[cshenton]

I personally find the experience of writing GPU compute code pretty nice on graphics APIs. The interface is pretty much the same “dispatch a 1-3D set of 1-3D work group indices”.

The main pain points vs dedicated compute stuff like cuda is libraries and boilerplate to manage memory and launch kernels.

[synergy20]

and, how to make the kernel and memory-allocation code working with tensorflow/pytorch, GPGPU is really now just a few libraries made for Tensorflow and Pytorch to invoke, same as CUDA, as far as ML is concerned.