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Pythran – a compiler for Python scientific kernels – release
- mratsim 7y agoI know Pythran from the Julia challenge [1] but you should link to some docs / examples / tutorials in this thread because the release notes doesn't do justice of what Pythran can do. [1] https://github.com/SimonDanisch/julia-challenge/pull/4 https://github.com/SimonDanisch/julia-challenge/pull/4
- contravariant 7y agoIf you have some time would you mind sharing some of your thoughts about Pythran? I'm curious but wouldn't really know where to start looking.
- chenzhekl 7y agoHow does it compare to Numba and Cython?
- mattip 7y agoHere is a nice comparison https://flothesof.github.io/optimizing-python-code-numpy-cython-pythran-numba.html https://flothesof.github.io/optimizing-python-code-numpy-cyt... Tl;dr: pythran is very similar to numba but blazingly fast on cpu
- mlthoughts2018 7y agoThe Cython example in that link is actually not a fair comparison, since it still forces the numpy ndarray type in the signature. Instead it should use typed memoryviews [0], which are faster and can avoid more cases that will rely on the GIL accidentally (such as when an ndarray has to be treated as a Python object). [0]: https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html https://cython.readthedocs.io/en/latest/src/userguide/memory...
- serge-ss-paille 7y agoMore materials for the curious: Some benchmarks here: http://serge-sans-paille.github.io/pythran-stories/testing-pythran-on-random-kernels.html http://serge-sans-paille.github.io/pythran-stories/testing-p... Some more benchmark you can run on you own: https://github.com/serge-sans-paille/numpy-benchmarks/ https://github.com/serge-sans-paille/numpy-benchmarks/ A comparison with Julia and native code: http://serge-sans-paille.github.io/pythran-stories/micro-benchmarking-julia-c-and-pythran-on-an-economics-kernel.html http://serge-sans-paille.github.io/pythran-stories/micro-ben...