2nd/10_Wiki/Topic_Programming/Frontend/Timestamp Queries.md

---
id: wiki-2026-0508-timestamp-queries
title: Timestamp Queries
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [GPU Timestamp Queries, WebGPU Timestamp]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [webgpu, profiling, gpu, performance]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: javascript
  framework: webgpu
---

# Timestamp Queries

## 매 한 줄
> **"매 nanosecond-precision GPU timing"**. WebGPU `timestamp-query` feature 의 GPU command buffer 안 의 timestamp 의 record 의 pass-level / draw-level latency 의 measure. 매 CPU `performance.now()` 의 GPU work 의 invisible 의 problem 의 solve, 매 2026 production profiling 의 standard.

## 매 핵심

### 매 동작 원리
- `timestamp` query type 의 query set 의 allocate.
- `pass.writeTimestamp(querySet, index)` 의 command buffer 의 inject.
- `commandEncoder.resolveQuerySet()` 의 GPU buffer 의 result 의 write.
- `mapAsync(GPUMapMode.READ)` 의 CPU 의 read — 매 BigInt64Array 의 nanosecond.

### 매 caveat
- 매 feature flag 의 `requiredFeatures: ['timestamp-query']` 의 device 의 request 의 require.
- 매 timestamp resolution 의 driver-dependent — 매 Chrome 의 100ns granularity 의 quantize (privacy).
- 매 GPU clock 의 CPU clock 의 unsynced — 매 absolute time 의 X.
- 매 query result 의 readback 의 1+ frame 의 lag — 매 real-time 의 X 의 best-effort.

### 매 응용
1. Render pass / compute pass 의 cost breakdown.
2. Shader optimization 의 before/after 의 measure.
3. Adaptive quality (timestamp 의 budget exceed 의 case 의 LOD drop).
4. Production telemetry 의 GPU bottleneck 의 detect.

## 💻 패턴

### Request feature
```javascript
const adapter = await navigator.gpu.requestAdapter();
if (!adapter.features.has('timestamp-query')) {
  throw new Error('timestamp-query unsupported');
}
const device = await adapter.requestDevice({
  requiredFeatures: ['timestamp-query'],
});
```

### Create query set + buffer
```javascript
const querySet = device.createQuerySet({ type: 'timestamp', count: 2 });
const queryBuffer = device.createBuffer({
  size: 16, // 2 × u64
  usage: GPUBufferUsage.QUERY_RESOLVE | GPUBufferUsage.COPY_SRC,
});
const readBuffer = device.createBuffer({
  size: 16,
  usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});
```

### Inject into render pass
```javascript
const pass = encoder.beginRenderPass({
  colorAttachments: [...],
  timestampWrites: {
    querySet,
    beginningOfPassWriteIndex: 0,
    endOfPassWriteIndex: 1,
  },
});
pass.setPipeline(...);
pass.draw(...);
pass.end();
```

### Resolve + readback
```javascript
encoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);
encoder.copyBufferToBuffer(queryBuffer, 0, readBuffer, 0, 16);
device.queue.submit([encoder.finish()]);

await readBuffer.mapAsync(GPUMapMode.READ);
const times = new BigInt64Array(readBuffer.getMappedRange());
const deltaNs = Number(times[1] - times[0]);
console.log(`Pass: ${(deltaNs / 1e6).toFixed(2)} ms`);
readBuffer.unmap();
```

### Three.js helper (r170+)
```javascript
const renderer = new THREE.WebGPURenderer({ trackTimestamp: true });
await renderer.init();

await renderer.renderAsync(scene, camera);
const renderTime = await renderer.resolveTimestampsAsync(THREE.TimestampQuery.RENDER);
console.log(`Render GPU: ${renderTime} ms`);
```

### Multiple pass labels
```javascript
const querySet = device.createQuerySet({ type: 'timestamp', count: 6 });
// pass A: index 0, 1
// pass B: index 2, 3
// pass C: index 4, 5
// 매 single resolve 의 batch
```

### Ring buffer 의 frame-over-frame
```javascript
class GpuProfiler {
  constructor(device, frames = 4) {
    this.queries = Array.from({ length: frames }, () => ({
      set: device.createQuerySet({ type: 'timestamp', count: 2 }),
      buf: device.createBuffer({ size: 16, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }),
      pending: false,
    }));
    this.frame = 0;
  }
  acquire() {
    const slot = this.queries[this.frame % this.queries.length];
    this.frame++;
    return slot;
  }
}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Coarse CPU+GPU 의 frame time | `performance.now()` + `requestAnimationFrame` |
| Per-pass GPU breakdown | timestamp-query |
| Shader instruction-level | Chrome WebGPU Inspector / RenderDoc |
| Production telemetry | timestamp-query + sampling (1% session) |
| WebGL fallback | EXT_disjoint_timer_query (degraded, deprecated) |

**기본값**: 매 WebGPU app 의 production 의 ring-buffered timestamp-query — 매 1-frame lag 의 accept.

## 🔗 Graph
- 부모: [[WebGPU]]
- Adjacent: [[performance.now]]

## 🤖 LLM 활용
**언제**: 매 GPU bottleneck 의 suspect, 매 pass-level cost 의 attribute 의 want.
**언제 X**: 매 CPU-bound problem (e.g. JS hot loop) — 매 DevTools profiler 의 use.

## ❌ 안티패턴
- **Sync `mapAsync` await 의 매 frame**: 매 pipeline stall — 매 ring buffer 의 use.
- **Single timestamp 의 absolute time 의 interpret**: 매 GPU clock 의 CPU 의 unsync.
- **100ns 의 below 의 reliance**: 매 browser 의 quantize — 매 noise.
- **Feature 의 unrequest 의 use**: 매 `device.createQuerySet` 의 throw.
- **Profiler 의 production 의 always-on**: 매 overhead minor 의 X 의 budget — 매 sample.

## 🧪 검증 / 중복
- Verified (W3C WebGPU spec §22 Queries, Chrome WebGPU release notes).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — query set, ring buffer, Three.js integration |