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> Question from a curious sysadmin: How are these AMD GPUs for machine learning and math related stuff? They're just fine, though I think a tuned implementatio

> Question from a curious sysadmin: How are these AMD GPUs for machine learning and math related stuff? They're just fine, though I think a tuned implementation on either tends to show NVIDIA ahead. > I have only heard of Nvidia CUDA in this context. Does anybody do ML work on AMD GPUs? NVIDIA is still king here, but if AMD continues to develop their Radeon Instinct stuff, it could be competitive for some applications; it then becomes a question of whether or not there will be enough people who know something other than CUDA (which is not currently supported on AMD GPUs, though work is under way to make a compatible runtime).

celrod 8y ago
HIP (part of the ROCm stack) is Fairlane similar to CUDA. They also have goodies like tensorflow-rocm, available through pip or a docker container. That stack is also open source. And writing in it is supposed to keep you compatible with NVidea. Wanting to support them -- and hoping they become more competitive in ML -- are why I bought Vega GPUs. Although, I haven't found much time to actually try and get any of my software running on a GPU, let alone optimized. As a huge fan of avx512, I would like to try -- maybe I can get even better performance on a graphics card. But with AMD, the lack of support for the uninitiated is apparent. Not much in the way of existing software and resources like online tutorials. I'd like guides on how to optimize kernels, organizing wavefronts and discussing memory movement. Maybe I haven't looked hard enough. Especially painful in Julia, where almost half a year after 1.0, the only supported way to use GPUs on any library is with CUDANative. "Cross platform" GPUArrays.jl's open cl backend still hasn't been updated. Means all my coding will be done in HIP. Which is fine.

3 ms·

> Question from a curious sysadmin: How are these AMD GPUs for machine learning and math related stuff? They're just fine, though I think a tuned implementatio

by microcolonel 8y ago

celrod 8y ago
HIP (part of the ROCm stack) is Fairlane similar to CUDA. They also have goodies like tensorflow-rocm, available through pip or a docker container. That stack is also open source. And writing in it is supposed to keep you compatible with NVidea. Wanting to support them -- and hoping they become more competitive in ML -- are why I bought Vega GPUs. Although, I haven't found much time to actually try and get any of my software running on a GPU, let alone optimized. As a huge fan of avx512, I would like to try -- maybe I can get even better performance on a graphics card. But with AMD, the lack of support for the uninitiated is apparent. Not much in the way of existing software and resources like online tutorials. I'd like guides on how to optimize kernels, organizing wavefronts and discussing memory movement. Maybe I haven't looked hard enough. Especially painful in Julia, where almost half a year after 1.0, the only supported way to use GPUs on any library is with CUDANative. "Cross platform" GPUArrays.jl's open cl backend still hasn't been updated. Means all my coding will be done in HIP. Which is fine.