i] for i in wall_indices] + rigid = nnps.particles[rigid_index] + fixed_arrays = [fluid] + walls + fixed_sources = [fluid_index] + wall_indices + dim = nnps.dim + device = nnps.device + use_xsph = xsph_eps is not None and xsph_eps != 0.0 + eps = 0.0 if xsph_eps is None else xsph_eps + out_props = ('au', 'av', 'aw', 'arho', 'ax', 'ay', 'az') + + for pa in fixed_arrays: + for prop in ('rho', 'p', 'cs') + out_props: + _ensure_property(pa, prop, device) + for prop in ('rho', 'rho0', 'p', 'cs', 'arho', 'V', + 'fx', 'fy', 'fz', 'u', 'v', 'w'): + _ensure_property(rigid, prop, device) + if push: + for pa in fixed_arrays: + pa.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'p', 'cs', + 'u', 'v', 'w', *out_props) + rigid.gpu.push('x', 'y', 'z', 'h', 'm', 'rho', 'rho0', 'p', 'cs', + 'arho', 'V', 'fx', 'fy', 'fz', 'u', 'v', 'w') + nnps.update(push=push) + + if not getattr(rigid_state, 'number_density_initialized', False): + compute_rigid_number_density( + nnps, rigid_index, kernel=kernel, push=False) + rigid_state.number_density_initialized = True + + for pa in fixed_arrays: + save_wcsph_state(pa, dim=dim, device=device, push=False) + save_rigid_body_density(rigid, device=device, push=False) + save_rigid_body_state(rigid, rigid_state, push=False) + + def accel(): + compute_tait_eos(fluid, rho0=rho0, c0=c0, gamma=gamma, p0=p0, + device=device, push=False) + for wall in walls: + compute_tait_eos_hg_correction( + wall, rho0=rho0, c0=c0, gamma=gamma, + device=device, push=False) + compute_tait_eos_hg_correction( + rigid, rho0=rho0, c0=c0, gamma=gamma, + device=device, push=False) + + for pa in fixed_arrays: + _zero_device_props(pa, out_props) + _zero_device_props(rigid, ('arho',)) + initialize_rigid_body_force( + rigid, gx=gx, gy=gy, gz=gz, device=device, push=False) + + # Existing fluid + fixed-wall physics. The rigid body is deliberately + # excluded from this fused block so its continuity is not double-counted. + for src_index in fixed_sources: + _run_equation_group( + nnps, src_index, fluid_index, + list(_WCSPH_DAM_BREAK_FLUID_BLOCKS), + scalar_values={'alpha': alpha, 'beta': beta}, kernel=kernel, + neighbor_mode='grid', accumulate_outputs=True) + # Rigid contribution to fluid density exactly once, then pressure + # acceleration + equal-and-opposite body force in deterministic passes. + _run_equation_group( + nnps, rigid_index, fluid_index, [ContinuityEquation()], + kernel=kernel, neighbor_mode='grid', accumulate_outputs=True) + compute_liu_fluid_rigid_coupling( + nnps, fluid_index, rigid_index, kernel=kernel, push=False) + + if use_xsph: + _run_equation_group( + nnps, fluid_index, fluid_index, [XSPHCorrection()], + scalar_values={'eps': eps}, kernel=kernel, + neighbor_mode='grid', accumulate_outputs=True) + for wall_index in wall_indices: + _run_equation_group( + nnps, fluid_index, wall_index, [ContinuityEquation()], + kernel=kernel, neighbor_mode='grid', accumulate_outputs=True) + _run_equation_group( + nnps, fluid_index, rigid_index, [ContinuityEquation()], + kernel=kernel, neighbor_mode='grid', accumulate_outputs=True) + apply_body_force(fluid, gx=gx, gy=gy, gz=gz, dim=dim, + device=device, push=False) + + accel() + if adaptive_dt: + dt = compute_wcsph_adaptive_timestep( + nnps, pa_index=fluid_index, c0=c0, cfl=cfl, dt_min=dt_min, + dt_max=dt_max, push=False, neighbor_mode='grid') + dt = min(float(dt) * float(adaptive_dt_scale), float(step_dt_max)) + for pa in fixed_arrays: + wcsph_pec_stage(pa, dt=dt, stage=0.5, dim=dim, + xsph=(use_xsph and pa is fluid), device=device, + push=False) + rigid_body_density_stage(rigid, dt=dt, stage=0.5, device=device) + rigid_body_rk2_stage(rigid, rigid_state, dt=dt, stage=0.5, push=False) + nnps.update(push=False) + + accel() + for pa in fixed_arrays: + wcsph_pec_stage(pa, dt=dt, stage=1.0, dim=dim, + xsph=(use_xsph and pa is fluid), device=device, + push=False) + rigid_body_density_stage(rigid, dt=dt, stage=1.0, device=device) + rigid_body_rk2_stage(rigid, rigid_state, dt=dt, stage=1.0, push=False) + nnps.update(push=False) + + if return_dt: + return dt + return dt + + +def wc_sph_euler_step(nnps, pa_index=0, dt=1.0e-4, rho0=1000.0, + c0=20.0, p0=0.0, alpha=0.0, beta=0.0, + eos='isothermal', gamma=7.0, kernel='cubic'): + """Run one minimal WCSPH-style device step. + + The step computes summation density, pressure, optional artificial + viscosity, and a simple Euler velocity/position update on the device. + """ + pa = nnps.particles[pa_index] + compute_summation_density( + nnps, pa_index, pa_index, kernel=kernel, neighbor_mode='grid' + ) + if eos == 'isothermal': + compute_isothermal_eos( + pa, rho0=rho0, c0=c0, p0=p0, device=nnps.device, push=False + ) + _ensure_sound_speed(pa, c0, nnps.device) + elif eos == 'tait': + compute_tait_eos( + pa, rho0=rho0, c0=c0, gamma=gamma, p0=p0, + device=nnps.device, push=False + ) + else: + raise ValueError("EOS must be 'isothermal' or 'tait'") + compute_pressure_gradient( + nnps, pa_index, pa_index, push=False, kernel=kernel, + neighbor_mode='grid' + ) + if alpha != 0.0 or beta != 0.0: + compute_artificial_viscosity( + nnps, pa_index, pa_index, alpha=alpha, beta=beta, c0=c0, + push=False, kernel=kernel, neighbor_mode='grid' + ) + return euler_step(pa, dt=dt, dim=nnps.dim, device=nnps.device, + push=False) diff --git a/setup.py b/setup.py index 44a93ae38..251ffabad 100644 --- a/setup.py +++ b/setup.py @@ -268,7 +268,16 @@ def get_zoltan_args(): """ global HAVE_MPI, USE_ZOLTAN zoltan_include_dirs, zoltan_library_dirs = [], [] - if not HAVE_MPI or not HAVE_ZOLTAN: + if not HAVE_MPI: + return zoltan_include_dirs, zoltan_library_dirs + if not HAVE_ZOLTAN: + # PyZoltan is not installed, so the parallel/Zoltan extension cannot be + # cythonized (it cimports pyzoltan's .pxd headers). Disable the parallel + # build rather than failing -- the serial/GPU build does not need it. + print("*" * 80) + print("PyZoltan not found; skipping the MPI/Zoltan parallel extension.") + print("*" * 80) + HAVE_MPI = False return zoltan_include_dirs, zoltan_library_dirs # First try with the environment variable 'ZOLTAN' zoltan_base = get_zoltan_directory('ZOLTAN')