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Comment by joefourier | Hacker News Reader

I still use OpenCL on the daily, it allows me to program for both NVidia and AMD GPUS simultaneously with the minimum amount of pain. And you can still use it for mobile GPUs and specialized chips like the Myriad.

Do you have another open, cross-platform, widely compatible GPU programming framework to recommend?

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There is DCompute[1] which is able to generate SPIR-V and PTX simultaneously from the same D source. See my comment to the parent.

http://github.com/libmir/dcompute

Same here. What else can you do to ship GPU acceleration to AMD and NVIDIA people?

The alternatives recommended here aren't even serious IMHO. I'd rather switch to CUDA and wait till Intel/AMD sort out a REAL compatibility layer than deal with those.

Unless I'm mistaken, HIP still requires a separate compile for either platform and what runtime do they expect end users to have exactly?! At least CUDA and OpenCL are integrated in the vendor drivers.

Vulkan compute with SPIR-V seems to be the only real solution, but even that is still very early. Sill waiting for proper OpenCL 2.0 support in NVIDIA drivers :P

You simply don't ship. Enterprise deep learning doesn't ship their training code - large models are trained on purpose-designed, dedicated hardware. Hardware compatability doesn't matter, software does. Even that's flexible if it's significantly faster.

(The models can be executed on low-powered, commodity CPUs. No need for any GPU there.)

You simply don't ship

That's totally an option for our product, great idea! Why did I never think of this!

No seriously we are shipping, using OpenCL and it gives about a 20 times performance advantage for most users regardless if they have AMD or NVIDIA hardware. If something that's actually better than OpenCL comes along (or if AMD RTG goes out of business) I'll switch to it no heart broken.

But that hasn't happened yet.

General-purpose programming on the GPU has many use cases apart from deep learning, e.g. image processing, computer vision, offline rendering and other high performance computing applications. OpenCL allows you to have a single code-base working on NVidia, AMD and Intel GPUs without having to recompile or put any special effort - the same kernel that works on NVidia will work anywhere.

HIP allows developers to convert CUDA code to portable C++. The same source code can be compiled to run on NVIDIA or AMD GPUs https://github.com/ROCm-Developer-Tools/HIP

What mobile GPUs?

On iOS it is kind of deprecated and the way forward is Metal Compute.

On Android it never happened, Google created their own Renderscript dialect instead.

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