perf-check

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%).