This skill should be used when optimizing AMD GPU kernels on MI300 using the aiter project, including running op tests, benchmarking, iterating on kernel changes, and recording results in the kernel experiment database.
原文の言語: 英語
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SkillsMP は AMD-AGI/Apex から 12 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 12 件中 12 件を表示しています。
This skill should be used when optimizing AMD GPU kernels on MI300 using the aiter project, including running op tests, benchmarking, iterating on kernel changes, and recording results in the kernel experiment database.
原文の言語: 英語
This skill should be used when reasoning about GPU architecture fundamentals to guide kernel optimization choices such as memory hierarchy usage, execution model mapping, block sizing, and latency-aware tuning across HIP, Triton, and PyTorch.
原文の言語: 英語
This skill should be used when writing or tuning HIP kernels on AMD/NVIDIA GPUs, covering memory coalescing, shared-memory tiling, bank conflict avoidance, warp primitives, occupancy, vectorization, async ops, loop unrolling, and profiling.
原文の言語: 英語
This skill should be used when optimizing kernels in this repo and needing to consult past optimization experiments, or when recording the current optimization iteration back into the kernel experiment database.
原文の言語: 英語
MI300/CDNA3 architecture guide for HIP/Triton optimization—MFMA variants, dual register files, data formats, sparsity, LDS/GWS, and best practices.
原文の言語: 英語
CDNA3/MI300 HIP programming insights—chiplet/cache model, Infinity Cache, memory coherency, matrix cores, sparsity, and best practices.
原文の言語: 英語
MI300 HIP programming differences vs NVIDIA—wavefront vs warp, memory hierarchy, MFMA usage, occupancy, and profiling pitfalls.
原文の言語: 英語
This skill should be used when optimizing PyTorch models and kernels, including efficient tensor operations, torch.compile, custom autograd/CUDA/Triton extensions, mixed precision, memory and data pipeline tuning, model optimization techniques, CUDA graphs,…
原文の言語: 英語
This skill should be used when profiling AMD GPU kernels with rocprof-compute to collect metrics, roofline data, and analyze bottlenecks for HIP kernels.
原文の言語: 英語
Search and adapt Triton/HIP kernel patterns from a corpus to optimize AMD GPUs; use to find similar ops and reuse tiling/occupancy strategies.
原文の言語: 英語
This skill should be used when writing or tuning Triton GPU kernels, including autotuning block sizes, coalesced accesses, tiled matmul, fused ops, reductions, flash-attention style kernels, quantization, custom gradients, and profiling.
原文の言語: 英語
Reflection/self-critique prompts for reviewing and fixing AMD-targeted Triton kernels after generation or test failures.
原文の言語: 英語