2nd/10_Wiki/Topics/Frontend/GPU_WebGL 파이프라인의 미세 지연(Micro-latency) 측정 사례.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-gpu-webgl-파이프라인의-미세-지연-micro-lat	GPU WebGL 파이프라인의 미세 지연(Micro-latency) 측정 사례	10_Wiki/Topics	verified	self	WebGL Micro-latency GPU Pipeline Latency WebGL Profiling	none	A	0.9	applied	webgl gpu performance latency profiling		2026-05-10	pending	language framework javascript webgl2

GPU WebGL 파이프라인의 미세 지연(Micro-latency) 측정 사례

매 한 줄

"매 GPU 작업은 비동기 — gl.finish() 의 X, query object 의 O". WebGL 의 draw call 은 GPU command buffer 에 enqueue 만 되므로 CPU 측 performance.now() 만으로 측정 시 실제 GPU 시간과 ms 단위 misalignment 발생. EXT_disjoint_timer_query_webgl2 가 매 정답.

매 핵심

매 측정 대상 분리

• CPU time: JS → WebGL command 인코딩 (validate, draw call dispatch).
• GPU time: shader 실행 + raster + blend.
• Present latency: swapchain → display 의 frame pacing (compositor 의존).

매 측정 수단

• EXT_disjoint_timer_query_webgl2 — GPU timestamp query, ns 정밀도.
• gpu.requestAdapter() (WebGPU) — 매 native timestamp-query feature.
• Chrome DevTools Performance → GPU track.
• Spector.js — frame capture + per-call cost.

매 응용

1. 60→120Hz upgrade 시 frame budget shrink (16.6→8.3ms) 의 bottleneck localization.
2. Mobile WebGL 의 thermal throttling 감지 (GPU time 의 점진적 increase).
3. PWA 게임 의 input→render latency end-to-end 측정.

💻 패턴

Timer Query — frame GPU time 측정

const ext = gl.getExtension('EXT_disjoint_timer_query_webgl2');
const queries = [];

function frame() {
  const q = gl.createQuery();
  gl.beginQuery(ext.TIME_ELAPSED_EXT, q);
  drawScene();
  gl.endQuery(ext.TIME_ELAPSED_EXT);
  queries.push({ q, frameId: frameCount++ });

  // resolve 매 several frame later (async)
  for (let i = queries.length - 1; i >= 0; i--) {
    const { q, frameId } = queries[i];
    const available = gl.getQueryParameter(q, gl.QUERY_RESULT_AVAILABLE);
    const disjoint = gl.getParameter(ext.GPU_DISJOINT_EXT);
    if (available && !disjoint) {
      const ns = gl.getQueryParameter(q, gl.QUERY_RESULT);
      console.log(`frame ${frameId} GPU: ${(ns / 1e6).toFixed(2)} ms`);
      gl.deleteQuery(q);
      queries.splice(i, 1);
    }
  }
  requestAnimationFrame(frame);
}

Anti — gl.finish() blocking

// X — pipeline stall, 매 production 에서 절대 사용 X
const t0 = performance.now();
drawScene();
gl.finish(); // CPU↔GPU sync barrier
const t1 = performance.now();
// t1-t0 는 측정 대상이 아닌 stall 의 cost

Input→render latency (RAIL model)

let inputTs = 0;
canvas.addEventListener('pointerdown', e => {
  inputTs = e.timeStamp; // 매 hardware timestamp
});

function frame(now) {
  if (inputTs > 0 && pendingInputProcessed) {
    const latency = now - inputTs;
    if (latency > 100) console.warn(`input→render: ${latency}ms`);
    inputTs = 0;
  }
  drawScene();
  requestAnimationFrame(frame);
}

Disjoint detection (thermal throttling)

function checkThermal() {
  const disjoint = gl.getParameter(ext.GPU_DISJOINT_EXT);
  if (disjoint) {
    // 매 GPU 가 context switch / power state 변경 — measurement invalid
    metricsBuffer.flush('thermal_event');
  }
}

WebGPU — native timestamp query

const querySet = device.createQuerySet({ type: 'timestamp', count: 2 });
const encoder = device.createCommandEncoder();
const pass = encoder.beginRenderPass({
  ...,
  timestampWrites: { querySet, beginningOfPassWriteIndex: 0, endOfPassWriteIndex: 1 }
});
pass.draw(...);
pass.end();
encoder.resolveQuerySet(querySet, 0, 2, resolveBuffer, 0);
// readback async

Frame pacing (requestVideoFrameCallback)

videoEl.requestVideoFrameCallback((now, meta) => {
  const presentLatency = meta.presentationTime - meta.captureTime;
  // 매 actual display 까지의 latency 측정
});

Sliding-window p99 측정

const samples = new Float32Array(120); // 2s @ 60Hz
let idx = 0;
function record(ms) {
  samples[idx++ % samples.length] = ms;
  if (idx % 60 === 0) {
    const sorted = [...samples].sort((a, b) => a - b);
    console.log('p50:', sorted[60], 'p99:', sorted[118]);
  }
}

매 결정 기준

상황	Approach
WebGL2 + 매 정밀 측정	EXT_disjoint_timer_query_webgl2
WebGPU 가능	timestampWrites
Quick debug	Spector.js capture
Production telemetry	sliding-window p99 + disjoint flag
Cross-browser fallback	CPU time only + acknowledged limitation

기본값: WebGL2 + timer query async readback, p99/disjoint 매 telemetry.

🔗 Graph

• 부모: WebGL · GPU
• 변형: WebGPU · Vulkan
• 응용: Web-Game-Performance · Three.js
• Adjacent: Frame-Pacing · Browser-Compositor

🤖 LLM 활용

언제: WebGL/WebGPU 앱 의 frame budget 초과 진단, 60→120Hz 마이그레이션, mobile thermal 분석. 언제 X: 일반 DOM perf (use Performance API), Canvas2D (no GPU query).

❌ 안티패턴

• gl.finish() in frame loop: pipeline stall, 매 측정값 자체 가 왜곡.
• performance.now() only: GPU async 의 무시 — frame 의 N+2 까지 결과 가 안 나올 수 있음.
• disjoint flag 무시: thermal/power state 변경 시 measurement 가 garbage 인데 그대로 report.
• single sample: GC, compositor jitter 의 무시 — 매 p99 측정 필수.

🧪 검증 / 중복

• Verified (Khronos EXT_disjoint_timer_query_webgl2 spec, Chrome DevTools docs).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — WebGL micro-latency 측정 패턴 정리