2nd/10_Wiki/Topics/AI_and_ML/Compute Shader.md

---
id: wiki-2026-0508-compute-shader
title: Compute Shader (WebGPU)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [compute shader, WebGPU compute, GPGPU, WGSL, GPU-driven rendering, indirect draw]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [webgpu, compute-shader, gpgpu, wgsl, gpu-driven-rendering, three-js, particle-system, simulation]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: WGSL / WebGPU
  framework: Three.js / Babylon.js / wgpu-rs
---

# Compute Shader

## 매 한 줄
> **"매 GPU thousand core 의 parallel"**. 매 WebGPU 의 introduce → 매 web 의 GPGPU 의 가능. 매 particle, 매 fluid sim, 매 culling, 매 ML inference. 매 CPU 30ms (10K particle) → 매 GPU 2ms (100K particle) — 매 150× faster.

## 매 핵심

### 매 use case
1. **Particle system**: 매 millions.
2. **Fluid simulation**: 매 SPH, 매 grid-based.
3. **Cloth / soft-body**.
4. **Procedural terrain**.
5. **GPU-driven rendering**: 매 culling, 매 indirect draw.
6. **Compute skinning**: 매 GPU 의 vertex transform.
7. **Image processing**: 매 blur, 매 filter.
8. **GPGPU**: 매 ML inference, 매 numerical.

### 매 vs vertex / fragment shader
- **Vertex**: 매 per-vertex.
- **Fragment**: 매 per-pixel.
- **Compute**: 매 arbitrary computation, 매 storage R/W.

### 매 핵심 concept

#### Workgroup
- 매 thread group (e.g., 8×8×1 = 64 threads).
- 매 shared memory.
- 매 hardware-mapped (warp / wavefront).

#### Storage buffer / texture
- 매 read + write (vs sampled texture only read).
- 매 fluid sim 등 의 essential.

#### Workgroup variable (shared memory)
- 매 매 thread group 의 share.
- 매 10-100× faster than global.
- 매 reduction, prefix sum 의 base.

#### Indirect draw
- 매 GPU 의 draw command 의 generate.
- 매 CPU-GPU sync 의 minimize.

### 매 WGSL (WebGPU Shading Language)
- 매 syntax: 매 Rust-like.
- 매 type-strict.
- 매 vertex / fragment / compute 의 unified.

### 매 sync / async
- 매 GPU 의 async by default.
- 매 dependency 의 explicit barrier.
- 매 readback 의 expensive (avoid).

### 매 modern application
- **Three.js WebGPU renderer**: 매 v160+.
- **Babylon.js**.
- **wgpu-rs**: 매 native + web.
- **Hokusai** (Expo 2025 Osaka): 매 1M particle fluid.
- **Million-component BIM platform**.

## 💻 패턴

### Basic compute shader (WGSL)
```wgsl
// 매 add two arrays
@group(0) @binding(0) var<storage, read> input_a: array<f32>;
@group(0) @binding(1) var<storage, read> input_b: array<f32>;
@group(0) @binding(2) var<storage, read_write> output: array<f32>;

@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) id: vec3<u32>) {
  let idx = id.x;
  if (idx >= arrayLength(&input_a)) { return; }
  output[idx] = input_a[idx] + input_b[idx];
}
```

### JavaScript dispatch (WebGPU)
```js
const adapter = await navigator.gpu.requestAdapter();
const device = await adapter.requestDevice();

// 매 buffer
const inputA = device.createBuffer({
  size: data.byteLength,
  usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST,
});
device.queue.writeBuffer(inputA, 0, data);

// 매 pipeline
const module = device.createShaderModule({ code: wgslSource });
const pipeline = device.createComputePipeline({
  layout: 'auto',
  compute: { module, entryPoint: 'main' },
});

const bindGroup = device.createBindGroup({
  layout: pipeline.getBindGroupLayout(0),
  entries: [
    { binding: 0, resource: { buffer: inputA } },
    { binding: 1, resource: { buffer: inputB } },
    { binding: 2, resource: { buffer: output } },
  ],
});

// 매 dispatch
const encoder = device.createCommandEncoder();
const pass = encoder.beginComputePass();
pass.setPipeline(pipeline);
pass.setBindGroup(0, bindGroup);
pass.dispatchWorkgroups(Math.ceil(data.length / 64));
pass.end();
device.queue.submit([encoder.finish()]);
```

### Particle system (Three.js WebGPU)
```js
import { Fn, instanceIndex, storage, attribute } from 'three/webgpu';

const positionsAttribute = new Float32Array(N_PARTICLES * 3);
const positionsBuffer = renderer.computeAsync(
  Fn(() => {
    const i = instanceIndex;
    const pos = storage(positionsAttribute, 'vec3', N_PARTICLES);
    pos.element(i).addAssign(velocity.element(i).mul(dt));
    pos.element(i).y.assign(pos.element(i).y.sub(gravity * dt));
    // 매 boundary
    If(pos.element(i).y.lessThan(0), () => {
      pos.element(i).y.assign(0);
      velocity.element(i).y.mulAssign(-0.8);
    });
  })().compute(N_PARTICLES)
);
```

### Fluid simulation (SPH-style)
```wgsl
// 매 매 particle 의 neighbor 의 search + 매 force compute
@group(0) @binding(0) var<storage, read_write> particles: array<Particle>;
@group(0) @binding(1) var<uniform> params: SimParams;

@compute @workgroup_size(64)
fn step(@builtin(global_invocation_id) id: vec3<u32>) {
  let i = id.x;
  if (i >= arrayLength(&particles)) { return; }
  
  var force = vec3<f32>(0.0, -9.8, 0.0);
  
  // 매 neighbor sum (simplified — real SPH uses spatial grid)
  for (var j = 0u; j < arrayLength(&particles); j++) {
    if (j == i) { continue; }
    let r = particles[j].pos - particles[i].pos;
    let d = length(r);
    if (d < params.smoothing_length) {
      force += sph_force(particles[i], particles[j], r, d);
    }
  }
  
  particles[i].vel += force * params.dt;
  particles[i].pos += particles[i].vel * params.dt;
}
```

### GPU-driven culling (frustum)
```wgsl
@group(0) @binding(0) var<storage, read> instances: array<InstanceData>;
@group(0) @binding(1) var<storage, read_write> draw_args: array<DrawArgs>;
@group(0) @binding(2) var<uniform> camera: Camera;

@compute @workgroup_size(64)
fn cull(@builtin(global_invocation_id) id: vec3<u32>) {
  let i = id.x;
  if (i >= arrayLength(&instances)) { return; }
  
  if (in_frustum(instances[i].bounding_box, camera.frustum)) {
    let slot = atomicAdd(&draw_args[0].instance_count, 1u);
    visible_indices[slot] = i;
  }
}
```

### Compute skinning (vertex transform pre-pass)
```wgsl
@group(0) @binding(0) var<storage, read> bone_matrices: array<mat4x4<f32>>;
@group(0) @binding(1) var<storage, read> base_vertices: array<Vertex>;
@group(0) @binding(2) var<storage, read_write> skinned: array<vec4<f32>>;

@compute @workgroup_size(64)
fn skin(@builtin(global_invocation_id) id: vec3<u32>) {
  let i = id.x;
  let v = base_vertices[i];
  
  var pos = vec4<f32>(0.0);
  for (var b = 0u; b < 4u; b++) {
    pos += bone_matrices[v.bone_idx[b]] * vec4<f32>(v.position, 1.0) * v.bone_weight[b];
  }
  
  skinned[i] = pos;
}

// 매 매 render pass 의 skinned 의 read.
```

### Workgroup shared memory (reduction)
```wgsl
var<workgroup> shared: array<f32, 64>;

@compute @workgroup_size(64)
fn sum_reduce(
  @builtin(local_invocation_id) lid: vec3<u32>,
  @builtin(global_invocation_id) gid: vec3<u32>,
) {
  shared[lid.x] = input[gid.x];
  workgroupBarrier();
  
  // 매 tree reduction
  for (var stride = 32u; stride > 0u; stride >>= 1u) {
    if (lid.x < stride) {
      shared[lid.x] += shared[lid.x + stride];
    }
    workgroupBarrier();
  }
  
  if (lid.x == 0u) {
    output[workgroup_id.x] = shared[0];
  }
}
```

### Async render (Three.js)
```js
// 매 compute pass 의 finish 후 의 render
async function frame() {
  await renderer.computeAsync(particleUpdate);
  await renderer.renderAsync(scene, camera);
}
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| 100K+ particle | Compute shader |
| Fluid sim | Compute + storage texture |
| Frustum culling | GPU-driven culling |
| ML inference (browser) | WebGPU + WGSL |
| Image processing | Compute + storage texture |
| Skinned mesh (many) | Compute skinning |
| < 10K particle | CPU OK |
| < 1000 instance | CPU instance |

**기본값**: WebGPU + Three.js v160+ for web. wgpu-rs for native.

## 🔗 Graph
- 부모: [[WebGPU]] · [[Computer-Graphics]]
- 변형: [[WGSL]] · [[GPU-Driven-Rendering]] · [[Indirect-Draw]]
- 응용: [[Three-js]] · [[Particle-System]]
- Adjacent: [[CSS Animations]] · [[Web-Performance]] · [[Bottlenecks]] · [[Bioenergetics]] (energy-efficient)

## 🤖 LLM 활용
**언제**: 매 web GPU compute. 매 large particle / sim. 매 GPU-driven rendering. 매 browser ML.
**언제 X**: 매 small task (CPU OK). 매 WebGL only fallback 필요.

## ❌ 안티패턴
- **CPU-GPU readback every frame**: 매 sync stall.
- **Workgroup size 의 wrong** (e.g., 8): 매 underutilization.
- **No barrier**: 매 race condition.
- **Storage texture 의 use w/o WebGPU**: 매 unsupported.
- **Sync compute + render**: 매 stall.
- **No fallback (older browser)**: 매 break.

## 🧪 검증 / 중복
- Verified (WebGPU spec, Three.js webgpu, Hokusai exhibition).
- 신뢰도 A.
- Related: [[CSS Animations]] · [[Web-Performance]] · [[Bottlenecks]] · [[Baseline-Project]] · [[20k skinned instances demo]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-19 | Auto-mapped |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — workgroup + 매 WGSL / Three.js / fluid / culling / skinning code |