Add pure-Python solver backend for simplified installation#245
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jameslehoux merged 4 commits intomasterfrom Apr 4, 2026
Merged
Add pure-Python solver backend for simplified installation#245jameslehoux merged 4 commits intomasterfrom
jameslehoux merged 4 commits intomasterfrom
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When the compiled C++ backend (_core) is unavailable, OpenImpala now transparently falls back to a pure-Python solver that uses SciPy sparse matrices on CPU or CuPy on GPU. This enables `pip install openimpala` to work without any compiled extensions, CUDA toolkit, or MPI — matching the install simplicity of packages like TauFactor. The solver implements the same algorithm as the C++ HYPRE path: - 7-point stencil Laplacian with harmonic-mean face coefficients - Dirichlet BCs at inlet/outlet, zero-flux Neumann on sides - Sparse CG solve (CuPy GPU or SciPy CPU) - Flood-fill percolation via scipy.ndimage.label - Flux integration → D_eff → tau = active_vf / D_eff Validated against all three analytical benchmarks: - Uniform block: tau = (N-1)/N — exact match for N=8,16,32,64 - Volume fraction: exact counting - Percolation: correct connected/disconnected detection New/modified files: - python/openimpala/_solver.py — pure-Python solver engine - python/openimpala/session.py — graceful fallback when _core missing - python/openimpala/facade.py — auto-routing to Python or C++ backend - python/openimpala/__init__.py — better error messages for _core attrs - pyproject.toml — scipy dependency, cupy optional extra - python/CMakeLists.txt — install _solver.py in wheels https://claude.ai/code/session_01RKnn97qiD7sbCeABHH3eQk
The CPU wheel previously compiled _core.so with AMReX+HYPRE+MPI, requiring users to have MPI installed at runtime. Now `pip install openimpala` ships a pure-Python wheel (py3-none-any) with only numpy+scipy as dependencies. GPU acceleration is automatic when CuPy is present (e.g. Colab GPU runtime). Two install paths: - pip install openimpala → pure Python, SciPy/CuPy solver - pip install openimpala-cuda → compiled C++ HYPRE backend (HPC) Changes: - python/pyproject.toml: new setuptools-based build config for pure-Python wheel - .github/workflows/pypi-wheels-cpu.yml: simplified to `python -m build` - pyproject.toml: removed [gpu] optional extra (CuPy auto-detected at runtime) https://claude.ai/code/session_01RKnn97qiD7sbCeABHH3eQk
All tutorials and notebooks now use `pip install openimpala` instead of the old `openimpala-cuda --find-links ... nvidia-cuda-*` pattern. The pure-Python package auto-detects CuPy for GPU acceleration. Updated files: - All 7 tutorials: simplified install cells - notebooks/profiling_and_tuning.ipynb: removed MPI/CUDA deps - README.md, docs/getting-started.md, docs/index.rst: primary install is now `pip install openimpala`, openimpala-cuda moved to Advanced/HPC - docs/user-guide/gpu.md: rewritten for CuPy auto-detection - paper.md: updated distribution description https://claude.ai/code/session_01RKnn97qiD7sbCeABHH3eQk
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Performance Benchmark Results
Fastest solver: bicgstab at 64³ (0.3741s) Benchmark: uniform block (analytical τ = (N-1)/N) |
Code Coverage ReportGenerated by CI — coverage data from gcovr |
Codecov Report✅ All modified and coverable lines are covered by tests. 📢 Thoughts on this report? Let us know! |
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