refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-compute-shader
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title: Compute Shader (WebGPU)
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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: [compute shader, WebGPU compute, GPGPU, WGSL, GPU-driven rendering, indirect draw]
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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: [webgpu, compute-shader, gpgpu, wgsl, gpu-driven-rendering, three-js, particle-system, simulation]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: WGSL / WebGPU
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framework: Three.js / Babylon.js / wgpu-rs
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---
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# Compute Shader
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 use case
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1. **Particle system**: 매 millions.
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2. **Fluid simulation**: 매 SPH, 매 grid-based.
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3. **Cloth / soft-body**.
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4. **Procedural terrain**.
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5. **GPU-driven rendering**: 매 culling, 매 indirect draw.
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6. **Compute skinning**: 매 GPU 의 vertex transform.
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7. **Image processing**: 매 blur, 매 filter.
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8. **GPGPU**: 매 ML inference, 매 numerical.
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### 매 vs vertex / fragment shader
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- **Vertex**: 매 per-vertex.
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- **Fragment**: 매 per-pixel.
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- **Compute**: 매 arbitrary computation, 매 storage R/W.
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### 매 핵심 concept
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#### Workgroup
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- 매 thread group (e.g., 8×8×1 = 64 threads).
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- 매 shared memory.
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- 매 hardware-mapped (warp / wavefront).
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#### Storage buffer / texture
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- 매 read + write (vs sampled texture only read).
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- 매 fluid sim 등 의 essential.
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#### Workgroup variable (shared memory)
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- 매 매 thread group 의 share.
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- 매 10-100× faster than global.
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- 매 reduction, prefix sum 의 base.
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#### Indirect draw
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- 매 GPU 의 draw command 의 generate.
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- 매 CPU-GPU sync 의 minimize.
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### 매 WGSL (WebGPU Shading Language)
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- 매 syntax: 매 Rust-like.
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- 매 type-strict.
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- 매 vertex / fragment / compute 의 unified.
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### 매 sync / async
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- 매 GPU 의 async by default.
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- 매 dependency 의 explicit barrier.
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- 매 readback 의 expensive (avoid).
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### 매 modern application
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- **Three.js WebGPU renderer**: 매 v160+.
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- **Babylon.js**.
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- **wgpu-rs**: 매 native + web.
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- **Hokusai** (Expo 2025 Osaka): 매 1M particle fluid.
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- **Million-component BIM platform**.
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## 💻 패턴
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### Basic compute shader (WGSL)
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```wgsl
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// 매 add two arrays
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@group(0) @binding(0) var<storage, read> input_a: array<f32>;
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@group(0) @binding(1) var<storage, read> input_b: array<f32>;
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@group(0) @binding(2) var<storage, read_write> output: array<f32>;
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@compute @workgroup_size(64)
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fn main(@builtin(global_invocation_id) id: vec3<u32>) {
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let idx = id.x;
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if (idx >= arrayLength(&input_a)) { return; }
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output[idx] = input_a[idx] + input_b[idx];
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}
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```
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### JavaScript dispatch (WebGPU)
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```js
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const adapter = await navigator.gpu.requestAdapter();
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const device = await adapter.requestDevice();
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// 매 buffer
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const inputA = device.createBuffer({
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size: data.byteLength,
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usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST,
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});
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device.queue.writeBuffer(inputA, 0, data);
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// 매 pipeline
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const module = device.createShaderModule({ code: wgslSource });
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const pipeline = device.createComputePipeline({
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layout: 'auto',
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compute: { module, entryPoint: 'main' },
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});
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const bindGroup = device.createBindGroup({
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layout: pipeline.getBindGroupLayout(0),
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entries: [
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{ binding: 0, resource: { buffer: inputA } },
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{ binding: 1, resource: { buffer: inputB } },
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{ binding: 2, resource: { buffer: output } },
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],
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});
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// 매 dispatch
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const encoder = device.createCommandEncoder();
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const pass = encoder.beginComputePass();
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pass.setPipeline(pipeline);
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pass.setBindGroup(0, bindGroup);
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pass.dispatchWorkgroups(Math.ceil(data.length / 64));
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pass.end();
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device.queue.submit([encoder.finish()]);
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```
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### Particle system (Three.js WebGPU)
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```js
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import { Fn, instanceIndex, storage, attribute } from 'three/webgpu';
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const positionsAttribute = new Float32Array(N_PARTICLES * 3);
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const positionsBuffer = renderer.computeAsync(
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Fn(() => {
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const i = instanceIndex;
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const pos = storage(positionsAttribute, 'vec3', N_PARTICLES);
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pos.element(i).addAssign(velocity.element(i).mul(dt));
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pos.element(i).y.assign(pos.element(i).y.sub(gravity * dt));
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// 매 boundary
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If(pos.element(i).y.lessThan(0), () => {
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pos.element(i).y.assign(0);
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velocity.element(i).y.mulAssign(-0.8);
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});
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})().compute(N_PARTICLES)
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);
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```
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### Fluid simulation (SPH-style)
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```wgsl
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// 매 매 particle 의 neighbor 의 search + 매 force compute
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@group(0) @binding(0) var<storage, read_write> particles: array<Particle>;
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@group(0) @binding(1) var<uniform> params: SimParams;
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@compute @workgroup_size(64)
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fn step(@builtin(global_invocation_id) id: vec3<u32>) {
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let i = id.x;
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if (i >= arrayLength(&particles)) { return; }
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var force = vec3<f32>(0.0, -9.8, 0.0);
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// 매 neighbor sum (simplified — real SPH uses spatial grid)
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for (var j = 0u; j < arrayLength(&particles); j++) {
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if (j == i) { continue; }
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let r = particles[j].pos - particles[i].pos;
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let d = length(r);
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if (d < params.smoothing_length) {
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force += sph_force(particles[i], particles[j], r, d);
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}
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}
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particles[i].vel += force * params.dt;
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particles[i].pos += particles[i].vel * params.dt;
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}
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```
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### GPU-driven culling (frustum)
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```wgsl
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@group(0) @binding(0) var<storage, read> instances: array<InstanceData>;
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@group(0) @binding(1) var<storage, read_write> draw_args: array<DrawArgs>;
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@group(0) @binding(2) var<uniform> camera: Camera;
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@compute @workgroup_size(64)
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fn cull(@builtin(global_invocation_id) id: vec3<u32>) {
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let i = id.x;
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if (i >= arrayLength(&instances)) { return; }
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if (in_frustum(instances[i].bounding_box, camera.frustum)) {
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let slot = atomicAdd(&draw_args[0].instance_count, 1u);
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visible_indices[slot] = i;
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}
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}
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```
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### Compute skinning (vertex transform pre-pass)
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```wgsl
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@group(0) @binding(0) var<storage, read> bone_matrices: array<mat4x4<f32>>;
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@group(0) @binding(1) var<storage, read> base_vertices: array<Vertex>;
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@group(0) @binding(2) var<storage, read_write> skinned: array<vec4<f32>>;
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@compute @workgroup_size(64)
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fn skin(@builtin(global_invocation_id) id: vec3<u32>) {
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let i = id.x;
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let v = base_vertices[i];
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var pos = vec4<f32>(0.0);
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for (var b = 0u; b < 4u; b++) {
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pos += bone_matrices[v.bone_idx[b]] * vec4<f32>(v.position, 1.0) * v.bone_weight[b];
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}
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skinned[i] = pos;
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}
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// 매 매 render pass 의 skinned 의 read.
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```
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### Workgroup shared memory (reduction)
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```wgsl
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var<workgroup> shared: array<f32, 64>;
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@compute @workgroup_size(64)
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fn sum_reduce(
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@builtin(local_invocation_id) lid: vec3<u32>,
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@builtin(global_invocation_id) gid: vec3<u32>,
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) {
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shared[lid.x] = input[gid.x];
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workgroupBarrier();
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// 매 tree reduction
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for (var stride = 32u; stride > 0u; stride >>= 1u) {
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if (lid.x < stride) {
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shared[lid.x] += shared[lid.x + stride];
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}
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workgroupBarrier();
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}
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if (lid.x == 0u) {
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output[workgroup_id.x] = shared[0];
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}
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}
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```
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### Async render (Three.js)
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```js
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// 매 compute pass 의 finish 후 의 render
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async function frame() {
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await renderer.computeAsync(particleUpdate);
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await renderer.renderAsync(scene, camera);
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}
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```
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## 🤔 결정 기준
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| 상황 | Approach |
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|---|---|
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| 100K+ particle | Compute shader |
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| Fluid sim | Compute + storage texture |
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| Frustum culling | GPU-driven culling |
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| ML inference (browser) | WebGPU + WGSL |
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| Image processing | Compute + storage texture |
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| Skinned mesh (many) | Compute skinning |
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| < 10K particle | CPU OK |
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| < 1000 instance | CPU instance |
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**기본값**: WebGPU + Three.js v160+ for web. wgpu-rs for native.
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## 🔗 Graph
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- 부모: [[WebGPU]] · [[Computer-Graphics]]
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- 변형: [[WGSL]] · [[GPU-driven Rendering]] · [[Indirect Draw]]
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- 응용: [[Three.js]] · [[Particle-System]]
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- Adjacent: [[CSS Animations]] · [[Web-Performance]] · [[Bottlenecks]] · [[Bioenergetics]] (energy-efficient)
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## 🤖 LLM 활용
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**언제**: 매 web GPU compute. 매 large particle / sim. 매 GPU-driven rendering. 매 browser ML.
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**언제 X**: 매 small task (CPU OK). 매 WebGL only fallback 필요.
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## ❌ 안티패턴
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- **CPU-GPU readback every frame**: 매 sync stall.
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- **Workgroup size 의 wrong** (e.g., 8): 매 underutilization.
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- **No barrier**: 매 race condition.
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- **Storage texture 의 use w/o WebGPU**: 매 unsupported.
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- **Sync compute + render**: 매 stall.
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- **No fallback (older browser)**: 매 break.
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## 🧪 검증 / 중복
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- Verified (WebGPU spec, Three.js webgpu, Hokusai exhibition).
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- 신뢰도 A.
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- Related: [[CSS Animations]] · [[Web-Performance]] · [[Bottlenecks]] · [[Baseline (Web Platform Features)]] · [[20k skinned instances demo]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-19 | Auto-mapped |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — workgroup + 매 WGSL / Three.js / fluid / culling / skinning code |
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