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Curved Minimal Subtile Granularity

Scope

Phase 1.5 characterizes the existing tile-metrics scaffold on the curved_minimal validation path without changing scheduler behavior.

Runs used:

  • --tile-metrics=1
  • subtile widths: 64, 32, 16, 8, 4
  • same curved-minimal render-test path already used for validation

Reference artifacts:

  • batch summary: /tmp/curved_minimal_granularity_8cmn/summary.json
  • logs:
  • /tmp/curved_minimal_granularity_8cmn/w64.log
  • /tmp/curved_minimal_granularity_8cmn/w32.log
  • /tmp/curved_minimal_granularity_8cmn/w16.log
  • /tmp/curved_minimal_granularity_8cmn/w8.log
  • /tmp/curved_minimal_granularity_8cmn/w4.log

The scaled film width on this path is effectively 80px, so widths below 4 would likely over-fragment without adding meaningful spatial signal.

Summary Table

Width Unique Subtiles Active Empty Hit Concentration Mean Spread Max Spread Active-Band Top Stability
64 2 1 1 one subtile owns 100% of hits 0.0392 0.1880 100% same subtile
32 3 1 2 one subtile owns 100% of hits 0.0783 0.3750 100% same subtile
16 5 1 4 one subtile owns 100% of hits 0.1566 0.7500 100% same subtile
8 10 2 8 hits split 53.3% / 46.7% across two neighbors 0.1671 0.7500 66.6% / 33.4% between two neighbors
4 20 4 16 hits split 40.0% / 36.7% / 13.3% / 10.1% across four neighbors 0.2504 1.0000 66.6% / 33.4% between two neighbors

Interpretation

Active vs Empty Subtiles

  • Widths 64, 32, and 16 are too coarse for scheduler experiments on this scene. They collapse the active region into a single subtile, so prioritization would have little to choose between.
  • Width 8 is the first setting that exposes more than one active subtile while still keeping the active region compact.
  • Width 4 reveals more structure, but most subtiles remain empty and the active region becomes noticeably more fragmented.

Hit Concentration

  • Coarse widths hide internal structure by assigning all hits to one container.
  • At width 8, the scene resolves into two adjacent active subtiles around x=32 and x=40, with a near-even split in hit share.
  • At width 4, that same region breaks into four active subtiles, but the outer two carry much less signal than the inner pair.

Mean / Max Yield Spread

  • Yield spread rises as width narrows, which is expected and desirable up to a point.
  • Width 8 increases spatial contrast relative to 16 while still preserving a compact active set.
  • Width 4 pushes max spread to 1.0, which is strong discrimination but also a sign of sparse, highly localized occupancy.

Top-Yield Stability

Important note:

  • counting the top subtile across all bands is misleading here because many bands are zero-hit and therefore tie at 0
  • the useful signal is top-subtile stability across active bands only

Using active bands only:

  • widths 64, 32, and 16 are trivially stable because only one subtile ever carries hits
  • width 8 remains stable enough for prioritization: the best subtile stays within the same two-neighbor region, with a 66.6% / 33.4% split
  • width 4 keeps the same core region, but becomes more spike-prone and fragmented

Recommendation

Recommended initial subtile width for first scheduler experiments: 8

Why:

  • it is the first width that exposes non-trivial horizontal structure
  • it keeps the active region narrow and interpretable
  • it avoids the over-fragmentation and one-pixel-like spikes seen at width 4
  • it should give a scheduler enough spatial choice to test prioritization without making bookkeeping dominate the experiment

Safest First Prioritization Experiment

Recommended first experiment: reorder-only

Why:

  • it isolates the effect of tile ordering from the effect of reduced work
  • it preserves the current budget envelope, which is safer for output-stability comparisons
  • it makes validation easier to interpret because any change in results comes from traversal order, not fewer rays or fewer candidate checks

reorder plus budget reduction should be deferred until reorder-only shows:

  • stable top-subtile preference over time
  • no visible instability or regression in the curved-minimal validation path
  • a clear work-to-hit advantage worth converting into an actual budget cut