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Claude
03113312ad
phase3-cjson-002 (class 5+3): stack-buffer number parsing + strtod round-trip replacing sscanf. +8.85% median, 95% CI [+7.5%, +10.0%] on the C target, banked. 100% audit: byte-identical golden replay, independent ASan+UBSan clean, 10k-input differential fuzz 0 divergences, patch bounds/free-gating verified by eye. - verdict: an infrastructure sanitizer-BUILD failure (missing runtime, broken toolchain) now caps the accept at needs-human-review and logs it, instead of crashing the verdict and losing a measured bench. - results/phase3/: four public case studies (tokei class-5 wins; build flags + allocators; the comrak caught false-accept; the cJSON cross-language proof) + README. Compound-speedup claim stated as an approximation, not a measured figure (CIs don't multiply). - .gitignore: cjson build-artifact dirs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
51 lines
2.4 KiB
Markdown
51 lines
2.4 KiB
Markdown
# Case study 2 — cheap wins first: build flags and allocators
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**The playbook's ordering principle (SPEC §3.3, CLAUDE.md
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non-negotiable #3): build flags → LTO → PGO → allocator swap → *then*
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code changes.** The cheapest classes are tried first because they carry
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the lowest cost-per-attempt and the lowest risk. Two of them produced
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Phase-0 results that frame the whole project's discipline.
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## Class 1 — link-time optimization (tokei) — ACCEPTED
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`phase0-tokei-003`: set `lto = true` (fat LTO, up from `thin`) and
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`codegen-units = 1` in tokei's release profile.
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- **Speedup: 1.104 median, 95% CI [1.085, 1.120]** vs the pristine
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`thin`-LTO baseline.
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- Byte-identical golden replay; no source change, so equivalence is by
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construction (same code, different codegen). Auto-accept tier.
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- A pure build-config win worth **+10%** — the cheapest possible class,
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and on this target the single largest individual increment.
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The lesson the ledger enforces: you do not go hand-optimize a hot loop
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before you have tried turning on the optimizer's own switches.
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## Class 2 — allocator swap (comrak) — NEEDS-HUMAN-REVIEW
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`phase0-comrak-002`: swap the global allocator to
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[mimalloc](https://github.com/microsoft/mimalloc).
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- **Speedup: 1.046 median, 95% CI [1.033, 1.058]** — a real, CI-clean
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+4.6% on comrak's Pro Git render.
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- Byte-identical golden replay. And yet the verdict is **not
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`accepted`** — it is **`needs-human-review`**.
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Why: an allocator swap changes the process's memory behavior globally.
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It is exactly the class of change SPEC §8 routes to a human — not
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because this instance is wrong, but because "the benchmark got faster"
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is not sufficient evidence that a global allocator change is safe under
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every workload, fragmentation pattern, and concurrency scenario the
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target will meet in production. The pipeline measured a real win and
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then **refused to auto-ship it**, tagging it for a human ruling instead.
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## What the pair demonstrates
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The cheap classes are not lesser — class 1 produced this project's
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biggest single win. But "cheap to try" is not "safe to auto-accept."
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LTO is deterministic codegen: accept it. An allocator swap is a global
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behavioral change: measure it, prove the speedup, and *still* hand it to
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a human. The tier is decided by the *nature* of the change, never by the
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size of the number — the same rule that later caught a leaking teardown
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patch the bench loved (case study 3).
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