5 ms·

AMD always launches impressive hardware specs. But they are way behind in software, which is more important than hardware

PartiallyTyped 3y ago
StableHLO[1] and IREE[2] are interesting projects that might help AMD here, from [1]: > Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE). From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend. [1] https://github.com/openxla/stablehlo https://github.com/openxla/stablehlo [2] https://github.com/openxla/iree https://github.com/openxla/iree [3] https://www.tensorflow.org/xla https://www.tensorflow.org/xla
stravant 3y ago
If they were allowed to get significantly ahead that status quo would likely be disrupted pretty fast.
photonbucket 3y ago
If AMD launches hardware that is clearly faster, the software will move towards it
- pixelpoet 3y ago
  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...
  - zozbot234 3y ago
    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.)
  - saltcured 3y ago
    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.
    - zozbot234 3y ago
      AIUI, your current best bet for good OpenCL implementation on less-than-cutting-edge AMD hardware is the Mesa Project's RustiCL work.
    - pixelpoet 3y ago
      Yes, even ~2012 OpenCL code works incredibly well today for spectral path tracing: https://indigorenderer.com/indigobench https://indigorenderer.com/indigobench Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/ 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 https://github.com/glaretechnologies/glare-core Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!
- filterfiber 3y ago
  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.
deleted 3y ago
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PartiallyTyped 3y ago

StableHLO[1] and IREE[2] are interesting projects that might help AMD here, from [1]: > Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE). From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend. [1] https://github.com/openxla/stablehlo https://github.com/openxla/stablehlo [2] https://github.com/openxla/iree https://github.com/openxla/iree [3] https://www.tensorflow.org/xla https://www.tensorflow.org/xla

stravant 3y ago

If they were allowed to get significantly ahead that status quo would likely be disrupted pretty fast.

photonbucket 3y ago

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

pixelpoet 3y ago
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...
- zozbot234 3y ago
  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.)
- saltcured 3y ago
  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.
  - zozbot234 3y ago
    AIUI, your current best bet for good OpenCL implementation on less-than-cutting-edge AMD hardware is the Mesa Project's RustiCL work.
  - pixelpoet 3y ago
    Yes, even ~2012 OpenCL code works incredibly well today for spectral path tracing: https://indigorenderer.com/indigobench https://indigorenderer.com/indigobench Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/ 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 https://github.com/glaretechnologies/glare-core Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!
filterfiber 3y ago
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.

deleted 3y ago

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