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Run matmul performance benchmarks. Use when user wants to measure TFLOPS, compare kernel performance, or verify correctness after code changes.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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| name | benchmark |
| description | Run matmul performance benchmarks. Use when user wants to measure TFLOPS, compare kernel performance, or verify correctness after code changes. |
Run comprehensive matmul benchmarks for all supported dtypes.
# Full benchmark
python scripts/benchmark.py
# Quick mode (fewer iterations)
python scripts/benchmark.py --quick
# Specific sizes
python scripts/benchmark.py --sizes 4096,8192
# TF32 kernel version
python scripts/benchmark.py --tf32-version v2
--sizes: Comma-separated matrix sizes (default: 2048,4096,8192)--quick: Fewer warmup/iterations for faster results--dtypes: Which dtypes to test (default: fp32,tf32,fp16,bf16)--tf32-version: v1 (WMMA) or v2 (PTX mma.sync, default)python scripts/benchmark.py [options]| Dtype | Target TFLOPS |
|---|---|
| FP32 | ~18 |
| TF32 | ~27 |
| FP16 | ~15 |
| BF16 | ~15 |
PYGPUKIT_ALLOW_TF32=1: Enable TF32 TensorCorePYGPUKIT_TF32_V2=1: Use PTX mma.sync kernel