bench: stabilize sub-ms measurements — more samples, no per-sample GC, warmup, batching, regression threshold
The CI benchmark gate was flagging wild run-to-run 'regressions'/'improvements' (-23%..-83% on unchanged code) because sub-ms checks were measured from ~5 samples with a global.gc() injected between every iteration: - uncertaintyThreshold 0.99 -> 0.1: the loop now keeps sampling until variance actually tightens instead of 'passing' at the first check. - minSamples 0 -> 200, maxSamples 200 -> 2000: real sample floor + headroom. - gcBetweenSamples true -> false: per-sample GC dominated sub-ms timings. - warmup phase: each hot path runs to steady state (JIT, lazy index, caches) before sampling, eliminating the bimodal ~4us vs ~20us distribution. - BATCH=100 for sub-ms checks: jitter amortizes across a batch per sample; the relative comparison stays exact because the baseline uses the same batch. - minimum-change threshold (MIN_HIGH_REGRESSION_PERCENT, default 10): a high-severity flag only fails CI when the change exceeds run-to-run noise. Result: within-run p95 spread is now ~5% instead of ~100x. Residual cross-run variance on loaded shared runners (this machine: load ~19) is environmental — the baseline alphaCuts capture per-run spread, not machine-load swings.
This commit is contained in:
John Dvorak
+57
-57
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