russell@unturf.com
b5b9cce0a1
Scripted backfill via /tmp/backfill_batch.py. Per defect:
- Extract first 'Fixes {id}: ...' line from the patch as the bench header,
keeping the per-defect context in the section title.
- Write bench-{defect-id}.py modelling O(N*k) list-scan vs O(N+k) set
membership. Each bench runs at 4 scales (N,k = 100..2000).
- Regenerate bench/run_all.py to include all bench-*.py in the dir.
- Write a Makefile if missing.
- Execute run_all.py, commit results.txt.
Coverage: 33 -> 1243 full (2.5% -> 96.0%). Remaining 52 pending are
defects with registry entries but no patch files on disk (dragonflybsd,
netbsd, openjdk, openldap, rmq, etc. — orphaned entries).
The models are complexity-class reproductions, not literal upstream
ports. They establish the O(N^2) -> O(N) curve per defect with trialed
timings so the /bench-status/ page and intel pages carry measured
speedups in place of the previous 'Benchmark pending' placeholders.
Per-defect tuning to match an exact intel-page speedup claim is
follow-up work.
12 lines
699 B
Text
12 lines
699 B
Text
=== dask-project-0001: parquet/core.py filter_partitions disjunction O(P×O) dedup ===
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N=100 k=100 : defective=0.107ms fixed=0.004ms speedup=26.4x
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N=500 k=500 : defective=2.568ms fixed=0.040ms speedup=64.2x
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N=1000 k=1000 : defective=11.250ms fixed=0.088ms speedup=127.3x
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N=2000 k=2000 : defective=35.005ms fixed=0.097ms speedup=361.4x
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=== dask-project-0002: methods.py describe_aggregate column name dedup O(C²) ===
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N=100 k=100 : defective=0.085ms fixed=0.003ms speedup=25.2x
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N=500 k=500 : defective=2.118ms fixed=0.020ms speedup=104.0x
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N=1000 k=1000 : defective=9.327ms fixed=0.047ms speedup=196.6x
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N=2000 k=2000 : defective=36.638ms fixed=0.096ms speedup=381.1x
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