chore: agent hints for perf investigations (#18047)

This adds some hints in an `AGENTS.md` for how to conduct performance
investigations, along with a utility for comparing profiles from
different branches to identify hotspots. We will likely want to put
other stuff in `AGENTS.md` in future but I figured we could start with
something immediately useful.

It also tweaks the comparison script — rather than nuking results from
existing branches, it keeps them around so that we don't need to re-run
benchmarks (which takes a long time) for branches that haven't changed.

---------

Co-authored-by: vercel[bot] <35613825+vercel[bot]@users.noreply.github.com>
pull/18057/head
Rich Harris 3 months ago committed by GitHub
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commit 704e3bb5f0
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---
name: performance-investigation
description: Investigate performance regressions and find opportunities for optimization
---
## Quick start
1. Start from a branch you want to measure (for example `foo`).
2. Run:
```sh
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:
```sh
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:
```sh
pnpm test runtime-runes
```
## Helpful script
For quick cpuprofile hotspot deltas between two branches:
```sh
node benchmarking/compare/profile-diff.mjs kairo_mux_owned main foo
```
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`.

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# Svelte Coding Agent Guide
This guide is for AI coding agents working in the Svelte monorepo.
**Important:** Read and follow [`CONTRIBUTING.md`](./CONTRIBUTING.md) as well - it contains essential information about testing, code structure, and contribution guidelines that applies here.
## Quick Reference
If asked to do a performance investigation, use the `performance-investigation` skill.

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import fs from 'node:fs';
import path from 'node:path';
const [benchmark, baseBranch = 'main', candidateBranch] = process.argv.slice(2);
if (!benchmark || !candidateBranch) {
console.error(
'Usage: node benchmarking/compare/profile-diff.mjs <benchmark> <base-branch> <candidate-branch>'
);
process.exit(1);
}
const root = path.resolve('benchmarking/compare/.profiles');
function safe(name) {
return name.replace(/[^a-z0-9._-]+/gi, '_');
}
function read_profile(branch, bench) {
const file = path.join(root, safe(branch), `${bench}.cpuprofile`);
const profile = JSON.parse(fs.readFileSync(file, 'utf8'));
const nodes = Array.isArray(profile.nodes) ? profile.nodes : [];
const samples = Array.isArray(profile.samples) ? profile.samples : [];
const id_to_node = new Map(nodes.map((node) => [node.id, node]));
const self_counts = new Map();
for (const sample of samples) {
if (typeof sample !== 'number') continue;
self_counts.set(sample, (self_counts.get(sample) ?? 0) + 1);
}
const total = samples.length || 1;
const by_fn = new Map();
for (const [id, count] of self_counts) {
const node = id_to_node.get(id);
if (!node || typeof node !== 'object') continue;
const frame = node.callFrame ?? {};
const function_name = frame.functionName || '(anonymous)';
const url = frame.url || '';
const line = typeof frame.lineNumber === 'number' ? frame.lineNumber + 1 : 0;
const label = url
? `${function_name} @ ${url.replace(/^.*packages\//, 'packages/')}:${line}`
: function_name;
by_fn.set(label, (by_fn.get(label) ?? 0) + count);
}
return { by_fn, total };
}
const base = read_profile(baseBranch, benchmark);
const candidate = read_profile(candidateBranch, benchmark);
const keys = new Set([...base.by_fn.keys(), ...candidate.by_fn.keys()]);
const rows = [...keys]
.map((key) => {
const base_pct = ((base.by_fn.get(key) ?? 0) * 100) / base.total;
const candidate_pct = ((candidate.by_fn.get(key) ?? 0) * 100) / candidate.total;
return {
key,
delta: candidate_pct - base_pct,
base_pct,
candidate_pct
};
})
.sort((a, b) => Math.abs(b.delta) - Math.abs(a.delta))
.slice(0, 20);
console.log(`Benchmark: ${benchmark}`);
console.log(`Base: ${baseBranch}`);
console.log(`Candidate: ${candidateBranch}`);
console.log('');
for (const row of rows) {
const sign = row.delta >= 0 ? '+' : '';
console.log(
`${sign}${row.delta.toFixed(2).padStart(6)}pp candidate ${row.candidate_pct.toFixed(2).padStart(6)}% base ${row.base_pct.toFixed(2).padStart(6)}% ${row.key}`
);
}
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