Profiling the boot answered "is there a real cold-start win?": no wasteful hotspot — the renderer does only ~tens of ms of work at mount, no heavy library (shiki/mermaid/katex/d3/motion) initializes at startup; the rest is Electron runtime + waiting, near the Electron floor. It also exposed that the cold-start number was pessimistic: a fresh --user-data-dir per run means a COLD V8 code cache and worst-case bundle recompile every launch. Real users reuse their profile. Measured delta: fresh (cold cache): spawn→interactive ~1.48s reused (warm cache): ~1.0s So representative launch is ~1.0s; only first-launch-after-install pays ~+400ms. - coldStartSamples() reuses one profile (run 0 warms the cache, discarded; runs 1..N are warm samples), stepping ports + pausing so the single-instance lock releases. `--cold-fresh` measures the first-launch worst case. - Re-baselined cold-start with the representative warm numbers. Net: nothing high-ROI left to optimize. The only lever is shipping a pre-warmed V8 code cache to make first launch match warm (~400ms, once per update) — real packaging complexity for a marginal win, deliberately not pursued. |
||
|---|---|---|
| .. | ||
| lib | ||
| scenarios | ||
| baseline.json | ||
| README.md | ||
| run.mjs | ||
| serve.mjs | ||
Desktop perf harness
One systematized way to measure desktop rendering/interaction performance,
diff it against a committed baseline, and fail on regressions. It replaces the
dozen one-off measure-* / profile-* scripts that each reinvented the CDP
client, arg parsing, stats, and output (and never had a baseline).
Quick start
# Isolated instance (recommended) — no running app or LLM credits needed.
# Its own --user-data-dir + HERMES_HOME means it never collides with `hgui`.
npm run perf -- --spawn
# Or: launch an isolated instance once, attach repeatedly (faster iteration).
npm run perf:serve # leaves an instance on :9222
npm run perf # attaches, runs the CI suite, gates on baseline
# One scenario, with a CPU profile:
npm run perf -- stream --cpuprofile --tokens 800
# Representative PRODUCTION numbers (minified React, not the ~3x-slower dev build):
npm run perf -- cold-start stream keystroke transcript --spawn --prod
# Re-capture the baseline on your reference device, then commit baseline.json:
npm run perf -- cold-start stream keystroke transcript --spawn --prod --update-baseline
Dev vs prod
By default the harness measures the dev renderer (fast to spin up, good for
relative regression checks). Pass --prod (with --spawn) to build a
production renderer with the probe included (VITE_PERF_PROBE=1) and measure
minified React — the representative shipped numbers. The committed baseline is
captured with --prod.
Why isolation matters
The measurement this harness exists to run was historically blocked: a running
hgui holds the Electron single-instance lock, so a second instance quit
immediately. --spawn / perf:serve launch with their own --user-data-dir
(separate lock scope), their own HERMES_HOME (separate backend + sessions),
and their own --remote-debugging-port. Synthetic scenarios drive $messages
directly via window.__PERF_DRIVE__, so no LLM credits are spent.
Scenarios
| scenario | tier | measures | replaces |
|---|---|---|---|
stream |
ci | streaming longtasks, frame p95/p99, mutation cadence | measure-synthetic-stream, profile-synth-stream, profile-long-stream |
stream --real |
backend | same, from a real LLM stream | measure-real-stream, profile-real-stream |
keystroke |
ci | composer keystroke → paint latency | measure-latency, profile-typing, leak-typing |
transcript |
ci | large-transcript mount + paint cost | (new) |
cold-start |
cold | launch → CDP → driver → first paint (fresh spawn/run) | (new) |
first-token |
backend | Enter → first assistant token painted (TTFT) | (new) |
submit |
backend | Enter → cleared → user msg painted, scroll jump | measure-submit, measure-jump |
session-switch |
backend | route → first-paint → settle | profile-session-switch |
profile-switch |
backend | rail click → sidebar settled | measure-profile-switch |
ci + cold scenarios need no backend/credits and are gated against
baseline.json (cold-start requires --spawn since it measures a fresh
launch, and must be run in its own invocation). backend scenarios need a live
backend (and --spawn or a real session/credits) and are report-only.
CPU profiling is a cross-cutting --cpuprofile flag on any scenario (it wraps
the run in Profiler.start/stop and prints a top-self-time table), replacing
every standalone profile-* script.
Adding a scenario
Create scenarios/<name>.mjs exporting { name, tier, description, run(cdp, opts) }
where run returns { metrics, detail } (metrics = flat numbers, lower is
better), then register it in scenarios/index.mjs. If it's ci, add a
baseline.json entry (or run --update-baseline).
Layout
lib/cdp.mjs— the one CDP client + target discovery + typing + CPU-profile wrapper + DOM selectors.lib/stats.mjs— percentiles, histograms, CPU-profile self-time ranking.lib/baseline.mjs— load/compare/update the baseline + regression gate.lib/launch.mjs— attach, or spawn a fully isolated instance.scenarios/— one module per measurement.run.mjs— entrypoint.serve.mjs— standalone isolated launcher.
Not migrated (kept as dev utilities)
eval.mjs, reload.mjs, reload-renderer.mjs, probe-renderer.mjs,
probe-thread.mjs, click-session.mjs, diag-*.mjs are interactive dev
helpers, not benchmarks. They can adopt lib/cdp.mjs in a follow-up.