No, plain python code: https://github.com/modularml/mojo/blob/5ce18c47a27c0c4123de1...
I'd like to know how fast numpy is here, but they didn't compare... which is weird because that's what almost everyone would use.
I'd like to know how fast numpy is here, but they didn't compare... which is weird because that's what almost everyone would use.
If you ask your average Python programmer what “pure Python” means they’d think numpy is included.
Their Mojo code just does the same optimizations numpy certainly has.
There's also the issue of doing complicated work with NumPy and you start looping and revert back to slow (pure) Python because you are crossing the NumPy/ Python interface. Tools like Cython, Numba, and probably Mojo help to solve this.
But nobody doing any significant numeric work would ever consider a "pure" python matrix multiply meaningful. It's disingenuous to present that as your comparison, at least without also including the way it's actually done in practice.
Honestly, it undermines their presentation with anyone involved in the area.
It's like claiming that your code is faster than MATLAB's for loops. Why write gemm yourself?
Even if this language is a good idea, I worry that they don't seem to understand the audience.
Not too dissimilar to Swift, which was launched as being faster than C/C++.