Re:Which GPUs to get for deep learning
by fluffything on Thursday September 10, 2020 @07:39AM
(#24431296)
> That sounds like koolaid marketing to me.
That's a fact: check HPL, MLPerf, Spec, etc. results. MLPerf is the perfect example, were your results are only accepted if they can be verified by others. Where is AMD in there? (nowhere, their products suck for compute).
> AMD GCN was more compute oriented than Nvidia for years
No, the only thing AMD GCN was good for is as a very expensive stove.
AMD GCN had a lot of compute, on paper, and higher numbers than nvidia GPUs of the time. Unfortunately, AMD GCN's memory subsystem sucked, and it was impossible to deliver data fast enough to actually be able to use the compute.
So nvidia's hardware essentially destroyed GCN for any useful practical application.
IIRC, the only application for which GCN's got some use was bitcoin mining, which avoided hitting GCN's issues because it just requires doing a ton of useless work on a tiny amount of memory. Perfect for GCN right? Nope, nvidia's hardware was still better, but sold out, and GCN wasn't horrible at this, so it got some use.
AMD actually fired the architect of GCN over this. Yet this still perfectly summarizes AMD's GPGPU strategy of the last 15 years: higher numbers on paper, that cannot be achieved in practice, and lower that the numbers that nvidia's hardware achieves in practice.
That's a fact: check HPL, MLPerf, Spec, etc. results. MLPerf is the perfect example, were your results are only accepted if they can be verified by others. Where is AMD in there? (nowhere, their products suck for compute).
> AMD GCN was more compute oriented than Nvidia for years
No, the only thing AMD GCN was good for is as a very expensive stove.
AMD GCN had a lot of compute, on paper, and higher numbers than nvidia GPUs of the time. Unfortunately, AMD GCN's memory subsystem sucked, and it was impossible to deliver data fast enough to actually be able to use the compute.
So nvidia's hardware essentially destroyed GCN for any useful practical application.
IIRC, the only application for which GCN's got some use was bitcoin mining, which avoided hitting GCN's issues because it just requires doing a ton of useless work on a tiny amount of memory. Perfect for GCN right? Nope, nvidia's hardware was still better, but sold out, and GCN wasn't horrible at this, so it got some use.
AMD actually fired the architect of GCN over this. Yet this still perfectly summarizes AMD's GPGPU strategy of the last 15 years: higher numbers on paper, that cannot be achieved in practice, and lower that the numbers that nvidia's hardware achieves in practice.