> So why not just fix AMD accelerators in pytorch? Both ROCm and pytorch are open sourced. Isn't the point of the OSS community to use the community to solve problems?
Because there's no real evidence that AMD cares about this problem, and without them caring your efforts may well be replaced by whatever AMD does next in the space. Their Brooks language[1] is abandoned, OpenCL doesn't compare well, ROCm is like the Sharepoint of GPU APIs (it ticks boxes but doesn't actually work very well).
> So why not just fix AMD accelerators in pytorch
Why not just buy NVidia? They care deeply about the space, will actually help you if you have trouble, etc etc.
Even using Google TPUs is better: Google will help you too.
While everyone using NVidia isn't great for the market as a whole as an individual company or person it makes a lot of sense.
Read "The Red Team (AMD)" section in the linked article:
> The software is called ROCm, it’s open source, and supposedly it works with PyTorch. Though I’ve tried 3 times in the last couple years to build it, and every time it didn’t build out of the box, I struggled to fix it, got it built, and it either segfaulted or returned the wrong answer. In comparison, I have probably built CUDA PyTorch 10 times and never had a single issue.
This is geohot. He knows how to build software, and how to fix problems.
Note that "Our short term goal is to get AMD on MLPerf using the tinygrad framework."
> There's a clear difference in how AMD and Nvidia measure TFLOPS. techpowerup shows AMD at 2-3x Nvidia, but performance is similar. Either AMD is crazy underutilized or something is wrong. Does anyone know the answer?
From the linked article:
> That’s the kernel space, the user space isn’t better. The compiler is so bad that clpeak only gets half the max possible FLOPS. And clpeak is a completely contrived workload attempting to maximize FLOPS, never mind how many FLOPS you get on a real program
[1] https://en.wikipedia.org/wiki/BrookGPU
Because there's no real evidence that AMD cares about this problem, and without them caring your efforts may well be replaced by whatever AMD does next in the space. Their Brooks language[1] is abandoned, OpenCL doesn't compare well, ROCm is like the Sharepoint of GPU APIs (it ticks boxes but doesn't actually work very well).
> So why not just fix AMD accelerators in pytorch
Why not just buy NVidia? They care deeply about the space, will actually help you if you have trouble, etc etc.
Even using Google TPUs is better: Google will help you too.
While everyone using NVidia isn't great for the market as a whole as an individual company or person it makes a lot of sense.
Read "The Red Team (AMD)" section in the linked article:
> The software is called ROCm, it’s open source, and supposedly it works with PyTorch. Though I’ve tried 3 times in the last couple years to build it, and every time it didn’t build out of the box, I struggled to fix it, got it built, and it either segfaulted or returned the wrong answer. In comparison, I have probably built CUDA PyTorch 10 times and never had a single issue.
This is geohot. He knows how to build software, and how to fix problems.
Note that "Our short term goal is to get AMD on MLPerf using the tinygrad framework."
> There's a clear difference in how AMD and Nvidia measure TFLOPS. techpowerup shows AMD at 2-3x Nvidia, but performance is similar. Either AMD is crazy underutilized or something is wrong. Does anyone know the answer?
From the linked article:
> That’s the kernel space, the user space isn’t better. The compiler is so bad that clpeak only gets half the max possible FLOPS. And clpeak is a completely contrived workload attempting to maximize FLOPS, never mind how many FLOPS you get on a real program
[1] https://en.wikipedia.org/wiki/BrookGPU