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Abstract:

This live stream documents the challenging process of compiling AMD's ROCm GPU compute stack, specifically targeting AI workloads like llama.cpp, on a 64-core RISC-V system ("RISC-V Ripper" with 128GB RAM) and preparing builds for ARM64. The stream highlights the significant hurdles encountered, including extreme build and link times (especially on RISC-V), pervasive issues with CMake build system configurations across different ROCm components, inconsistent naming conventions for GPU target flags within the ROCm project, and numerous linker errors due to incorrect library paths and dependencies. Despite successfully compiling many ROCm components and llama.cpp itself with the HIP backend enabled after extensive debugging and patching over several weeks, a critical segmentation fault occurs during the compilation of rockSolver on the RISC-V platform, potentially related to the Clang compiler or experimental kernel features, ultimately blocking the final goal of running the AI workload on the RISC-V hardware. The session underscores the complexity and frustration involved in porting large, intricate software ecosystems to non-x86 architectures.

Building ROCm for AI on RISC-V: Debugging Builds & Chasing Errors

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