Alternative(s) to run CUDA on non-Nvidia hardware

woctordho•about 1 hour ago

There's nothing wrong to run CUDA on non-Nvidia hardware. CUDA has an interface that is reasonably well-designed, well-documented/reverse-engineered, and battle-tested for decades. What we need is not to invent another interface just under the name of 'open standard', but to implement the same interface. ROCm is exactly doing this, and so are other hardware SDKs such as MooreThread and Alibaba T-Head.

luciana1u•about 1 hour ago

every CUDA alternative follows the same arc: bold launch, works for 3 operations, then a Discord server where the last message is 'any updates?' from 2024

msond•12 minutes ago

Actually we launched in 2024 and the last message in our discord is definitely not that: https://discord.gg/KNpgGbTc38

pjmlp•about 2 hours ago

Most of these "alternatives" focus on CUDA C++, and overlook what actually makes CUDA interesting.

Already in 2020,

https://developer.nvidia.com/blog/cuda-refresher-the-gpu-com...

mschuetz•20 minutes ago

> Ease of programming and a giant leap in performance is one of the key reasons for the CUDA platform’s widespread adoption

This, so much. Other platforms continue to ignore developer UX, but it's one of the main things that get's new users onboard and keeps old users around.

msond•about 2 hours ago

We're actually targeting all of it, and not just CUDA C++.

pjmlp•about 2 hours ago

Including stuff like Fortran, Haskell, Java, .NET via PTX, Python JIT, IDE tooling integration with major IDEs, graphical GPU debugging and profiling, libraries and co?

Then I guess all the best.

zorked•about 1 hour ago

This post has some serious peanut-gallery vibes.

embedding-shape•about 1 hour ago

Ambitions but neat, good luck if nothing else :)

If you were to guess, when do you think your Nsight Compute alternative might be ready with your own toolchain?

msond•9 minutes ago

A guess would be some time next year — since our public launch our focus has generally been on API coverage and increasingly recently, on performance.

While performance improvements will always remain a target, we're soon at full coverage of the core CUDA APIs and will be shifting an increasing amount of effort towards developer tooling.

maxloh•about 1 hour ago

There is also ZLUDA, which is open source and works on pre-compiled binaries.

https://github.com/vosen/ZLUDA

tuananh•17 minutes ago

this is closest thing we have to "cuda on non-nvidia" hardware

msond•4 minutes ago

We have a comparison page: https://docs.scale-lang.com/stable/manual/comparison/#zluda

DiabloD3•about 1 hour ago

Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly.

No reason to tie yourself to Nvidia's moat.

mschuetz•17 minutes ago

A couple of years ago I evaluated both Vulkan and Cuda as a choice for future projects. I couldnt get anything done after a week in Vulkan, but had the test prototype project working after just a day in Cuda.

Needless to say, I'd never ever pick Vulkan for any project after that experience. It's just way to needlessly overengineered and bloated.

swerner•about 1 hour ago

Unfortunately, Vulkan Compute doesn’t to all the things that OpenCL, SYCL, HIP or CUDA do.

binsquare•29 minutes ago

Yep, there are inference stacks where it just does not work without cuda in any meaningful performance

lulzx•about 1 hour ago

I have been trying for cuda -> metal, to run it on mac, https://github.com/lulzx/cuda-metal

Alternative(s) to run CUDA on non-Nvidia hardware

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