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Agreed on ZLUDA being the practical choice. This project is more impressive as a "build a GPU compiler from scratch" exercise than as something you'd actually u
by fdefitte 8mo ago
Agreed on ZLUDA being the practical choice. This project is more impressive as a "build a GPU compiler from scratch" exercise than as something you'd actually use for ML workloads. The custom instruction encoding without LLVM is genuinely cool though, even if the C subset limitation makes it a non-starter for most real CUDA codebases.
- tgtweak 8mo agoZLUDA doesn't have full coverage though and that means only a subset of cuda codebases can be ported successfully - they've focused on 80/20 coverage for core math. Specifically: CuBLAS (limited/partial scope), cuBLASLt (limited/partial scope), cuDNN (limited/partial scope), cuFFT, cuSPARSE, NVML (very limited/partial scope) Notably Missing: cuSPARSELt, cuSOLVER, cuRAND, cuTENSOR, NPP, nvJPEG, nvCOMP, NCCL, OptiX I'd estimate it's around 20% of CUDA library coverage.