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Parallelism in a NumPy-based program
- 1. Understanding and optimizing parallelism in NumPy-based programs Ralf Gommers 21 April 2022
- 2. First make it work, then make it fast >>> %timeit main() 50.1 ms ± 1.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) >>> # ... perform some optimizations >>> %timeit main() 9.58 ms ± 22.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) >>> # break out your profiler (e.g., py-spy), optimize some more >>> %timeit main() 2.83 ms ± 30.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
- 4. Approaches for performant numerical code (single-threaded) Vectorization Use compiled code Python compilers Python interpreters Pythran CPython Plus Cinder, Pyston, and more -- very experimental, and limited gains for numerical code
- 6. A key issue: oversubscription Package A sees N CPU cores, and decides to use them all:
- 7. A key issue: oversubscription Package B, which uses package A, or the end user decides to use multiprocessing, 1 process per core: The more CPU cores a machine has, the worse the effect is!
- 8. Parallel APIs & behavior: NumPy NumPy is single-threaded, no code in NumPy is written for parallel execution. However, most numpy.linalg functions (those using BLAS or LAPACK) execute in parallel. They use all available cores on a machine. NumPy does release the GIL wherever it can. numpy.random has specific APIs to allow users to: (a) Obtain independent streams for random number generation across processes (local or distributed) (b) Perform multithreaded random number generation
- 9. Parallel APIs & behavior: SciPy SciPy is single-threaded by default (same as NumPy) Calls to functionality using BLAS or LAPACK is again multithreaded: ● primarily in scipy.linalg and scipy.sparse.linalg, ● also higher-level functionality using linear algebra under the hood: kernel density estimation, multivariate distributions etc. in scipy.stats, vector quantization in scipy.cluster, interpolators in scipy.interpolate, optimizers in scipy.optimize, and more Some APIs have a workers=1 keyword, which allows the user to control the number of processes or threads. Or pass in a custom Pool. scipy.fft provides a context manager:
- 10. Parallel APIs & behavior: SciPy An example using workers=:
- 11. Parallel APIs & behavior: scikit-learn Scikit-learn is mostly single-threaded by default. However, more and more functionality uses OpenMP for automatic parallelization. This defaults to the number of virtual (not physical) CPU cores. Many scikit-learn APIs offer a n_jobs= keyword to let user enable multiple threads or processes via joblib. Scikit-learn implements fairly complex control of NumPy/SciPy’s BLAS and LAPACK libraries to prevent oversubscription in the presence of multiprocessing on top of multi-threading. This is done via the threadpoolctl package.
- 12. Controlling parallelism - packages Dependencies (Conda)
- 13. Controlling parallelism - packages Dependencies (PyPI)
- 14. Controlling parallelism - packages Conda PyPI
- 15. Tuning the default behavior Default behavior is inconsistent: too aggressive for linear algebra, and too conservative for workers (SciPy) and n_jobs (scikit-learn)! OpenBLAS, MKL and OpenMP don’t have a nice API, only environment variables: For scikit-learn you can explicitly choose a backend (but defaults are usually fine):
- 16. Tuning the default behavior NumPy, SciPy and scikit-learn all recommend using threadpoolctl in case you want more granular control over threading behavior of BLAS, LAPACK and OpenMP libraries (or cannot set environment variables):
- 17. A pitfall on multi-tenant machines Multi-tenant machines: N “vCPU” (virtual CPU) cores for you, M in total. CircleCI gives you 2 cores for a CI job, on a 64 core machine (and os.cpu_count() reports 64). Set OPENBLAS_NUM_THREADS=2 to avoid problems! GitHub Actions, Azure DevOps and other services are better behaved. The impact can be severe:
- 18. Parallel random number generation
- 19. Parallel random number generation First what not to do – simply drawing random numbers in different subprocesses will give you the same numbers in each process:
- 20. Parallel random number generation Use SeedSequence to obtain independent streams easily:
- 21. Parallel random number generation Second option: use the .jumped() method of BitGenerator instances to obtain independent streams easily:
- 22. Parallel random number generation
- 23. Where is NumPy going - technical Interoperability Array API standard support Extensibility Easier custom dtypes Performance SIMD acceleration on: x86, arm64, PPC, …? C++ Just dipping our toes in the water here - so far it was just Python and C Platform support PPC, AIX, s390x, cross-compiling to embedded ARM systems, ... Type annotations Main namespace annotations just completed Note what is not on this list: auto-parallelization
- 24. Resources to learn more Scikit-learn: https://scikit-learn.org/stable/computing/parallelism.html https://joblib.readthedocs.io/en/latest/parallel.html SciPy: http://scipy.github.io/devdocs/dev/toolchain.html#openmp-support http://scipy.github.io/devdocs/search.html?q=workers NumPy: https://numpy.org/doc/stable/reference/random/parallel.html Relevant paper: Composable Multi-Threading and Multi-Processing for Numeric Libraries
- 25. Find me at: [email protected], rgommers, ralfgommers Thank you!