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Pythran – a compiler for Python scientific kernels – release (serge-sans-paille.github.io)
60 points by serge-ss-paille 2421 days ago
3 comments

I 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

If 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.
More materials for the curious:

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...

How does it compare to Numba and Cython?
Here is a nice comparison https://flothesof.github.io/optimizing-python-code-numpy-cyt...

Tl;dr: pythran is very similar to numba but blazingly fast on cpu

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).

[0]: https://cython.readthedocs.io/en/latest/src/userguide/memory...