←back to thread

1045 points mfiguiere | 3 comments | | HN request time: 0.229s | source
Show context
Cu3PO42 ◴[] 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 #
Certhas ◴[] No.39346714[source]
They are focusing on HPC first. Which seems reasonable if your software stack is lacking. Look for sophisticated customers that can help build an ecosystem.

As I mentioned elsewhere, 25% of GPU compute on the Top 500 Supercomputer list is AMD. This all on the back of a card that came out only three years ago. We are very rapidly moving towards a situation where there are many, many high-performance developers that will target ROCm.

replies(1): >>39347423 #
1. ametrau ◴[] No.39347423[source]
Is a top 500 super computer list a good way of measuring relevancy in the future?
replies(2): >>39347862 #>>39352508 #
2. latchkey ◴[] No.39347862[source]
No, it isn't. What is a better measure is to look at businesses like what I'm building (and others), where we take on the capex/opex risk around top end AMD products and bring them to the masses through bare metal rentals. Previously, these sorts of cards were only available to the Top 500.
3. llm_trw ◴[] No.39352508[source]
Yes it is, it's how cuda got it's dominance 10 years ago. Businesses don't release their source code, super computers are attached to labs and universities and have much better licenses for software, or publish papers about it.