streaming: split dense passage-junction grid fills; heavy-first order + far-parent deferral in Unreal (L85)

Tiles with passage-overlap score >= 9 split grid fill over up to 4 helper lanes (disjoint rows,
same per-sample order, join before MC) when workers are idle. FI junction tile fill 173 -> 99 ms;
estimated serial floor ~0.55-0.60 s. Readiness essentially unchanged (FI/TN LOD0-2 ~2.15 s @W12):
the limit is now aggregate work, not single tiles.
Unreal: LOD0-2 ordered by passage overlap before travel/distance (L80 modes kept); far parents
built after LOD0-1, LOD2 gated on them; helper budget via ParallelFor. Unreal null-RHI FI
arrival: LOD0-2 resident at 3.65 s.
All tile hashes identical to L84 (four whole views). Fast tier PASS; Linux UE host builds.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016dhxM43dnWvUu2B5Ph1iG1

Standalone/Research/WorldAssemblerM3/Linux85/RunL85Scaling.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""L80-protocol W8/W12 readiness pairs for the L85 split-grid candidate."""
+from __future__ import annotations
+
+import json
+import re
+import statistics
+import subprocess
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[4]
+OUT = ROOT / "Saved/Linux/l85/scaling-final"
+DATA = ROOT / "Standalone/data/owner_world.json"
+BINARY = ROOT / "Saved/Linux/l85/bin/VoxelForgeCoreHeadless-clang19"
+WORKERS = (8, 12)
+CAMERAS = {
+    0: (8.0, 16.0, -372.2626953125),
+    1: (25.8369140625, 3.19921875, -737.0),
+    2: (19.166015625, -14.2001953125, -753.3740234375),
+    3: (-20.0625, -5.068359375, -900.353515625),
+}
+EXPECTED_NEAR = {0: 1408, 1: 1216, 2: 1344, 3: 1344}
+EXPECTED_WHOLE = {0: 2850, 1: 2249, 2: 2233, 3: 2329}
+SOURCE_STAMP = "2967bb8aaea5a7beac0cd0735c0c2e5cccd7b31a9622dd33395e81ff1e5f2589"
+PIPELINE = re.compile(r"^\[StreamPipeline\] (.*)$", re.M)
+FIELDS = re.compile(r"([a-zA-Z0-9_]+)=([^ ]+)")
+NEAR = re.compile(r"^\[WorldAssemblerNearLODReady\] lods=0-2 tiles=(\d+)/(\d+) cold_ready_s=([0-9.]+)$", re.M)
+STAMP = re.compile(r"^\[VoxelForgeSourceStamp\] sha256=(\S+)$", re.M)
+
+
+def run(strate: int, workers: int, phase: str, rep: int) -> dict:
+    OUT.mkdir(parents=True, exist_ok=True)
+    log = OUT / f"s{strate}-w{workers}-{phase}{rep}.log"
+    camera = ",".join(f"{v:.12f}" for v in CAMERAS[strate]) + ",0,0"
+    command = [
+        "taskset", "-c", "0-11", "prlimit", "--as=6442450944", "--",
+        "env", "VOXELFORGE_L81_PASSAGE_BOUND_PADDING_VOXELS=0", "timeout", "1740s",
+        str(BINARY), str(DATA), "--world-assembler-prototype", "--stream-benchmark",
+        "--stream-pipeline", f"--workers={workers}", "--clip-radius=3",
+        "--max-clip-level=8", "--max-level=8", f"--camera={camera}",
+        "--profile-lod-work", "--stream-scheduler-policy=3",
+    ]
+    start = time.monotonic()
+    with log.open("w", encoding="utf-8") as stream:
+        result = subprocess.run(command, cwd=ROOT, stdout=stream,
+                                stderr=subprocess.STDOUT, check=False, timeout=1740)
+    output = log.read_text(encoding="utf-8", errors="replace")
+    pipeline_match, near_match, stamp_match = (
+        PIPELINE.search(output), NEAR.search(output), STAMP.search(output))
+    if result.returncode or pipeline_match is None or near_match is None:
+        raise RuntimeError(f"readiness run failed: exit={result.returncode} {log}")
+    if stamp_match is None or stamp_match.group(1) != SOURCE_STAMP:
+        raise RuntimeError(f"source stamp mismatch in {log}")
+    pipeline = dict(FIELDS.findall(pipeline_match.group(1)))
+    near_done, near_total = int(near_match.group(1)), int(near_match.group(2))
+    whole_done, whole_total = (int(v) for v in pipeline["whole_tiles"].split("/"))
+    if (near_done, near_total) != (EXPECTED_NEAR[strate], EXPECTED_NEAR[strate]):
+        raise RuntimeError(f"near readiness incomplete in {log}: {near_done}/{near_total}")
+    if (whole_done, whole_total) != (EXPECTED_WHOLE[strate], EXPECTED_WHOLE[strate]):
+        raise RuntimeError(f"whole readiness incomplete in {log}: {whole_done}/{whole_total}")
+    row = {
+        "strate": strate, "workers": workers, "phase": phase, "replicate": rep,
+        "exit_code": result.returncode, "wall_s": round(time.monotonic() - start, 3),
+        "lod02_ready_s": float(near_match.group(3)),
+        "whole_view_ready_s": float(pipeline["whole_view_ready"].removesuffix("s")),
+        "shape_s": float(pipeline["shape"].removesuffix("s")),
+        "far_shape_s": float(pipeline["far_shape"].removesuffix("s")),
+        "first_tile_ready_s": float(pipeline["first_tile_ready"].removesuffix("s")),
+        "neighborhood_ready_s": float(pipeline["neighborhood_ready"].removesuffix("s")),
+        "full_detail_ready_s": float(pipeline["full_detail_ready"].removesuffix("s")),
+        "near_tiles": near_total, "whole_tiles": whole_total,
+        "source_stamp": stamp_match.group(1), "log": str(log.relative_to(ROOT)),
+    }
+    print("[L85ScalingRun] " + json.dumps(row, sort_keys=True), flush=True)
+    return row
+
+
+def main() -> int:
+    if not BINARY.is_file() or not DATA.is_file():
+        raise SystemExit(f"missing input: {BINARY} or {DATA}")
+    rows = []
+    for strate in range(4):
+        for workers in WORKERS:
+            run(strate, workers, "warmup", 0)
+        for rep, order in enumerate(((8, 12), (12, 8), (8, 12)), 1):
+            for workers in order:
+                rows.append(run(strate, workers, "measured", rep))
+                (OUT / "runs-partial.json").write_text(
+                    json.dumps(rows, indent=2) + "\n", encoding="utf-8")
+    summary = []
+    for strate in range(4):
+        for workers in WORKERS:
+            subset = [row for row in rows
+                      if row["strate"] == strate and row["workers"] == workers]
+            item = {"strate": strate, "workers": workers, "measured_runs": len(subset)}
+            for metric in ("lod02_ready_s", "whole_view_ready_s", "shape_s", "far_shape_s",
+                           "first_tile_ready_s", "neighborhood_ready_s", "full_detail_ready_s"):
+                values = [row[metric] for row in subset]
+                item[metric] = values
+                item[f"{metric}_median"] = statistics.median(values)
+                item[f"{metric}_min"] = min(values)
+                item[f"{metric}_max"] = max(values)
+            summary.append(item)
+    (OUT / "summary.json").write_text(
+        json.dumps({"runs": rows, "summary": summary}, indent=2) + "\n",
+        encoding="utf-8")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())