The per-observation corr update (7.6M items) ran through a work-stealing ParallelFor that
does one atomic fetch_add PER item - pure contention for trivial work (measured: update 0.60s
vs reduce 0.15s / fit 0.13s in the scale-partials loop). Add ParallelChunks (one contiguous
range per worker, no per-item sync) and use it for UpdateCorr, and parallelise the ASU keying
(gemmi reduction per distinct raw hkl - HKLKeyGenerator is const, safe to read concurrently)
and the group-stamping over disjoint raw-hkl runs.
scale-partials 0.90 -> 0.28s, group-hkl 0.20 -> 0.09s, per-pass warm 0.83s, whole scale/merge
phase ~3.3 -> ~2.0s. Bit-identical output (same space group, ISa, CC1/2). ParallelChunks is
the CPU stand-in for a flat CUDA grid-stride kernel; ParallelFor stays for the heavy, uneven
per-frame fits where the atomic amortises and work-stealing balances the load.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
leonarski_fandClaude Opus 4.8
b5d9167bf4