21–30 of 116 posts
Too bad it's CUDA Sooner or later this will become a problem because you are depending on the benevolence of a single manufacturer.
Which other hardware vendor provides the level of performance that Nvidia's GPU provide? Wasnt the benevolence on single (or couple) manufacturer(s) true in 90s, 2020s?
Too bad it's CUDA Sooner or later this will become a problem because you are depending on the benevolence of a single manufacturer.
Which other hardware vendor provides the level of performance that Nvidia's GPU provide? Wasnt the benevolence on single (or couple) manufacturer(s) true in 90s, 2020s?
Too bad it's CUDA Sooner or later this will become a problem because you are depending on the benevolence of a single manufacturer.
AMD's ROCm 4.0 now supports cooperative groups, which is probably one of the last major holdouts for CUDA compatibility. There's still the 64-wavefront (for AMD CDNA cards) instead of 32-wavefronts (for CUDA). But AMD even has 4x4 half-float matrix multiplication instructions in ROCm (for MI100, the only card that supports the matrix-multiplication / tensor instructions) --------- I think CUDA vs OpenCL is over. ROCm…
No, their main issue is not properly supporting ROCm in general. No Windows support at all? It still feels like they don't know whether they want to continue investing in ROCm long term.
Earlier quoted context omitted.
AMD's ROCm 4.0 now supports cooperative groups, which is probably one of the last major holdouts for CUDA compatibility. There's still the 64-wavefront (for AMD CDNA cards) instead of 32-wavefronts (for CUDA). But AMD even has 4x4 half-float matrix multiplication instructions in ROCm (for MI100, the only card that supports the matrix-multiplication / tensor instructions) --------- I think CUDA vs OpenCL is over. ROCm…
> The main issue with AMD is that they're cutting support for their older cards. No, their main issue is not properly supporting ROCm in general. No Windows support at all? It still feels like they don't know whether they want to continue investing in ROCm long term.
Too bad it's CUDA Sooner or later this will become a problem because you are depending on the benevolence of a single manufacturer.
Sure, but if this abstraction layer becomes popular, then it becomes much easier to support other GPUs without requiring client libraries to change, which is a much harder problem.
Compilers generate PTX, then the rest of the CUDA infrastructure turns PTX into Turing machine code, or Ampere machine code, or Pascal machine code.
In theory, SPIR-V should do the same job, but its just not as usable right now. In the meantime, getting it to work on PTX is easier, and then there's probably hope (in the far future) to move to SPIR-V if that ever actually takes off.
I'm not a developer on Triton, but that'd be my expectation.
Too bad it's CUDA Sooner or later this will become a problem because you are depending on the benevolence of a single manufacturer.
Which other hardware vendor provides the level of performance that Nvidia's GPU provide? Wasnt the benevolence on single (or couple) manufacturer(s) true in 90s, 2020s?
NVidia is faster at 4x4 16-bit matrix multiplications (common in Tensor / Deep Learning stuff), but MI100 still has 4x4 16-bit matrix multiplication instructions and acceleration. Its not far behind, and the greater 64-bit FLOPs is enough to win in scientific fields.
OpenAI keeps innovating. Amazing to see the speed of execution of the team
See: https://www.reddit.com/r/MachineLearning/comments/otdpkx/n_i...
Unfortunate name clash with NVIDIAs Triton Inference Server: https://developer.nvidia.com/nvidia-triton-inference-server
> PS: The name Triton was coined in mid-2019 when I released my PhD paper on the subject (http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tille...). I chose not to rename the project when the Triton inference server came out a year later since it's the only thing that ties my helpful PhD advisors to the project.
I have found writing CUDA code is much simpler than writing correct multi-threaded AVX2/AVX-512 code.
If you need CPU-side SIMD, then try ispc: https://ispc.github.io/ Its pretty much the OpenCL-model, except it compiles into AVX2 code / AVX512 code. Very similar to CUDA / OpenCL style programming. Its not single-source like CUDA, but it largely accomplishes the programming model IMO.