A little feedback to AMD executives about the current status of ROCm here:

(1) - Supporting only Server grade hardware and ignoring laptop/consumer grade GPU/APU for ROCm was a terrible strategical mistake.

A lot of developers experiments first and foremost on their personal laptop first and scale on expensive, professional grade hardware later. In addition: some developers simply do not have the money to buy server grade hardware.

By locking ROCm only to server grade GPUs, you restrict the potential list of contributors to your OSS ROCm ecosystem to few large AI users and few HPC centers... Meaning virtually nobody.

A much more sensible strategy would be to provide degraded performance for ROCm on top of consummer GPUs, and this is exactly what Nvidia with CUDA is doing.

This is changing but you need to send a clear message there. EVERY new released device should be properly supported by ROCm.

- (2) Supporting only the two last generations of architecture is not what customers want to see.

https://rocm.docs.amd.com/projects/install-on-linux/en/docs-...

People with existing GPU codebase invests significant amount of effort to support ROCm.

Saying them two years later: "Sorry you are out of update now!" when the ecosystem is still unstable is unacceptable.

CUDA excels to backward compatibility. The fact you ignore it entirely plays against you.

(3) - Focusing exclusively on Triton and making HIP a second class citizen is non-sensical.

AI might get all the buzz and the money right now, we go it.

It might look sensible on the surface to focus on Python-base, AI focused, tools like Triton and supporting them is definitively necessary.

But there is a tremendous amount of code that is relying on C++ and C to run over GPU (HPC, simulation, scientific, imaging, ....) and that will remain there for the multiple decades to come.

Ignoring that is loosing, again, custumers to CUDA.

It is currently pretty ironic to see such a move like that considering that AMD GPUs currently tend to be highly competitive over FP64, meaning good for these kind of applications. You are throwing away one of your own competitive advantage...

(4) - Last but not least: Please focus a bit on the packaging of your software solution.

There has been complained on this for the last 5 years and not much changed.

Working with distributions packagers and integrating with them does not cost much... This would currently give you a competitive advantage over Nvidia..

Taking on CUDA with ROCm: 'One Step After Another'