Antigravity Agent
159 lines
5.0 KiB
Markdown
159 lines
5.0 KiB
Markdown
---
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id: wiki-2026-0508-cpu-bottleneck
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title: CPU Bottleneck
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [CPU-Bound, Compute Bottleneck]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [performance, profiling, cpu]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: applied
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tech_stack:
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language: C++/Rust/JS
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framework: perf/Instruments/Chrome DevTools
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---
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# CPU Bottleneck
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## 매 한 줄
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> **"매 GPU 가 놀고 main thread 가 100% 면 CPU bottleneck."**. CPU bottleneck 은 frame budget 16.7ms (60fps) 또는 11ms (90fps XR) 안에 main thread 작업이 안 끝나는 상태. 2026 진단: Chrome Performance panel + perf + Instruments → fix: WebWorker / WASM SIMD / off-main-thread / batching.
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## 매 핵심
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### 매 진단 신호
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- GPU utilization < 70% but FPS drop.
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- Long Task > 50ms in Performance panel.
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- `perf top` 의 single function 이 hot.
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- Profile 의 self-time 이 한 함수에 집중.
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### 매 Bottleneck Source
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- **Main-thread JS**: parse, layout, large loop.
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- **Layout thrash**: read-write-read DOM.
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- **GC pause**: allocation pressure.
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- **Synchronous IO**: blocking syscall.
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- **Unoptimized algorithm**: O(n²) on hot path.
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- **Single-core saturation**: no parallelism.
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### 매 Fix Strategy
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1. **Profile first** — 매 measure, not guess.
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2. **Off-main-thread**: WebWorker, OffscreenCanvas.
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3. **Batch**: requestAnimationFrame, microtask.
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4. **SIMD/WASM**: 매 hot inner loop.
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5. **Algorithmic**: O(n²) → O(n log n).
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6. **Cache**: memoize, weak-ref.
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7. **Lazy**: defer, code-split.
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## 💻 패턴
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### Detect long task
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```javascript
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const obs = new PerformanceObserver(list => {
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for (const e of list.getEntries()) {
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if (e.duration > 50) console.warn('long task', e.duration, e.name);
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}
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});
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obs.observe({ entryTypes: ['longtask'] });
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```
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### Move work to Worker
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```javascript
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// main.js
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const w = new Worker('worker.js', { type: 'module' });
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w.postMessage({ data: bigArray }, [bigArray.buffer]); // 매 transfer, zero-copy
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w.onmessage = e => render(e.data);
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// worker.js
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self.onmessage = e => {
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const result = heavyCompute(e.data.data);
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self.postMessage(result, [result.buffer]);
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};
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```
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### WASM SIMD hot loop (Rust)
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```rust
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#[target_feature(enable = "simd128")]
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unsafe fn dot_product(a: &[f32], b: &[f32]) -> f32 {
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use std::arch::wasm32::*;
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let mut sum = f32x4_splat(0.0);
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for i in (0..a.len()).step_by(4) {
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let va = v128_load(a.as_ptr().add(i) as *const v128);
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let vb = v128_load(b.as_ptr().add(i) as *const v128);
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sum = f32x4_add(sum, f32x4_mul(va, vb));
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}
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f32x4_extract_lane::<0>(sum) + f32x4_extract_lane::<1>(sum)
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+ f32x4_extract_lane::<2>(sum) + f32x4_extract_lane::<3>(sum)
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}
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```
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### Time-sliced loop (yield to event loop)
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```javascript
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async function processChunked(items) {
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const CHUNK = 200;
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for (let i = 0; i < items.length; i += CHUNK) {
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items.slice(i, i + CHUNK).forEach(processOne);
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await new Promise(r => setTimeout(r, 0)); // 매 yield
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}
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}
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// 또는 scheduler.yield() (2025+)
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if ('scheduler' in window && 'yield' in scheduler) await scheduler.yield();
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```
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### Batch DOM read/write
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```javascript
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// 매 안티 — layout thrash
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items.forEach(el => { const w = el.offsetWidth; el.style.width = (w*2)+'px'; });
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// 매 fix — read first, then write
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const widths = items.map(el => el.offsetWidth);
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items.forEach((el, i) => { el.style.width = (widths[i]*2)+'px'; });
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```
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### Linux perf hot function
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```bash
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sudo perf record -F 99 -g -p $(pidof myapp) -- sleep 10
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sudo perf report --stdio | head -40
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sudo perf script | stackcollapse-perf.pl | flamegraph.pl > flame.svg
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Long JS function | WebWorker / time-slice |
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| Image/video pipeline | OffscreenCanvas |
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| Number crunching | WASM SIMD / GPU compute |
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| Layout thrash | read-then-write batch |
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| GC pressure | object pool |
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| Multi-core unused | Worker pool / parallel |
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**기본값**: 매 measure → identify hot fn → off-main-thread or algorithmic fix.
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## 🔗 Graph
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- 부모: [[Analyze runtime performance]] · [[Flame_Graphs]]
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- 변형: [[Draw Call]]
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- 응용: [[Tree Shaking (번들 크기 최적화)]] · [[Frustum Culling]]
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- Adjacent: [[Memory Management]] · [[Branch Prediction]]
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## 🤖 LLM 활용
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**언제**: profile flamegraph 해석, hot-function refactor 제안, perf annotation.
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**언제 X**: 매 actual perf measurement — deterministic 도구가 정확.
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## ❌ 안티패턴
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- **Premature optimization**: 매 profile 없이 추측 — 잘못된 부분 fix.
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- **Worker overuse**: 매 small task 의 postMessage 오버헤드 > 이득.
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- **`while(true)` busy-wait**: 매 throttle / requestIdleCallback 사용.
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- **Synchronous XHR**: 매 deprecated, main-thread block.
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## 🧪 검증 / 중복
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- Verified: Chrome Performance docs; web.dev Long Tasks; Linux perf-tools (Brendan Gregg).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — diagnosis + Worker/SIMD/yield patterns |
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