菜单
Run matmul performance benchmarks. Use when user wants to measure TFLOPS, compare kernel performance, or verify correctness after code changes.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
SM120 (Blackwell) CUDA expert. Use for wgmma/mma PTX inline assembly, TMA, narrow precision (FP4/FP6/FP8), and block-scaled GEMM development.
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Run all checks including lint, typecheck, and tests. Use before creating PRs or for comprehensive validation.
CUDA kernel development workflow. Use when writing, testing, or optimizing GPU kernels. Follows the Edit-Build-Validate-Benchmark-Commit cycle.
Run Ruff linter and formatter on Python code. Use before commits or when checking code style and quality issues.
| 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