cpython VS pythran

Not to forget CPython's own faster-cpython project which aims at JIT compiling. [0]

Also JAX[1], PyTorch[2] come with JIT compilation specifically aimed at GPU kernels "fusing" multiple higher-level operation

And NumPy/Scipy (also) uses Pythran[3], an AOT compiler not too unsimilar to Numba.

[0] https://github.com/faster-cpython/cpython

Presumably development will be taking place on this organization's fork of cpython?

https://github.com/faster-cpython/cpython

Looks like they've also got a repo with some tools for profiling bytecode:

https://github.com/faster-cpython/tools

Numpy and Scipy are good reasons. Unfortunately Scipy does not even compile on FreeBSD lately, and I have opened three issues about it against Scipy and Pythran (and the fix was with xsimd).

https://github.com/serge-sans-paille/pythran/issues/2070

In someone lands here seeking a maintained compiler for Python, there's a lot, on top of my head:

- Pythran (https://pythran.readthedocs.io) (ahead of time compiler)

Yes, I mean Pythran ( https://github.com/serge-sans-paille/pythran ). Thank you.

Was Nuitka better? Pythran is quite simple to install and use in Jupyter.

The only project that compares 1:1 is Pythran: https://github.com/serge-sans-paille/pythran

Pythran is fairly nice, and it really does work. I tried it last year and it compiles down to modifiable templated C++. I was able to use it to build Python for a highly specialized environment.

All the others compile down to dynamically linked binaries, and that just puts them in the "other" box.

You might want to contact the author of Pythran [1], maybe something can be learned from what they do.

[1] https://github.com/serge-sans-paille/pythran/commits/master

[1] https://github.com/serge-sans-paille/pythran