←back to thread

AMD funded a drop-in CUDA implementation built on ROCm: It's now open-source

(www.phoronix.com)
1045 points mfiguiere | 1 comments | 12 Feb 24 14:00 UTC | HN request time: 0.213s | source
Show context
Cu3PO42 ◴[12 Feb 24 16:09 UTC] No.39346489[source]▶
I'm really rooting for AMD to break the CUDA monopoly. To this end, I genuinely don't know whether a translation layer is a good thing or not. On the upside it makes the hardware much more viable instantly and will boost adoption, on the downside you run the risk that devs will never support ROCm, because you can just use the translation layer.

I think this is essentially the same situation as Proton+DXVK for Linux gaming. I think that that is a net positive for Linux, but I'm less sure about this. Getting good performance out of GPU compute requires much more tuning to the concrete architecture, which I'm afraid devs just won't do for AMD GPUs through this layer, always leaving them behind their Nvidia counterparts.

However, AMD desperately needs to do something. Story time:

On the weekend I wanted to play around with Stable Diffusion. Why pay for cloud compute, when I have a powerful GPU at home, I thought. Said GPU is a 7900 XTX, i.e. the most powerful consumer card from AMD at this time. Only very few AMD GPUs are supported by ROCm at this time, but mine is, thankfully.

So, how hard could it possibly to get Stable Diffusion running on my GPU? Hard. I don't think my problems were actually caused by AMD: I had ROCm installed and my card recognized by rocminfo in a matter of minutes. But the whole ML world is so focused on Nvidia that it took me ages to get a working installation of pytorch and friends. The InvokeAI installer, for example, asks if you want to use CUDA or ROCm, but then always installs the CUDA variant whatever you answer. Ultimately, I did get a model to load, but the software crashed my graphical session before generating a single image.

The whole experience left me frustrated and wanting to buy an Nvidia GPU again...

replies(10): >>39346714 #>>39347956 #>>39348258 #>>39349464 #>>39349658 #>>39350019 #>>39350273 #>>39351237 #>>39354496 #>>39433413 #
1. nocombination ◴[12 Feb 24 20:02 UTC] No.39349658[source]▶
As other folks have commented, CUDA not being an open standard is a large part of the problem. That and the developers who target CUDA directly when writing Stable Diffusion algorithms—they are forcing the monopoly. Even at the cost of not being able to squeeze every ounce out of the GPU, portability greatly improves software access when people target Vulkan et al.