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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
45 lines
2.0 KiB
Markdown
45 lines
2.0 KiB
Markdown
# Class 6 — SIMD
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Autovectorization enablement first, intrinsics last (SPEC §4).
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## Preconditions
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- Profile signature: hot, trip-count-heavy inner loops over contiguous
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numeric/byte data; scalar math or byte-wise processing in the top
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frames; disassembly of the hot loop shows scalar ops where vector ops
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are plausible.
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## Procedure (cheapest first)
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1. Read the optimizer's mind: check the hot loop's assembly (or
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`-Cremark`/`-Rpass=loop-vectorize`) for *why* vectorization failed.
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2. Remove the blocker, in order of cheapness:
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- Bounds checks: restructure with slices/iterators or hoist a single
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length check (`&x[..n]` pattern) — not `unsafe` indexing.
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- Aliasing: split borrows, `split_at_mut`, pass slices instead of
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overlapping raw pointers.
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- Loop-carried dependencies: reassociate integer reductions, unroll
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accumulators.
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3. Widen the ISA baseline if class 1 hasn't already (`target-cpu`), since
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SSE2-only baselines strangle the vectorizer.
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4. `std::simd` / portable-SIMD explicit vectorization.
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5. Raw intrinsics only when all of the above fail and the win is large;
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isolate behind a scalar-fallback path selected at runtime.
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## Verification notes
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- **FP flags are a hard line**: any `-ffast-math`-class flag, FP
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reassociation, or FMA-contraction change routes to
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`needs-human-review` — no exceptions, even for a 2x win (SPEC §8).
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Integer/byte SIMD has no such taint and can auto-accept.
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- Intrinsics imply `unsafe` → automatic `needs-human-review` + MIRI
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where feasible + differential fuzz against the scalar path with
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adversarial lengths (0, 1, lane-1, lane, lane+1, unaligned offsets).
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## Known failure modes
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- Tail handling: the classic SIMD bug class. Fuzz lengths around lane
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boundaries explicitly.
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- Alignment assumptions that hold on the bench box and fault (or crawl)
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elsewhere.
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- Runtime feature detection done per-call in the hot loop, eating the
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win; hoist detection to init.
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- Integer reduction reassociation is safe; float reduction reassociation
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changes results — do not confuse the two.
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