[aldanor]

NumPy is a C library with Python frontend, moreover lots of functionality based on other existing C libraries like blas etc.

PyTorch, quoting themselves, is a Python binding into a monolithic C++ framework; also optionally depending on existing libs like mkl etc.

> You can write performant code in any language if you try.

Unfortunately, only to a certain extent. Sure, if you just need to multiply a handful of matrices and you want your blas ops to be blas'ed where the sheer size of data outweighs any of your actual code, it doesn't really matter. Once you need to implement lower-level logic, ie traversing and processing the data in some custom way, especially without eating extra memory, you're out of luck with Python/numpy and the rest.

[benrutter]

> NumPy is a C library with Python frontend

I guess this is a pretty legitimate take, but in that case VectorDB looks like (from the got repo) it makes huge use of libraries like pytorch and numpy.

If numpy is fast but "doesn't count" because the operations aren't happening in python, then I guess VectorDB isn't in python either by that logic?

On the other hand, if it is in Python despite shipping operations out to C/C++ code, then I guess numpy shows that can be an effective approach?

[bee_rider]

BLAS can be implemented in any language. In terms of LOC, most BLAS might be C libraries, but the best open source BLAS, BLIS, is totally structured around the idea of writing custom, likely assembly, kernels for a platform. So, FLOPs-wise it is probably more accurate to call it an assembly library.

LAPACK and other ancillary stuff could be Fortran or C.

Anyway, every language calls out to functions and runtimes, and compiles (or jits or whatever) down to lower level languages. I think it is just not that productive to attribute performance to particular languages. Numpy calls BLAS and LAPACK code, sure, but the flexibility of Python also provides a lot of value.

How does Numba fit into this hierarchy?