[smhx]

Hi I am a PyTorch maintainer.

NVIDIA has dropped CUDA support for macOS: http://www.cgchannel.com/2019/11/nvidia-drops-macos-support-...

This was pretty evident for a few years, and it's one of the top reasons for us to not provide official binaries with CUDA support -- the maintainer overhead was way too much. We did work to make sure it still builds with CUDA support from source (with a contbuild) but once CUDA 10.3 or 11 releases, we have to drop that too.

[zackmorris]

Ah thanks for that. One of my biggest concerns right now is that since SIMD won out in the performance wars, and has come to be dominated by the video game industry and proprietary players like NVIDIA, that we are missing out on a whole possible tree of evolution in computer science.

For one, that we don't have easy access to MIMD, so we can't easily/cheaply experiment with our own simulations for things like genetic algorithms.

20 years ago I wanted to go into AI research and make a multicore FPGA (say 1000+ cores) where each one could run its own instance of an OS, or at the very least an isolated runtime for something like Lisp. But the world has gone a completely different direction, and that's great and everything with all the recent advances in machine learning, but it's like comparing rasterization (what we have) to ray tracing (what we could have had). Current implementations are orders of magnitude more complex than they need to be. I've written about this a bunch:

https://news.ycombinator.com/item?id=17759391

https://news.ycombinator.com/item?id=17419917

So I guess short of this, I hope that PyTorch can at least provide a cross-platform performant SIMD implementation. Which I had hoped OpenCL would be, but maybe it's too much like OpenGL and we need something a level of abstraction higher for easier vector processing without all the worrying about buffers and moving between CPU and GPU.