[jjoonathan]

My impression was that pytorch focused on linear algebra / deep learning. The reason I was playing with numbacuda in the first place was because part of my problem did not fit nicely into a (dense) linear algebra framework, so numbacuda's custom kernel support seemed attractive. Does pytorch have a good low-level kernel library? Or sparse linear algebra library?

I love Julia, but I haven't managed to convert anyone else on my team and I already spent my informal exploration budget for the GPU project on nubacuda, so JuliaGPU will have to wait for another time. I'll be sure to keep it in mind, though!

How is the CUDA debug/perf story with Julia? Does it play nice with the nvidia tooling?

[elcritch]

Ah, that makes sense. I've only dabbled a little with DNN's recently, but pytorh/tensorflow seemed very targeted toward deep learning. Generic tools seem more useful to me. What are you doing with fft's?

I haven't dug too deep with CudaNative / Cuarray to understand the state of Julia perf debugging. Though here's one post on the topic:

https://discourse.julialang.org/t/cudanative-is-awesome/1786...

In general It's been very pleasant experimenting with gpu programming in Julia. I couldn't quite grok tensorflow code, and it's cool to just declare a Julia array and send it the GPU.