Re:Pythran – a compiler for Python scientific kernels – release
by mlthoughts2018 on Thursday November 07, 2019 @06:54AM
(#21472345)
The 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).
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
Some benchmarks here: 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/
A comparison with Julia and native code: http://serge-sans-paille.github.io/pythran-stories/micro-ben...
Tl;dr: pythran is very similar to numba but blazingly fast on cpu
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/memory...