Develop and extend gfxGRAPH internals — add gap bridges, fix HIP/ROCm issues, debug graph capture failures, and understand the architecture. Covers BridgedCUDAGraph, ShapeBucketPool, ConditionalGraph, native bridge, and the monkey-patch system. USE FOR: extend gfxGRAPH, add new gap bridge, fix gfxGRAPH bug, understand gfxGRAPH architecture, modify BridgedCUDAGraph, add gfxGRAPH feature, debug graph capture internals, improve gfxGRAPH performance, gfxGRAPH C++ native bridge, write gfxGRAPH tests. DO NOT USE FOR: just using gfxGRAPH in a project (use gfxgraph-integration), general ROCm issues.
Benchmark gfxGRAPH internals, run the public benchmark suite, and compare performance between Python and Rust.
Guide and resources for deployment and development using the ROCm/AMD ATOM (AiTer Optimized Model) inference backend.
Validates and benchmarks ROCm-DS component ports against their CPU baselines (e.g., pandas vs hipDF). Use this to generate parity tests, verify correctness, and measure the performance speedups of GPU-accelerated code.
Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.
Migrates pandas-like workflows to hipDF and cudf.pandas style acceleration, auditing for unsupported features.
Leverages GPU acceleration to process and analyze complex graph structures using hipGRAPH. Note: hipGRAPH is early access.
Utilizes the HIP Memory Manager (hipMM) for advanced GPU memory pooling, efficient allocation, and data movement.