Nhabls t1_j32q6zp wrote on January 5, 2023 at 5:33 PM

The "monopoly" is from the ecosystem mostly, not the hardware itself. Practicioners and researchers have a much better time using consumer/entry level professional nvidia hardware. So they use nvidia.

Mind you that in the supercomputer level there is no real "monopoly" as those people just develop their solutions from the ground up.

AlmightySnoo t1_j32s8ve wrote on January 5, 2023 at 5:45 PM

Also this. $AMD still makes it explicit that they officially support Rocm only on CDNA GPUs, and even then it's only under Linux. That's an immediate turn off for lots of beginner GPGPU programmers who'll immediately flock to CUDA as it works with any not too old gaming GPU from Nvidia. It's astonishing how Lisa Su still hasn't realized the gravity of this blunder.

samobon OP t1_j32uicn wrote on January 5, 2023 at 5:59 PM

I agree with you both that until small academic labs are able to use entry level GPUs for research, there will not be a mass adoption.

wywywywy t1_j32xwg5 wrote on January 5, 2023 at 6:19 PM

Saying that though, it's nice to see AMD trying. The monopoly is extremely unhealthy.

[deleted] t1_j5jsep7 wrote on January 23, 2023 at 2:47 PM

[deleted]

visarga t1_j36bpnu wrote on January 6, 2023 at 9:42 AM

Maybe it was intended to keep AMD on a different path than NVIDIA. It looks incredibly stupid not to hop on the AI wave.

b3081a t1_j375hbo wrote on January 6, 2023 at 2:42 PM

They've added official support for Navi21 under Linux some time ago. It's still very small number of supported devices comparing to NVIDIA but at least it's no longer required to purchase CDNA accelerators to get started.

ReginaldIII t1_j33ff9r wrote on January 5, 2023 at 8:02 PM

Except there is an ecosystem monopoly at the cluster level too because some of the most established, scalable, and reliable software (like those used in fields like bio-informatics as an example) only provide CUDA implementations of key algorithms and being able to accurately reproduce results computed by them is vital.

This essentially limits those software to only running on large CUDA clusters. You can't reproduce the results without the scale of a cluster.

Consider software for processing Cryo-Electron Microscopy and Ptychography data. Very very few people are actually "developing" those software packages, but thousands of researchers around the world are using them at scale to process their micrographs. Those microscopists are not programmers, or really even cluster experts, and they just don't have the skillsets to develop on these code bases. They just need it work reliably and reproducibly.

I've been working in HPC on a range of large scale clusters for a long time. There has been a massive and dramatic demographic shift in terms of the skillsets that our cluster users have. A decade ago you wouldn't dream of letting someone not a HPC expert anywhere near your cluster. If a team of non-HPC people needed HPC you'd hire HPC experts into your team to handle that for you and tune the workloads onto the cluster and develop the code to make it work best. Now we have an environment where non-HPC people can pay for access and run their workloads directly because they leverage these pre-tinned software packages.