research: chord-bound + z-column fine-segment pruning (L116, exact, not merged)
Fine pairs 48.9M -> 38.8M (-21%) with L115-identical hashes, but no readiness change (still +15..+30% near-ready vs L109). Pair evaluation is not where the regression lives. Kept as patch. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016dhxM43dnWvUu2B5Ph1iG1
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#!/usr/bin/env python3
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"""Five interleaved L80 readiness runs for L109, L115, and L116."""
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from __future__ import annotations
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import json
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import os
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import re
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import statistics
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import subprocess
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import time
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[4]
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OUT = ROOT / "Saved/Linux/l116/timing"
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DATA = ROOT / "Standalone/data/owner_world.json"
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BINARIES = {
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"L109": ROOT / "Saved/Linux/l109/bin-final/VoxelForgeCoreHeadless-l109-clang",
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"L115": ROOT / "Saved/Linux/l115/bin/VoxelForgeCoreHeadless-l115-clang",
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"L116": ROOT / "Saved/Linux/l116/bin/VoxelForgeCoreHeadless-l116-clang",
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}
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CAMERAS = {
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0: (8.0, 16.0, -372.2626953125),
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1: (25.8369140625, 3.19921875, -737.0),
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2: (19.166015625, -14.2001953125, -753.3740234375),
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3: (-20.0625, -5.068359375, -900.353515625),
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}
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PIPELINE = re.compile(r"^\[StreamPipeline\] (.*)$", re.M)
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FIELDS = re.compile(r"([a-zA-Z0-9_]+)=([^ ]+)")
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NEAR = re.compile(
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r"^\[WorldAssemblerNearLODReady\] lods=0-2 tiles=(\d+)/(\d+) cold_ready_s=([0-9.]+)$",
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re.M,
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)
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def run(strate: int, workers: int, version: str, phase: str, replicate: int) -> dict:
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OUT.mkdir(parents=True, exist_ok=True)
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label = f"s{strate}-w{workers}-{version}-{phase}{replicate}"
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log = OUT / f"{label}.log"
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camera = ",".join(f"{v:.12f}" for v in CAMERAS[strate]) + ",0,0"
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command = [
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"taskset", "-c", "0-11", "prlimit", "--as=6442450944", "--",
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"timeout", "1740s", str(BINARIES[version]), str(DATA),
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"--world-assembler-prototype", "--stream-benchmark", "--stream-pipeline",
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f"--workers={workers}", "--clip-radius=3", "--max-clip-level=8",
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"--max-level=8", f"--camera={camera}", "--profile-lod-work",
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"--stream-scheduler-policy=3",
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]
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start = time.monotonic()
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with log.open("w", encoding="utf-8") as output:
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result = subprocess.run(command, cwd=ROOT, stdout=output,
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stderr=subprocess.STDOUT, check=False, timeout=1740)
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content = log.read_text(encoding="utf-8", errors="replace")
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pipeline_match = PIPELINE.search(content)
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near_match = NEAR.search(content)
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if result.returncode or pipeline_match is None or near_match is None:
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raise SystemExit(f"readiness run failed or trace missing: {log}; exit={result.returncode}")
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pipeline = dict(FIELDS.findall(pipeline_match.group(1)))
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row = {
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"strate": strate, "workers": workers, "version": version,
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"phase": phase, "replicate": replicate, "exit_code": result.returncode,
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"wall_s": round(time.monotonic() - start, 3),
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"lod02_ready_s": float(near_match.group(3)),
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"whole_view_ready_s": float(pipeline["whole_view_ready"].removesuffix("s")),
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"whole_tiles": int(pipeline["whole_tiles"].split("/")[0]),
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"log": str(log.relative_to(ROOT)),
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}
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print("[L116TimingRun] " + json.dumps(row, sort_keys=True), flush=True)
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return row
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def main() -> int:
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if any(not path.is_file() for path in (*BINARIES.values(), DATA)):
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raise SystemExit("missing L109/L115/L116 data or timing binary")
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OUT.mkdir(parents=True, exist_ok=True)
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load_1m = os.getloadavg()[0]
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uptime = subprocess.run(["uptime"], cwd=ROOT, text=True, capture_output=True,
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check=True).stdout.strip()
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top = subprocess.run(["top", "-b", "-n1"], cwd=ROOT, text=True,
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capture_output=True, check=True).stdout
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(OUT / "machine-load-before.txt").write_text(
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f"uptime: {uptime}\n1-minute load: {load_1m:.2f}\ntop:\n{top}",
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encoding="utf-8")
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print(f"[L116TimingGate] load_1m={load_1m:.2f} uptime={uptime}", flush=True)
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if load_1m >= 1.5:
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raise SystemExit(f"timing refused: 1-minute load {load_1m:.2f} is not below 1.5")
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rows = []
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versions = ("L109", "L115", "L116")
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for strate in range(4):
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for workers in (8, 12):
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for version in versions:
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run(strate, workers, version, "warmup", 0)
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for replicate in range(1, 6):
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offset = (replicate - 1) % len(versions)
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order = versions[offset:] + versions[:offset]
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for version in order:
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rows.append(run(strate, workers, version, "measured", replicate))
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(OUT / "runs-partial.json").write_text(
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json.dumps(rows, indent=2) + "\n", encoding="utf-8")
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summary = []
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for strate in range(4):
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for workers in (8, 12):
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item = {"strate": strate, "workers": workers, "replicates": 5}
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medians = {}
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for version in versions:
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group = [row for row in rows if row["strate"] == strate
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and row["workers"] == workers and row["version"] == version]
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medians[version] = {}
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for key in ("lod02_ready_s", "whole_view_ready_s"):
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values = [row[key] for row in group]
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medians[version][key] = statistics.median(values)
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item[f"{version}_{key}_values"] = values
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item[f"{version}_{key}_median"] = medians[version][key]
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for comparison in ("L109", "L115"):
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for key in ("lod02_ready_s", "whole_view_ready_s"):
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item[f"L116_vs_{comparison}_{key}_delta_pct"] = round(
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100.0 * (medians["L116"][key] / medians[comparison][key] - 1.0), 2)
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summary.append(item)
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(OUT / "summary.json").write_text(
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json.dumps({"runs": rows, "summary": summary}, indent=2) + "\n",
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encoding="utf-8")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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