perf-check

name	perf-check
description	Run the rocDecode performance test and compare to baseline to catch regressions
allowed-tools	["Bash(cmake *)","Bash(test/perf_regression.py *)","Bash(python3 test/perf_regression.py *)","Bash(nproc)"]

Run the rocDecode performance regression check: measure decode FPS and compare it against the GPU-specific baseline. Execute the steps in order, stopping only if a step fails. Run from the rocDecode project root (the directory containing build/, samples/, and test/).

IMPORTANT: Run each command as a SEPARATE Bash tool call. Do not chain commands with && or |.

The GPU is detected automatically (via amd-smi/rocm-smi/KFD) and mapped to the matching column in the baseline (MI250X, MI300X, MI300A, MI350, MI355, Navi31, Navi48). Override with ROCDECODE_PERF_GPU=<column> if detection is wrong.

Perf streams and the baseline file are located via ROCDECODE_PERF_DIR (default $HOME/rocDecodePerformance). That directory must contain the per-codec stream subdirectories AvcPerformance, Av1Performance, HevcPerformance, Vp9Performance, and the baseline rocDecode_perf_baseline.html (download it from SharePoint first). The regression threshold is 5% Avg FPS drop, overridable via ROCDECODE_PERF_TOLERANCE.

Step 1 — Verify the ROCm toolchain

Run: test/perf_regression.py --check-rocm

Confirms ROCM_PATH points at a usable ROCm install (default /opt/rocm). If it fails, set ROCM_PATH — in ~/.profile, since skills run cmake in a non-interactive shell that does not source ~/.bashrc — and re-run before continuing.

Step 2 — Build the performance sample

Each sample builds in its own directory. Run these two commands separately:

1. cmake -S samples/videoDecodePerf -B samples/videoDecodePerf/build
2. cmake --build samples/videoDecodePerf/build

This requires rocDecode to be installed already (make install); run the /validate skill first if you have not built and installed the library this session.

Step 3 — Run the regression check

Run: test/perf_regression.py

If the skill was invoked with a quick argument (e.g. /perf-check quick), run the fast variant instead — test/perf_regression.py --quick — which measures one stream per leaf subfolder capped at ≤4K (the 8K streams dominate runtime, so they are skipped) instead of every stream. Use it for a fast sanity check; use the full run before finalizing. Each run prints its own elapsed time.

Streams within tolerance pass on a single run; streams that appear to regress are re-measured (3-run average) to rule out noise before being reported.

Report the final summary box from perf_regression.py to the user, and list any streams marked REGRESSED (with their measured vs. baseline FPS and delta%).