2 commits

Author	SHA1	Message	Date
Brandon Schneider	4df6997b51	docs(kube): update infra docs, RayCluster manifest with nightly GPU images ... - infrastructure-status.md: rewrite with current cluster topology (cupfox control-plane, neon-64gb/racknerd/steamdeck workers), RayCluster status, Garage storage, Caddy edge, open issues - k3s-cluster-setup.md: fix steamdeck hardware specs (8 vCPU, 14.5 GB RAM) - raycluster.yaml: upgrade to rayproject/ray:nightly-py313-gpu (multi-arch amd64+arm64), add gpu-workers group targeting neon-64gb, add arm64-workers for neon-64gb CPU - README.md: update build job count (3460 → 3313, verified) Build: lake build Compiler 3313 jobs, 0 errors	2026-05-30 16:23:13 -05:00
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