| 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
- Run
nvidia-smi to check GPU occupancy.
- Pick the GPU with the lowest memory usage.
- Set
CUDA_VISIBLE_DEVICES to that GPU.
Benchmark commands
All benchmarks must be wrapped with denoise.sh for stable results.
Hopper GPU
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
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
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).
