cpython VS codon

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

https://github.com/exaloop/codon/blob/develop/LICENSE

Here are some others: https://github.com/search?q=%22Business+Source+License%22+%2...

I found at least one project that managed to compile python AOT to LLVM https://github.com/exaloop/codon. Even if LLVM is more expressive than Dart Kernel, that should at least be some evidence that this might not be too impractical.

Their fannkuch benchmark seems to be a bit dishonest. They claim an enormous perf delta on https://exaloop.io/benchmarks.html but fannkuch uses factorial a lot and they define factorial with a very small (n=20) table: https://github.com/exaloop/codon/blob/fb461371613049539654c1...

Disclaimer: I've worked on several Python runtimes and compilers, but I'm not by any means out to get Codon. Just happened across this by accident while looking at their inline LLVM, which is neat.

If you literally want Python, but compiled ... Look at Codon: https://github.com/exaloop/codon

Another "Python with high-performance compiled builds" would be https://github.com/exaloop/codon.

This is the project being discussed: https://github.com/exaloop/codon

Try Codon? https://github.com/exaloop/codon

https://github.com/exaloop/codon might save you from the rewrite.