Js perf notes

JavaScript performance optimization — transferable principles

When this skill is invoked, apply the principles below for the rest of the performance-optimization work in this session — let them guide how you profile, what you change, and how you measure. If no target was given alongside the invocation (no file, function, diff, or profile to work on), ask what to optimize before proceeding; don't summarize this document back to the user.

Scope: this is about CPU-bound JavaScript/TypeScript — hot loops, algorithms, data structures, parsers, numeric/geometry/graphics kernels — where a profiler points at a function doing actual computation. It is not about DOM/layout/rendering, React re-renders or memoization, bundle size, hydration, or network/page-load latency; those are dominated by the browser and framework, and the V8-level technique here won't move them. If the slowness is there, say so rather than misapplying these notes.

This is a reference to consult, not a procedure to run start-to-finish — only "The loop" below is sequential. Techniques live in on-demand files under techniques/; see the catalog below.

The loop

Every optimization session is the same cycle:

1. Profile the real workload (see #4 for tooling).
2. Hypothesize from the profile — not from reading the code (#3).
3. Change one thing — routing to the matching techniques/ file via the catalog below once the bottleneck type is clear.
4. Verify correctness against the safety net (#1).
5. Measure against noise (#5), then keep or revert — never skip this and claim the win anyway; "should be faster" is a hypothesis, not a result.
6. Re-profile before the next change. The distribution shifts after every kept win; a stale profile sends you chasing bars that no longer exist.

1. Make change safe before you make it fast

Before optimizing, decide how you'll confirm behavior didn't change — and make that check trustworthy and fast enough to run after every edit.

• A regression net you trust lets you refactor boldly. Without one you stay timid, making only the small changes you can eyeball — which are rarely the big wins.
• Prefer verifying against an independent source of truth (a known-good output set, a reference implementation, a property the result must satisfy) over "matches what it did yesterday." The former can also catch bugs that were already there; the latter just freezes whatever you started with.
• The time spent building the check is almost always repaid by the optimizations it unlocks. Build it first.

2. Tallest bars first, then the aggregate

Attack the highest self-time functions first — then switch to sweeping the whole distribution once the obvious peaks are gone.

• First pass: the peaks. A tall bar is often a blunder — accidental O(n²), redundant work, pathological allocation, or a call that shouldn't be hot. Clear these cheap, outsized wins first.
• Then sweep the flattened distribution. Once the peaks are genuine costs, leverage moves to mechanical techniques applied broadly: flatten dispatch, kill allocation, specialize common shapes. Don't keep sniping once the profile says the costs are spread out.
• Question the algorithm before sweeping mechanically. Is the data structure right for the workload's actual access pattern? Is there a known algorithm with better complexity or constants for this problem shape? Loop-level polish can't recover what a wrong algorithm gives away. And when no JS-level change suffices, ask whether the work belongs in this layer at all: precompute offline, cache at a higher level, or drop the kernel to WASM/SIMD.

3. Read the profile, not your cost model

The profile is the authority; your static reading of the code is a hypothesis.

• Don't trust an a-priori cost ranking — profile the real workload first. Static reading enumerates candidates; the profile ranks them.
• Self-time is reported after inlining. The engine folds small helpers into their callers, so a tall self-time bar may actually be inlined callees doing the work — and a helper that looks cheap may be hot everywhere it gets inlined. Before acting on a bar, drop to line-level / position ticks to confirm what's actually executing there. (Seen: 17% self-time in a function whose cost was an inlined sort; the sort's own frame read 2.7%.)
• Group by a domain-meaningful dimension and read per-line — they answer different questions. Per-group (by input type/config/size) tells you which workload dominates; per-line tells you which code executes most. They disagree when a hot line cross-cuts groups: the per-group view blames one category while a single line running across all of them is the real peak. Reconcile both before acting.
• Counter-first on algorithmic ideas — instrument the domain counts before prototyping. A profile tells you where time goes; before building a structural change, quantify the premise it rests on with a cheap counter: the prune rate the new index would buy, the fraction of iterations wasted, the composition of the candidate set you mean to filter. A false premise dies for free this way — no code written. The profile ranks the code you have; counters test the code you're about to write.

4. Capturing evidence: the profiling toolkit

Get the profile headlessly, then confirm hypotheses with trace flags.

• Sampling profile — where time goes. flamebearer summarizes Chrome traces (.json/.gz) and Node .cpuprofiles into a text report; flamebearer-node bench.js profiles a script in one step (--help for flags). Always run which flamebearer-node first: if installed (or repo-local), use it — no approval needed, and its --stacks <fn> flag gives per-line self-time including inlined code, the line-level confirmation #3 demands before acting on a tall bar. Only if absent (and you'd rather not ask to npm i -g flamebearer), fall back to node --cpu-prof bench.js, which writes a *.cpuprofile (plain JSON — parse it directly); the discipline (#2, #3) matters more than the tool.
• Trace flags — why the engine couldn't make it fast. node --trace-deopt … | grep <fn> shows a hot function kicked back to baseline and names the unstable type/field; --log-ic surfaces inline-cache state transitions (evidence for techniques/dispatch-inlining.md); --allow-natives-syntax + %GetOptimizationStatus(fn) after warmup confirms it actually reached the optimizing tier.
• The two are complementary — get trace evidence before an engine-level rewrite about deopts, inlineability, dispatch, or type instability.

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Technique catalog — load by bottleneck type

Once the profile tells you what kind of bottleneck you have, read the matching file below. Start with the one the profile points at; load another only when evidence or a cross-reference points there.

• Call/dispatch overhead → techniques/dispatch-inlining.md. Symptoms: a hot loop calling helpers; polymorphic/megamorphic call sites (--log-ic shows unstable inline-cache state); a function too big to inline its callees; deopts from type instability. Often the highest-leverage mechanical technique — load this first when the hot bar is doing dispatch rather than compute.
• Memory: allocation, layout, locality, bulk ops → techniques/memory.md. Symptoms: GC is a real slice of the profile; the loop is cache-miss-bound (cost on a dereference, not the math); per-iteration allocation; or you're choosing a representation (typed arrays vs objects, SoA vs AoS, packing fields).
• Loop-body compute → techniques/loops.md. Symptoms: a profiler-flagged hot loop doing redundant per-iteration work, re-deciding a branch/config every iteration, transcendental/library math per element, or data-dependent branches.

These files cite V8 specifics (SMI ranges, inlining budgets, shape monomorphism); other engines share the principles but differ in detail, so re-verify browser wins rather than asserting V8 behavior as universal.

5. Measuring against noise

• Run-to-run variance can be larger than the win you're chasing. A single before/after pair is not evidence.
• A hot spot that's a small fraction of total can't be measured on the full-workload bench — isolate it. Below roughly the bench's noise floor (single-digit % of total, even with interleaving), a real win on one function is invisible in end-to-end numbers because the noise on everything else swamps it. Pull that function into its own microbench driven by representative inputs and measure it directly; then sanity-check that the end-to-end number at least didn't regress. (Seen: a ~2.5% sort improvement that was unmeasurable on the full bench and only resolved in isolation.)
• Let the JIT warm up before you trust a number. Cold code runs in the interpreter/baseline tier, not the optimized tier that ships hot — benchmarking it measures code that never actually runs in production. Discard warmup iterations, or run long enough that the function tiers up before you record. (This interacts with dispatch — see techniques/dispatch-inlining.md: a call site that's polymorphic only during warmup can mislead in both directions.)
• Make sure the benchmark's work can't be eliminated. The JIT discards computation whose result is provably unused — a loop that calls the function under test and ignores the return value can measure nothing at all. Accumulate results into a checksum (or otherwise consume them after the loop) so every iteration's work stays observable.
• Don't let the timer outweigh what it times. At ns/op scale a per-iteration performance.now() (or counter bump) is itself a cost and a distortion of the profile (seen: two now() calls per op were ~10% of it). Time coarse-grained — wrap the whole loop — and prefer a sampling profiler over manual instrumentation for hot inner loops.
• Interleave A and B runs (ABAB), not batched (AAAA-BBBB). Thermal throttling, clock drift, and accumulating background load bias whichever variant runs later; alternating cancels the drift instead of attributing it to one side.
• Control the machine, not just the code. Background load, thermal throttling, efficiency vs performance cores (Apple Silicon may schedule a long-running quiet process onto E-cores), and Node version drift each move numbers more than a small win. Compare only runs from the same Node binary on the same machine state; treat any cross-machine or cross-version comparison as a different experiment.
• Use a stable summary across repeated runs (e.g. best-of-best over N runs) when the metric is noisy; keep N high enough that the clusters separate.
• To isolate one change's effect, revert only the code under test — not the scaffolding (build config, harness, fixtures) the measurement needs to run.
• "Does less work" is a hypothesis, and data-dependent. Provably fewer operations can still be slower (e.g. several targeted small ops losing to one blanket bulk op when the data favors the contiguous path). Validate every "obviously cheaper" change on a representative workload; re-measure before carrying it to a different distribution.

6. Before reporting: the gate

Every claim in the report must pass all four:

• Measured, not reasoned. No unmeasured "should be faster" or kept-because- it-can't-hurt wins — label hypotheses as hypotheses (see #5).
• Raw numbers + exact commands, so the reader can reproduce them and spot what a summary hides (bimodal runs, one outlier carrying the average).
• Per-case numbers, not one blended aggregate that drowns individual wins.
• Untested workloads/distributions/engines stated. A win carried silently to a different distribution is how regressions ship with a green report.

7. Don't trade correctness for speed silently

• Removing work because "the only current caller doesn't need it" is a latent bug. Guard it behind a cheap flag computed once instead, so it stays correct for any future caller at the same speed.

Two final habits, easy to forget

• Welcome a second set of eyes. Fresh perspective can spot a redundant pass that incremental profiling normalized into the background; your own profile, read alone, anchors you to expected costs.
• Distrust the opening plan. It's the hypothesis you're least likely to re-examine once you start building. Spike its premise first. (Seen: a plan to replace a spatial sort everywhere, redirected after spikes showed the sort was irreplaceable and the real win lived elsewhere.)

The techniques are common knowledge. The discipline — a trusted safety net, real profiles, cheap premise spikes, and honest measurements — is what produces the large win.