Perf Profile

Performance is a contract with reality. Intuition about hot paths is wrong more often than right. Capture, locate, hypothesize, optimize, re-measure, prove the regression with a benchmark — then defend the win with an invariant.

When to Apply / NOT

Apply: latency or throughput SLO violation; memory pressure (RSS growth, GC churn); benchmark regression; pre-optimization scoping; cold-start vs steady-state cost split; cache locality / branch-prediction concerns.

NOT apply: defect with wrong outputs; architectural redesign; micro-optimization without budget pressure; untested code (write tests first).

Anti-patterns

• Optimize without profile: intuition-driven changes.
• Single-run benchmarks: variance dominates. Use hyperfine --warmup 3 --min-runs 10.
• Profile in debug build: optimizer-disabled binaries lie.
• Confuse flat profile with call-graph: self-time vs total-time tell different stories.
• Ignoring tail latency: p50 stays flat while p99 explodes.
• Cherry-picking the win: re-measure end-to-end.
• Allocation blind spot: CPU profiler hides GC.
• Forgetting the regression guard.

Workflow (language-neutral)

1. Define budget — restate target metric: latency p95 Y rps, RSS < Z MB.
2. Establish baseline — run unoptimized workload under hyperfine plus profiler. Save raw artifacts.
3. Capture profile — sampled CPU profile → flamegraph; allocation profile if memory-bound; latency histogram for tail.
4. Locate hotspot — top self-time function or widest plateau. Cross-check with allocation profile.
5. Hypothesize — one falsifiable claim with predicted delta.
6. Optimize minimally — smallest change targeting the hypothesis.
7. Re-profile — capture same metric; differential flamegraph.
8. Prove the win — hyperfine 'baseline' 'optimized' --warmup 3 --min-runs 10.
9. Guard the win — add CI benchmark with regression bound.

Reading Flamegraphs

• Wide plateau on top: hot self-time function — primary target.
• Narrow towers: deep call chains — examine for over-abstraction.
• Repeated motifs: same callee under many parents — candidate for inlining or caching.
• Missing frames: rebuild with -fno-omit-frame-pointer / RUSTFLAGS=-C force-frame-pointers=yes.
• Differential flamegraph (hotspot --diff): red = added cost, blue = removed.

Parallel Tooling

| Family | CPU sampling | Memory / alloc | Differential / benchmark | |---|---|---|---| | Systems (C/C++/Rust) | perf record + flamegraph, samply, valgrind --tool=callgrind | valgrind --tool=massif, heaptrack, dhat | hyperfine, criterion, iai-callgrind | | Python | py-spy record, scalene, cProfile + snakeviz | scalene (memory mode), tracemalloc, memray | pytest-benchmark, hyperfine | | Go | go tool pprof (cpu profile), runtime/pprof | go tool pprof (heap), runtime.MemStats | go test -bench -benchmem, benchstat | | Java/Kotlin | async-profiler, JFR (jcmd JFR.start) | JFR allocation events, Eclipse MAT on heap dump | JMH, hyperfine | | JavaScript/TypeScript | Chrome DevTools, node --prof, clinic flame | Chrome heap snapshot, clinic doctor | tinybench, mitata, hyperfine | | OCaml | landmarks, magic-trace | memtrace + memtrace-viewer, Statmemprof | bechamel, core_bench, hyperfine |

Use procs (not ps). Use bat -P -p -n (not cat). Use difft (not diff). Use hyperfine (not time).

Constitutional Rules

1. No optimization without a profile.
2. One hypothesis per iteration.
3. Re-measure end-to-end.
4. Variance-aware comparison with hyperfine --warmup --min-runs.
5. Guard every win in CI bench.
6. Correctness first — keep TDD green.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: OutlineDriven
• Source: OutlineDriven/odin-codex-plugin
• License: Apache-2.0
• Homepage: https://outlinedriven.github.io

Install and usage instructions live in the source repository linked above.