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, and16are 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
8is the first setting that exposes more than one active subtile while still keeping the active region compact. - Width
4reveals 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 aroundx=32andx=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
8increases spatial contrast relative to16while still preserving a compact active set. - Width
4pushes max spread to1.0, 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, and16are trivially stable because only one subtile ever carries hits - width
8remains stable enough for prioritization: the best subtile stays within the same two-neighbor region, with a66.6% / 33.4%split - width
4keeps 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