While I agree, that CUDA is the best in class API for GPU programming, OpenCL, Vulkan compute shaders and Sycl are alternatives that are usable. I'm for example, using compute shaders for writing GPGPU algorithms that work on Mac, AMD, Intel and Nvidia. It works ok. The debugging experience and ecosystem sucks compared to CUDA, but being able to run the algorithms across platforms is a huge advantage over CUDA.
How are you writing compute shaders that work on all platforms, including Mac? Are you just writing Vulkan and relying on MoltenVK?
AFAIK, the only solution that actually works on all major platforms without additional compatibility layers today is OpenCL 1.2 - which also happens to be officially deprecated on MacOS, but still works for now.
WebGPU has no support for tensor cores (or their Apple Silicon equivalents). Vulkan has an Nvidia extension for it, is there any way to make MoltenVK use simdgroup_matrix instructions in compute shaders?
Technically, OpenCL can also include inline PTX assembly in kernels (unlike any compute shader API I've ever seen), which is relevant for targeting things like tensor cores. You're absolutely right about the language limitation, though.
Generally, the reason to bother with this approach is if you have a project that only needs tensor cores in a tiny part of the code and otherwise benefits from the cross platform nature of OpenCL, so you have a mostly shared codebase with a small vendor-specific optimization in a kernel or two. I've been in that situation and do find that approach valuable, but I'll be the first to admit the modern GPGPU landscape is full of unpleasant compromises whichever way you look.
Well it really depends on the kind of work you're doing. My (non-AI) software allows users to run my algorithms on whatever server-side GPU or local device they have. This is a big advantage IMO.
