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Comment by photonbucket | Hacker News Reader

If AMD launches hardware that is clearly faster, the software will move towards it

That's exactly what the CUDA monopoly is meant to prevent, and as a fervent supporter of OpenCL (with two commercial apps), this is exactly the case I always make: even if some GPU came out tomorrow costing $0 and with infinite performance, all these people who paint themselves into a corner are hosed.

Not that anyone cares, and everyone keeps using CUDA while simultaneously complaining about Nvidia GPU prices, as if those two things have nothing to do with each other...

My own understanding is that OpenCL is semi-obsolete at the moment (although newer standards revisions are still coming out, so this may change in the future) with forward-looking projects mostly targeting Vulkan Compute or SYCL.

(There are some annoying differences in the low-level implementations of OpenCL vs. Vulkan Compute, due to their being based on SPIR-V compute "kernels" vs. "shaders" respectively, that make it hard for them to interop cleanly. So that's why the choice can be significant.)

Have you had good experience with this for portability though? On what classes of hardware and OS?

I did a bit of work in OpenCL almost 10 years ago, and found it decently portable on a range of NVIDIA GPUs as well as Intel iGPUs. On the high end I used something like the Titan X while on the low end it was typical GPUs found in business class laptops.

But my limited exposure to AMD was terrible by comparison. Even though I am away from that work now, I still tend to try to run "clpeak" and one of my simpler image processing scripts on each new system. And while I liked a Ryzen laptop for general use or even games, it seemed like OpenCL was useless there. It seemed my best option was to ignore the GPU and use Intel's x86_64 SIMD OpenCL runtime.

AIUI, your current best bet for good OpenCL implementation on less-than-cutting-edge AMD hardware is the Mesa Project's RustiCL work.

Yes, even ~2012 OpenCL code works incredibly well today for spectral path tracing: https://indigorenderer.com/indigobench

Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/

Both work on [ Nvidia, AMD, Intel, Apple ] x [ CPU, GPU ].

Some of the shared code here: https://github.com/glaretechnologies/glare-core

Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!

OpenCL tooling has always been bad versus CUDA, and that isn't NVidia to blame, rather Intel and AMD.

Only C, C++ and Fortran were never taken seriously enough, other language stacks never considered.

Thus everyone that enjoyed programming in anything not C, with great libraries and graphical debuggers flocked to CUDA, now remains to be seen if SYCL and SPIRV will ever matter enough to regain some of those folks back.

The probability of a processor emerging that is so much cheaper that it warrants moving over, but that also its API is the OpenCL that you already target is very low. Aim for what is probable, not what is theoretically possible.

Most people who utilize this hardware aren't programming kernels directly for the GPU, they're using abstraction layers like pytorch, tensorflow, etc. For the developers of those type of frameworks, cuda itself offers a lot of libraries like cublas.

There's relatively few people capable of implementing these frameworks without a solid cuda-like foundation, and those that do exist would need a very strong incentive to do it.

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