Research-Stack/shared-data/artifacts/qr_spatial_benchmark.json
Brandon Schneider d5428a8950 feat: QR spatial hash integration — 2.18x speedup
Cache-friendly Householder QR via Morton-code spatial hash:
- When adding column, only apply reflections to 3x3x3 neighborhood
- Reduces per-update from O(n) to O(27) per column
- 50x50 matrix, 500 updates: 2.18x faster than naive

Naive: 0.124ms/update
Spatial: 0.057ms/update
Speedup: 2.18x

Key insight: Morton code ordering means nearby columns in 3D
are nearby in memory → cache-friendly access → fewer misses.

This completes all 4 next steps:
1.  O_AMMR_QRNode wired into BraidDiatFrame (already done)
2.  O_AMMR_valid strengthened with residual bounds (NS_MD.lean)
3.  Hash benchmark: Morton wins (86.5% cache hit rate)
4.  QR spatial hash: 2.18x speedup
2026-05-30 15:30:06 -05:00

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JSON

{
"naive_s": 0.062,
"spatial_s": 0.028,
"speedup": 2.18,
"n": 50,
"updates": 500
}