Scaling improves - #179
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…ered over a huge address range (3.4 GB span for 40 MB of real data at n=1M). cl.cells itself was visited via cell_indices_real in particle-arrival order (effectively random for the serial build path), not spatial order — this turned out to be the bigger contributor. Fix: a single _compact_particles! post-processing pass, run once at the end of every cell-list build (regardless of which of the four build paths produced it), that: copies each cell's particles into one flat, contiguous cl.particles buffer, and reorders cl.cells itself into linear cell-index (spatial) order, remapping cell_indices/cell_indices_real to match.
…th a stride equal to the batch count — actively fights the earlier cache-locality fix as batch count grows) to Consecutive() (keeps each chunk spatially contiguous, matching the cell ordering from _compact_particles!). Replaced the static "one fixed chunk per thread" scheme with a work-queue: cells are split into 16 × nbatches small chunks, and a fixed pool of nbatches worker tasks pulls chunks on demand from a shared atomic counter. Faster cores (P-cores) naturally claim more chunks; slower cores (E-cores) do fewer — this is the "distribute to free processors on demand" behavior you asked about. Memory stays exactly what it was: output_threaded is still sized to nbatches (not to chunk count), so there's no regression for large vector-valued outputs like the ones you flagged as a concern.
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #179 +/- ##
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- Coverage 98.87% 98.65% -0.23%
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Files 21 21
Lines 1515 1631 +116
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+ Hits 1498 1609 +111
- Misses 17 22 +5 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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