peltier.io/CLAUDE.md

hotpath — profile-guided optimization agent

Working name. Rename freely; nothing depends on it.

Mission

An agent that profiles real binaries and services, isolates hot paths, proposes optimizations, and accepts a change only when (a) behavioral-equivalence gates pass and (b) the benchmark improvement is statistically significant on trusted infrastructure. Output: verified performance deltas translated into ROI reports nobody can dispute.

Full engineering spec: SPEC.md. Read it before starting any phase.

Non-negotiables (re-read every session)

1. No unverified performance claims. A change without passing equivalence gates AND a significant bench delta does not exist. Discard it. Log it.
2. Measure before optimizing. No patch is proposed until profile data identifies the hot path. Never trust a single benchmark run.
3. Cheap wins first, always. Build flags → LTO → PGO → allocator swap → then code changes. Check the ledger before re-attempting any class of change on a target.
4. Equivalence gates are hard gates. Changes touching floating-point ordering, concurrency primitives, or anything UB-adjacent are NEVER auto-accepted — verdict is needs-human-review.
5. Every attempt goes in the ledger, including failures and rejections. Failures are future training data.
6. The agent never modifies the trust layer. crates/, config/, corpora/, and upstream test suites are read-only to the agent. Patches may touch only allowlisted paths under targets/<name>/. See SPEC.md §10.
7. Sandbox everything. Target code runs only inside no-network containers. Never on the host.
8. Statistical bar: accept only if the bootstrap 95% CI lower bound of speedup ≥ threshold in config/accept.toml (default 2%), from interleaved A/B runs on the same pinned hardware.

Architecture map

• crates/bench-runner — trust layer: containerized, CPU-pinned, interleaved A/B benchmarking with bootstrap confidence intervals and A/A self-tests
• crates/diff-test — behavioral equivalence: upstream test suite + golden I/O replay + differential fuzzing of changed functions + sanitizers
• crates/ledger — append-only SQLite record of every attempt: hypothesis, patch, gate results, bench deltas, verdict, cost
• crates/report — ROI generator: cores saved × $/core-hr, latency percentile deltas, CIs, workload caveats printed on every number
• agent/ — Claude Agent SDK (Python) loop, prompts, tool definitions
• targets/ — vendored OSS targets for case studies (permissive licenses only)
• playbook/ — optimization classes, ordered, with preconditions and known risks
• results/ — generated reports and flamegraphs

Phase 4 COMPLETE — all SPEC §5 exit criteria met. Phases 0–4 are all done. Service-mode latency benchmarking shipped: a coordinated-omission-correct, open-loop, fixed-rate load generator (crates/bench-runner/src/service.rs) measuring latency from each request's intended send time (queueing counted, not hidden), interleaved A/B, exact percentiles, bootstrap p50/p99 CIs. A real service target — targets/cjson/service.c, a minimal HTTP server wrapping the patched cJSON.c (trust-layer, outside the workspace allowlist). Service calibration PASS (0/10 A/A false-positive, 10/10 injected-5%-latency-regression detection; results/calibration/cjson-service-aa.json). The verified batch win phase3-cjson-002 measured under a 150 rps replay (20 rounds, 40k requests, 0 drops): p50 latency +6.2%, 95% CI [+5.8%, +7.2%] — accepted (phase4-cjson-service ledger row); p99 NOT claimed (CI [0.07, 4.97], single-worker loopback tail jitter) — harness correctly rejected it. Mechanical ROI report generator (crates/report bin, just report, SPEC §9): reads a ledger row + service-latency JSON → throughput→cores→dollars (27.5 cores / $9,621-per-year CI lower bound on a 500-core fleet) and latency percentiles, every figure with CI + workload + methodology inline; flags any non-accepted row. Case study: results/phase4/. The roadmap is complete — the profile→hypothesize→patch→gated-verdict→ROI loop runs end to end across Rust and C, batch and service, with zero shipped false accepts across all phases.

Phase 5 COMPLETE — the two SPEC §13 research forks, both built and both routed to human review. (a) Learned class-selection policy (crates/policy, cargo run -p policy): reads the append-only ledger and ranks optimization classes by the Wilson lower bound of their shippable-win rate — a learned prior over the fixed cheapest-first ordering, advisory only (gates still decide). A "win" counts only machine-sanitizer-verified accepts; the overturned comrak-010 and tier-gated mimalloc are excluded/held. On the current 34-row ledger it recommends algorithmic first (Wilson lb 0.066). (b) Kernel lane (targets/matmul/kernel.c, crates/diff-test FP-tolerance policy + fp-compare bin, scripts/kernel-lane-demo.sh): a matmul optimization (transpose-B cache locality + eight-accumulator ILP) that reorders FP accumulation, so byte-identical golden replay is the wrong gate — 244,901/262,144 values differ. The FP-tolerance gate (abs 1e-4 + rel 1e-3) accepts it, still REJECTS a +0.5 perturbation, and the bench measures 3.23× [3.16, 3.26] with the same interleaved A/B + bootstrap CI. Ledger row phase5-matmul-opt = needs-human-review (using the tolerance tier is a §8 signal, same posture as mimalloc). No GPU in this environment, so the GPU fork is the identical shape shown on CPU — only the timer and hardware change. Case study: results/phase5/; docs chapter docs/src/research-forks.md. Open follow-up: the standing accept-scoped ruling on phase0-comrak-002 (mimalloc) is resolved (results/rulings/).**

Commands

Fill these in as the harness is built; keep them current:

just profile <target>       # perf record + flamegraph for a target's benchmark workload
just bench <target> <ref>   # interleaved A/B: pristine baseline vs working tree
just gates <target>         # test suite + golden replay + diff-fuzz + sanitizers
just verdict <target>       # runs gates + bench, writes ledger row, prints verdict
just report <run-id>        # ROI report from ledger
just aa <target>            # A/A calibration run (must show null result)

Built so far (command-level, pre-target-integration):

just build / test / lint    # trust-layer workspace: cargo build/test/clippy+fmt
just aa [cmd]               # A/A self-test of bench-runner on a shell command
just compare <a> <b>        # interleaved A/B of two shell commands, bootstrap CI
just gates <target>         # corpus pin + upstream tests + golden replay
just pin-check <target>     # verify corpus MANIFEST.sha256
just calibrate <cmd> <out>  # automated A/A + regression-injection calibration
just verdict <t> <bin> ...  # gates + bench vs pristine-rebuilt baseline + ledger row
cargo run -p harnessd       # agent IPC daemon: six-tool JSON surface (SPEC §3.5)

Definition of done — one optimization attempt

• Hypothesis logged in ledger before patching
• Patch touches only allowlisted paths under targets/<name>/
• Upstream test suite green
• Golden replay byte-identical (or within the target's explicit FP tolerance policy)
• Differential fuzz on changed functions: no divergence (60s or 10k iterations minimum)
• ASan + UBSan clean; TSan if the patch touches anything threaded
• Interleaved A/B bench passes the CI-lower-bound threshold
• Ledger row written with verdict and full evidence
• If FP-ordering / concurrency / UB-adjacent: verdict needs-human-review, never auto-accept

An attempt that fails any gate is a valid, complete outcome — write the ledger row and move on. Do not iterate on a rejected patch more than twice without a new hypothesis.

Conventions

• Rust workspace (edition 2021+) for all harness crates; just for task running
• Python 3.11+ for agent/; Claude Agent SDK — verify current package name and API at https://docs.claude.com/en/docs/claude-code/overview before scaffolding
• Every number shown to a human carries its confidence interval and workload description. No naked percentages, ever.
• Commit style: phase0:, bench:, gates:, agent:, playbook: prefixes
• When in doubt between shipping a feature and hardening measurement, harden measurement. The product is trust.