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"CUDNN API supported by HIP" has a coverage table: https://rocm.docs.amd.com/projects/HIPIFY/en/amd-staging/tables/CUDNN_API_supported_by_HIP.html https://rocm.

"CUDNN API supported by HIP" has a coverage table: https://rocm.docs.amd.com/projects/HIPIFY/en/amd-staging/tables/CUDNN_API_supported_by_HIP.html https://rocm.docs.amd.com/projects/HIPIFY/en/amd-staging/tab... ROCm/hipDNN wraps CuDNN on Nvidia and MiOpen on AMD; but hasn't been updated in awhile: https://github.com/ROCm/hipDNN https://github.com/ROCm/hipDNN https://news.ycombinator.com/item?id=37808036 https://news.ycombinator.com/item?id=37808036 : conda-forge has various BLAS implementations, including MKL-optimized BLAS, and compatible NumPy and SciPy builds. BLAS: Basic Linear Algebra Sub programs: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra... "Using CuPy on AMD GPU (experimental)" https://docs.cupy.dev/en/v13.0.0/install.html#using-cupy-on-amd-gpu-experimental https://docs.cupy.dev/en/v13.0.0/install.html#using-cupy-on-... : $ sudo apt install hipblas hipsparse rocsparse rocrand rocthrust rocsolver rocfft hipcub rocprim rccl

HarHarVeryFunny 3y ago
I guess I misunderstood you. You were asking if this CUDA compatability layer might hold any advantage over HIP (e.g. for use by llama.cpp) ? I think the answer is no, since HIP includes pretty full-featured support for many of the higher level CUDA-based APIs (cuDNN, cuBLAS, etc), while per the Phoronix article ZLUDA only (currently) has minimal support for them. I wouldn't expect ZLUDA to provide any performance benefit over HIP either, since on AMD hardware HIP is just a pass-thru to MIOpen (AMD's equivalent to cuDNN), rocBLAS, etc.