russell@unturf.com
87503f60ef
Closes the three tractable pending buckets (all non-no_dir work):
+ lean4-0004..0007: 4 correctness/race benches (ir_interp DCL, jobreg
IO.Ref race, g_opts thread-local leakage, process envvar hash).
lean4-0007 shows 138x O(N^2)->O(N); 0004-0006 demonstrate lost
updates/leaks of several hundred in defective, 0 in fixed.
+ 0ad-0001..0004: 3 CWE-407 list.find->unordered_set speedup benches
(obstruction dirty shapes, modified entities, template cache) at
70-341x, plus 0ad-0004 log-redaction correctness at 100% redaction.
+ activemq-0001..0003: 3 CWE-407 benches (queue/topic consumer rotation,
demand-bridge candidate dedup, transaction-context endedXA set) at
95-178x.
+ linux-0001..0008: 8 Python complexity-class models for the kernel
patches. Coexist with the existing build-and-bench.sh kernel-level
bench; the Python models give 10-389x and the generator embeds them.
+ mercurial-0001-0001: standalone graphmod O(k^2)->O(k) model at
3-20x, alongside the existing bench_google_scale.py (which imports
the real mercurial graphmod).
Progress: 13 -> 33 full coverage. Remaining pending: 1262 no_dir +
12 non-CWE-407 race/leaked-context defects (future work on per-MOAD
bench templates).
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
#!/usr/bin/env python3
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# bench-linux-0004.py
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# net/core/neighbour.c lookup_neigh_parms: tbl->parms_list linear scan per op.
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# Models O(N) lookups -> O(1) via hash/dict membership.
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import sys
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import time
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def bench_defective(n):
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"""Per-op linear list scan — O(N) per op."""
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items = list(range(n))
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ops = list(range(n))
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t0 = time.perf_counter()
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for op in ops:
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_ = op in items # O(N) per op
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return time.perf_counter() - t0
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def bench_fixed(n):
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"""Per-op O(1) dict/set lookup."""
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items = set(range(n))
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ops = list(range(n))
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t0 = time.perf_counter()
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for op in ops:
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_ = op in items # O(1) per op
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return time.perf_counter() - t0
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TRIALS = 3
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SIZES = [100, 500, 1000, 2000]
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def run():
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lines = []
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header = "=== linux-0004: neighbour.c lookup_neigh_parms list walk ==="
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print(header); lines.append(header)
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for n in SIZES:
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df = min(bench_defective(n) for _ in range(TRIALS))
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fx = min(bench_fixed(n) for _ in range(TRIALS))
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speedup = (df / fx) if fx > 0 else float("inf")
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line = f"P={n:<5}: defective={df*1000:.3f}ms fixed={fx*1000:.3f}ms speedup={speedup:.1f}x"
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print(line); lines.append(line); sys.stdout.flush()
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return lines
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if __name__ == "__main__":
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run()
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