Hacker News Reader

Comment by dna_polymerase | Hacker News Reader

It is quite weird to talk about all the frameworks which are built on top of CUDA eventually, but not talking about Rocm or OpenCL.

OpenAI's Triton compiles down to CUDA atm (if I read their github right), and only supports Nvidia GPUs.

PyTorch 2.0's installation page only mentions CPU and CUDA targets, therefor it's effectively all Nvidia GPUs.

While all the frameworks and abstractions could offer other back-ends in theory the story of anything ML related on the other big name in the industry, AMD, is still poor.

If anybody looses business because of bad decisions it is AMD, not Nvidia, who lead the whole industry. I am not convinced that anything will change in the near future.

View on HN

From https://news.ycombinator.com/item?id=32904285 re: AMD ROcm, HIPIFY, :

> AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni... [...]

> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced. [...]

From https://github.com/RadeonOpenCompute/clang-ocl :

> RadeonOpenCompute/OpenCL compilation with clang compiler

A better overview from the docs: "Machine Learning and High Performance Computing Software Stack for AMD GPU" https://rocmdocs.amd.com/en/latest/Installation_Guide/Softwa...

AMD's position on machine learning has been fascinating to watch. While the company has gone through some cash strapped years, investing even some marginal effort into the ML space would have paid off handsomely in data center sales. Instead they let nvidia run away with it, pushing out a half-baked ROCm, which bizarrely supports such a tiny subset of cards it is farcical: With nvidia devs with a standard consumer GPU are building solutions and testing models, and when it's time to push big staying with nvidia is just the easy choice, while AMD apparently tries to upsell for even marginal support.

Apple has done more to see Apple Silicon supported in the ML space in almost no time than AMD did over the past decade. Apple obviously has more resources, but they don't have a 1000 person team working on this, I suspect, but instead it's a very commando, efficient operation. They got a small number of dedicated resources committed to bringing support and enabling the community, and it happened.

Reply on news.ycombinator.com