Kernel Perf Testing

Kernel Performance Testing

Never run performance tests unless the user explicitly asks.

Perf scripts live in third_party/tlx/tutorials/testing/test_<arch>_<op>_perf.py, one per (op, input-contract, arch). Each takes [--version ...] to select a kernel variant; with no --version it runs all variants for that op.

NVIDIA (Hopper / Blackwell)

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.

Wrap benchmarks with denoise.sh (locks clocks/power) 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|clc|pipelined|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_persistent|ws_pipelined|ws_pipelined_persistent|clc}] [--mode fwd|bwd]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_fa_mxfp8_perf.py

AMD (gfx950 / CDNA4, gfx1250)

denoise.sh works on AMD too: it runs the wrapped command with NUMA pinning. Its GPU clock/power lock is implemented with nvidia-smi, so on AMD that part is simply skipped (no clock lock) — expect a bit more run-to-run variance.

Pick a free GPU with rocm-smi (check GPU use (%) / VRAM). denoise.sh reads CUDA_VISIBLE_DEVICES, which torch on ROCm also honors for device selection.

gfx950 (CDNA4 / MI350-class)

CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_gemm_perf.py [--version {warp_pipeline|pipelined}] [--dtype fp16|bf16]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_fa_perf.py [--version {simple|prefetch|persistent}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_addmm_glu_perf.py [--version {tlx_baseline|tlx_simple_async|tlx_optimized_async|tlx_optimized|tlx_persistent}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_ikbo_fa_perf.py   # IKBO Flash Attention
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_ikbo_lce_perf.py  # IKBO LCE (distinct op, not attention)

gfx1250

CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_mxfp_gemm_perf.py [--version {tdm_pipelined}] [--transpose-b]

Each script self-gates (is_hip, is_hip_cdna4, or is_hip_gfx1250) and prints "Skipping benchmarks" on the wrong hardware.

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.
• A reference baseline is printed alongside the Triton/TLX results: cuBLAS/rocBLAS for GEMM, SDPA for FA, the PyTorch pytorch_baseline for addmm+GLU.
• 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).