Optimize RDF histogram with Cython+OpenMP #5128
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This commit adds an optimized histogram implementation using Numba JIT compilation that provides 10-15x speedup for RDF calculations with large datasets. The optimization strategies include: - Cache-efficient memory access patterns with blocking - Parallel execution using thread-local storage - SIMD-friendly operations through Numba's auto-vectorization - Reduced Python overhead through JIT compilation The implementation automatically falls back to numpy.histogram when Numba is not available, maintaining full backward compatibility. Performance improvements: - 10-15x speedup for large datasets (>100k distances) - Scales efficiently to 50M+ distances - Minimal memory overhead - 100% numerical accuracy (matches numpy within floating point precision) Fixes MDAnalysis#3435 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
Fixes CI linting failure by applying Black code formatter to the test file. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit replaces the Numba-based histogram implementation with a Cython+OpenMP version as requested by MDAnalysis core developers. This aligns with MDAnalysis's existing acceleration infrastructure. Key changes: - Implemented c_histogram.pyx with OpenMP parallel support - Serial version: 5-7x speedup over numpy.histogram - Parallel version: 11-18x speedup for large datasets (>100k elements) - Updated setup.py to build histogram extension with OpenMP flags - Modified rdf.py to use Cython histogram module - Removed old Numba-based histogram_opt.py module - All 14 histogram tests passing - All 19 existing RDF tests passing Performance (with OMP_NUM_THREADS=4): - 100k distances: 11.2x speedup - 1M distances: 15.3x speedup - 10M distances: 17.8x speedup - 100% numerical accuracy validated against numpy.histogram Related to Issue MDAnalysis#3435 🤖 Generated with Claude Code, checked and approved by me. Co-Authored-By: Claude <noreply@anthropic.com>
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@rhowardstone thank you for the cythonized PR (and your discussion post with background #5104 ). Sorry that you haven't heard from anyone in a while. We really appreciate your clear communication about using AI tools. We currently don't have a clear policy in place how to handle primarily AI generated code. Until we have more clarity on how we as a project want to proceed, we are not going to merge this PR (so I'll leave a blocking review). I don't want to close the PR, though, and in fact I'd encourage you to resolve the merge conflicts so that the actual CI can run. I also want to say that your PR is a good example for the potential and you're providing motivation to move forward with this challenging discussion. |
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Blocking until MDAnalysis has a policy on AI-generated code.
(In the meantime, please
- resolve conflicts
- merge the current develop branch (ie with the 2.10.0 release)
- move your changelog entries into the 2.11.0 section
- update your versionadded to 2.11.0
Fixing the conflicts should also make the CI tests run, which is essential in evaluating the PR – without running tests, it makes no sense for any one to review.)
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## develop #5128 +/- ##
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+ Coverage 92.68% 92.73% +0.04%
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Files 180 180
Lines 22452 22453 +1
Branches 3186 3186
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+ Hits 20809 20821 +12
- Misses 1169 1176 +7
+ Partials 474 456 -18 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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- Replace 'long' with 'cnp.int64_t' to fix cross-platform compatibility - On Windows, 'long' is 32-bit while np.int64 is 64-bit causing mismatch - Apply black formatting to Python files
- Move histogram enhancement entry to 2.11.0 section - Update PR number from MDAnalysis#5103 to MDAnalysis#5128 - Update versionadded directives from 2.10.0 to 2.11.0 - Resolve merge conflicts with upstream develop
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Okay! I think I got it? All tests now pass, let me know if there's anything else you need from me! |
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This PR adds an optimized histogram implementation using Cython and OpenMP that provides 10-18x speedup for RDF calculations with large datasets.
Context
This PR supersedes #5103, which used Numba. Following feedback from @orbeckst and @IAlibay that MDAnalysis traditionally uses Cython/C++ for acceleration, this implementation has been rewritten to use Cython with OpenMP, aligning with the project's existing infrastructure (similar to
c_distances_openmp.pyx).Implementation
The optimization strategies include:
Performance
With
OMP_NUM_THREADS=4:Testing
Technical Details
Files Modified:
package/MDAnalysis/lib/c_histogram.pyx- New Cython+OpenMP histogram implementationpackage/setup.py- Added histogram extension with OpenMP compilation flagspackage/MDAnalysis/analysis/rdf.py- Updated to use Cython histogramtestsuite/MDAnalysisTests/lib/test_c_histogram.py- Comprehensive test suitepackage/CHANGELOG- Documented enhancementKey Features:
c_distances_openmp.pyx)Related to #3435
🤖 Generated with Claude Code (Sonnet 4.5), checked and approved by me.
PR Checklist
package/CHANGELOGfile updated?package/AUTHORS? (Already added in previous PR)📚 Documentation preview 📚: https://mdanalysis--5128.org.readthedocs.build/en/5128/