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Claude
328aafae77
Commit the spec and scaffold the system it describes:
- SPEC.md: the full v0.1 engineering spec
- crates/: trust-layer Rust workspace, unit-tested
- bench-runner: interleaved ABAB scheduling with discarded warm-ups,
deterministic bootstrap CIs over the ratio of medians,
CI-lower-bound accept rule, env fingerprinting, and a CLI with
compare + A/A self-test modes (A/A verified passing end-to-end)
- diff-test: gate outcome model, sha256 corpus pin verifier that
refuses on mismatch, per-target equivalence policy parser
(byte-identical default, explicit fp-tolerance opt-in)
- ledger: append-only SQLite attempt schema with UPDATE/DELETE
refused by triggers, anti-double-attempt query
- report: cores-saved and dollars/yr ROI math over speedup CIs,
pricing.toml inputs
- playbook/: classes 1-7 with preconditions, procedure, verification
notes, and known failure modes per class
- agent/: Python package skeleton — six-tool contract and prompting
spine, harness IPC deferred to Phase 1
- config/: accept.toml (2% threshold, 30 runs/side), pricing.toml
- targets/, corpora/, results/calibration/: layout and policy docs
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
42 lines
2.0 KiB
Markdown
42 lines
2.0 KiB
Markdown
# Class 1 — Build configuration
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Zero source changes. Always first: a large fraction of real-world CPU is
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lost to shipping defaults.
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## Preconditions
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- Any CPU-bound target not already using an optimized release profile.
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- Profile signature: flat-ish flamegraph with time spread across many
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small frames (inlining starved); heavy call overhead into tiny leaf
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functions; or a build that visibly lacks `-O2`/`--release`.
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- Check before anything else: `opt-level`, `lto`, `codegen-units`,
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`panic`, `target-cpu` (Rust); `-O`, `-march`, `-flto` (C/C++).
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## Procedure (cheapest first)
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1. Confirm release profile: `opt-level=3`, `panic=abort` where the target
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permits, `codegen-units=1`.
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2. `target-cpu=native` / `-march=native` — only when the deployment
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fleet's ISA baseline is known; record the chosen baseline in the
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hypothesis.
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3. LTO: thin, then fat. Watch build-time cost; fat LTO on huge targets
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can be a net loss for the engagement.
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4. PGO: instrument with the golden-replay corpus as the training run,
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rebuild with the profile. The corpus is representative by
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construction.
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5. BOLT (post-link layout) where the binary and toolchain permit.
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## Verification notes
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- Flags must not include any `-ffast-math`-class option — that's an
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equivalence change, auto-routed to `needs-human-review` (SPEC §8).
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- Byte-identical golden replay still applies: build flags occasionally
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change FP contraction on some ISAs (`-march` enabling FMA); if golden
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replay diverges on numeric output, stop — this class is not
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equivalence-preserving for that target without a tolerance policy.
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## Known failure modes
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- `target-cpu=native` on the bench machine but not the fleet: a real win
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that the customer cannot deploy. Pin the ISA baseline to the fleet.
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- PGO trained on a non-representative workload overfits the benchmark —
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hold out workloads (SPEC §11).
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- Layout changes from LTO/BOLT interact with layout bias (§7): measured
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wins here *especially* need the randomize-and-aggregate protocol.
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