Why is cuda sub-optimal compared to SPIR-V? I don't think I know the internals enough to understand if it's supposed to be obvious why one is better than the other.

I'm currently sitting and learning cuda for ML purposes, so happy to get more educated :)

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jb1991 2 days ago | parent [-]

Just depends on how the manufacturer of the GPU handles code written in different languages. For example, what level of API access, what level of abstraction, and how is the source compiled i.e. how optimized is it. For example, on an apple GPU, you’ll see benchmarks that openCL and metal can vary depending on the tasks.

	▲	embedding-shape 2 days ago \| parent \| next [-]
		Right, but that'd depend a lot on the context, task, hardware and so on. What parent said seemed more absolute and less relative, almost positing it as there is no point in using cuda (since it's "sub-optimal" and people should use SPIR-V obviously. I was curious in the specifics about that.
	▲	sigbottle 2 days ago \| parent \| prev [-]
		I mean, nvidia exposes some pretty low level primitives, and you can always fiddle with the PTX as deepseek did.