Facebookexperimental Triton Kernel Perf Testing

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

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).

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