Three regions of the merge tail, measured with instrumented timers and confirmed
against a cycle profile. On a tail-heavy dataset the scale and merge tail is 70%
of the run's wall clock at six of thirty-two logical cores busy, with the GPU idle
88% of the time, so this is where the CPU headroom is.
fit_error_model ran a serial four-level nth_element cascade over the whole sample
pool, twelve times per dataset. The two halves either side of a partition are
disjoint and their contents are already fixed by the parent's nth_element, so the
recursion can descend both at once; it now does while a range is worth a thread.
The bins are unchanged.
ComputeAsuGroups sorted indices with an indirect comparator, taking a cache miss
per comparison into an array far larger than the last-level cache. It now sorts
packed key-and-run pairs. Tie order does not matter because the packed key encodes
h, k, l and the hand exactly, so every run in a tie reduces to the same reflection.
The per-thread histogram prefix walked thirty-two separate histograms column-wise
on one thread. It becomes a parallel per-group total, one sequential scan over two
flat arrays, and a parallel hand-out of the bases - the same sums in the same
order.
Faster on 21 of 23 matched pairs in an alternating A/B, and on 15 of 15 in the
quieter of the two sessions: 0.6% to 2.3% of whole-run wall clock depending on the
dataset, around 1.8% in aggregate, and 3 to 4% of the time spent outside the image
loop. The reflection files are byte-identical on every dataset tested.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016NNnL26LAvruQ9eLUUWvrJ
leonarski_fandClaude Opus 5
2f54a1189d