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Free-threaded CPython is ready to experiment with!
(labs.quansight.org)
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this post was submitted on 13 Jul 2024
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Programming
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Nope, if you're working on large arrays of data you can get significant speed ups using well optimised BLAS functions that are vectorised (numpy) which beats out simply written c++ operating on each array element in turn. There's also Numba which uses LLVM to jit compile a subset of python to get compiled performance, though I didnt go to that in this case.
You could link the BLAS libraries to c++ but its significantly more work than just importing numpy from python.
Numpy is written in C.
Numba is interesting... But a) it can already do multithreading so this change makes little difference, and b) it's still not going to be as fast as C++ (obviously we don't count the GPU backend).
So you get the best of both worlds then: the speed of C and the ease of use of Python.
Sure but that's not relevant to the current discussion. The point is that removing the GIL doesn't affect Numpy because Numpy is written in C.