AgentSkillsCN

kernel-perf-testing

在 Hopper 和 Blackwell GPU 上运行 TLX 内核性能基准测试。当用户要求对任意 TLX 内核（如 GEMM、Flash Attention 变体）进行基准测试、性能剖析或性能评估时使用。该工具可自动处理 GPU 选择、去噪封装以及版本标志的配置，除非明确被要求，否则切勿随意运行。

SKILL.md
--- frontmatter
name: kernel-perf-testing
description: >
  Run TLX kernel performance benchmarks on Hopper and Blackwell GPUs.
  Use when user asks to benchmark, profile, or measure performance of
  any TLX kernel (GEMM, Flash Attention variants). Handles GPU selection,
  denoise wrapping, and version flags. Never run unless explicitly asked.
disable-model-invocation: true

Kernel Performance Testing

Never run performance tests unless the user explicitly asks.

GPU selection protocol

  1. •Run nvidia-smi to check GPU occupancy.
  2. •Pick the GPU with the lowest memory usage.
  3. •Set CUDA_VISIBLE_DEVICES to that GPU.

Benchmark commands

All benchmarks must be wrapped with denoise.sh for stable results.

Hopper GPU

bash
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_hopper_gemm_perf.py [--version {ws|pipelined}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_hopper_fa_perf.py [--version {ws|ws_pipelined|ws_pipelined_pingpong|ws_pipelined_pingpong_persistent}]

Blackwell GPU

bash
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_gemm_perf.py [--version {ws|pipelined|clc|2cta}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_fa_perf.py [--version {ws|ws_pipelined|ws_pipelined_pingpong|ws_pipelined_pingpong_persistent}]

Other kernels

bash
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/<KERNEL.py>

If tests hang

Run third_party/tlx/killgpu.sh to kill GPU processes that have been running too long.

Interpreting results

  • •Output reports TFLOPS for each problem size and configuration.
  • •Compare against cuBLAS baselines when available (printed alongside Triton results).
  • •Higher TFLOPS = better. Look for regressions relative to previous runs.
  • •Check for consistency across runs — high variance suggests noisy measurements (ensure denoise.sh is being used).