ufunc.at (and possibly other methods) slow

I noticed that in many of my codes, seemingly harmless lines like

numpy.add.at(target, idx, vals)

take a large share of the runtime. I investigated and found that one gets a speed-up of a factor of 40 (!) by simply moving the critical code to C++.

(Not sure what’s actually done in numpy.ufunc.) I put this into a very simple module, https://github.com/nschloe/fastfunc, so feel free to take some code out of there.

[jaimefrio]

Here I explained what makes ufunc.at slow. Your code here takes a few shortcuts that are not really available to NumPy:

• You have hardcoded the operations you have implemented, +, -, * and /, which is OK for most use cases, but does not support generic operations. If you had a generic function that took a function pointer that performed the operation, you would have more generic, albeit slower code. That is what NumPy does.

• You have assumed that the a and b arrays are of the same type, which is not always the case. Not sure if your code can handle cases where these don’t match, but that’s a liberty NumPy can’t take either.

• I’m not sure how much magic pybind11 packs, but your iteration scheme seems deceptively simple for all the subtleties of fancy indexing that ufunc.at supports. Are you assuming that some/all of the arrays are 1D? That’s again something NumPy isn’t free to do.

[eric-wieser]

It would be great if we could start using C++ template instead of the .c.src stuff within numpy, but I imagine there are distribution issues with that…