performance-investigation

Quick start

1. Start from a branch you want to measure (for example foo).
2. Run:

pnpm bench:compare main foo

If you pass one branch, bench:compare automatically compares it to main.

Where outputs go

• Summary report: benchmarking/compare/.results/report.txt
• Raw benchmark numbers:
  • benchmarking/compare/.results/main.json
  • benchmarking/compare/.results/<your-branch>.json
• CPU profiles (per benchmark, per branch):
  • benchmarking/compare/.profiles/main/*.cpuprofile
  • benchmarking/compare/.profiles/main/*.md
  • benchmarking/compare/.profiles/<your-branch>/*.cpuprofile
  • benchmarking/compare/.profiles/<your-branch>/*.md

The .md files are generated summaries of the CPU profile and are usually the fastest way to inspect hotspots.

Suggested investigation flow

1. Open benchmarking/compare/.results/report.txt and identify largest regressions first.
2. For each high-delta benchmark, compare:
   • benchmarking/compare/.profiles/main/<benchmark>.md
   • benchmarking/compare/.profiles/<branch>/<benchmark>.md
3. Look for changes in self/inclusive hotspot share in runtime internals (runtime.js, reactivity/batch.js, reactivity/deriveds.js, reactivity/sources.js).
4. Make one optimization change at a time, then re-run targeted benches before re-running full compare.

Fast benchmark loops

Run only selected reactivity benchmarks by substring:

pnpm bench kairo_mux kairo_deep kairo_broad kairo_triangle
pnpm bench repeated_deps sbench_create_signals mol_owned

Tests to run after perf changes

Runtime reactivity regressions are most likely in runes runtime tests:

pnpm test runtime-runes

Helpful script

For quick cpuprofile hotspot deltas between two branches:

node

This prints top function sample-share deltas for the selected benchmark.

Practical gotchas

• bench:compare checks out branches while running. Avoid uncommitted changes (or stash them) so branch switching is safe.
• Each bench:compare run rewrites benchmarking/compare/.results and benchmarking/compare/.profiles.