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-shaders
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title: Compute Shaders
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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: [GPU Compute, GPGPU Shaders, WebGPU Compute]
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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: [gpu, shaders, webgpu, parallel, wgsl, cuda]
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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-glsl-cuda
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framework: webgpu-vulkan
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---
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# Compute Shaders
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## 매 한 줄
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> **"매 GPU program 매 graphics pipeline 의 X — 매 arbitrary parallel computation."**. Compute shader는 vertex/fragment shader 와 다르게 rendering 의 X — 매 raw SIMT 의 power. 2026년 WebGPU (browser GPU compute), CUDA, Vulkan compute, Metal compute, ML inference, particle sims, image processing 의 dominant. ML 의 attention/matmul 도 compute shader 본질.
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## 매 핵심
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### 매 model
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- **Workgroup**: 매 group of threads 가 same shader 실행 (e.g. 64 or 256 threads).
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- **Invocation**: single thread.
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- **Shared memory** (workgroup): fast, intra-group.
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- **Storage buffer**: GPU global memory (read/write).
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- **Uniform buffer**: small, read-only constants.
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- **Dispatch**: CPU 가 launches N workgroups.
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### 매 hardware mapping
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- NVIDIA: warp (32 threads), SM (streaming multiprocessor).
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- AMD: wave (64 threads, RDNA: 32), CU.
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- Apple: simdgroup (32), GPU core.
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- Intel: subgroup, EU.
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### 매 languages 2026
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- **WGSL** (WebGPU): cross-platform, modern.
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- **HLSL** (DirectX, Vulkan via DXC).
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- **GLSL** (OpenGL, Vulkan).
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- **MSL** (Metal Shading Language).
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- **CUDA C++**: NVIDIA only, but mature.
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- **Triton** (OpenAI): Python-like ML kernel DSL.
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### 매 응용
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1. ML inference (matmul, attention, conv).
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2. Image filters (blur, edge, color grading).
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3. Particle systems / fluid sim.
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4. Physics (cloth, soft body, mass-spring).
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5. Cryptography (proof-of-work, hash collisions).
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6. Video encode/decode prep.
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## 💻 패턴
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### WGSL compute shader — vector add
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```wgsl
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@group(0) @binding(0) var<storage, read> a : array<f32>;
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@group(0) @binding(1) var<storage, read> b : array<f32>;
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@group(0) @binding(2) var<storage, read_write> c : array<f32>;
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@compute @workgroup_size(64)
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fn main(@builtin(global_invocation_id) gid : vec3u) {
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let i = gid.x;
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if (i >= arrayLength(&a)) { return; }
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c[i] = a[i] + b[i];
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}
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```
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### WebGPU dispatch (TypeScript)
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```typescript
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const adapter = await navigator.gpu.requestAdapter();
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const device = await adapter!.requestDevice();
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const module = device.createShaderModule({ code: WGSL_SOURCE });
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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 N = 1_000_000;
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const buf = (data: Float32Array) => {
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const b = device.createBuffer({
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size: data.byteLength,
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usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST | GPUBufferUsage.COPY_SRC,
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});
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device.queue.writeBuffer(b, 0, data);
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return b;
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};
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const a = buf(new Float32Array(N).fill(1));
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const b = buf(new Float32Array(N).fill(2));
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const c = device.createBuffer({
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size: N * 4,
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usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC,
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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: a } },
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{ binding: 1, resource: { buffer: b } },
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{ binding: 2, resource: { buffer: c } },
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],
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});
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const enc = device.createCommandEncoder();
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const pass = enc.beginComputePass();
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pass.setPipeline(pipeline);
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pass.setBindGroup(0, bindGroup);
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pass.dispatchWorkgroups(Math.ceil(N / 64));
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pass.end();
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device.queue.submit([enc.finish()]);
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```
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### Shared memory reduction (workgroup-local)
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```wgsl
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var<workgroup> shared : array<f32, 256>;
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@compute @workgroup_size(256)
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fn reduce(
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@builtin(local_invocation_id) lid : vec3u,
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@builtin(global_invocation_id) gid : vec3u,
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) {
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shared[lid.x] = input[gid.x];
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workgroupBarrier();
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var stride : u32 = 128u;
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loop {
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if (stride == 0u) { break; }
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if (lid.x < stride) {
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shared[lid.x] = shared[lid.x] + shared[lid.x + stride];
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}
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workgroupBarrier();
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stride = stride / 2u;
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}
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if (lid.x == 0u) { output[gid.x / 256u] = shared[0]; }
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}
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```
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### CUDA matmul kernel
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```cuda
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__global__ void matmul(const float* A, const float* B, float* C, int N) {
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int row = blockIdx.y * blockDim.y + threadIdx.y;
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int col = blockIdx.x * blockDim.x + threadIdx.x;
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if (row >= N || col >= N) return;
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float sum = 0.0f;
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for (int k = 0; k < N; ++k) {
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sum += A[row * N + k] * B[k * N + col];
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}
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C[row * N + col] = sum;
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}
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// Launch: matmul<<<dim3((N+15)/16, (N+15)/16), dim3(16,16)>>>(A, B, C, N);
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```
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### Triton kernel (Python ML)
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```python
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import triton
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import triton.language as tl
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@triton.jit
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def add_kernel(x_ptr, y_ptr, out_ptr, n, BLOCK: tl.constexpr):
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pid = tl.program_id(0)
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offsets = pid * BLOCK + tl.arange(0, BLOCK)
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mask = offsets < n
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x = tl.load(x_ptr + offsets, mask=mask)
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y = tl.load(y_ptr + offsets, mask=mask)
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tl.store(out_ptr + offsets, x + y, mask=mask)
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# Launch
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add_kernel[(triton.cdiv(N, 1024),)](x, y, out, N, BLOCK=1024)
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```
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### Image blur compute (storage texture)
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```wgsl
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@group(0) @binding(0) var src : texture_2d<f32>;
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@group(0) @binding(1) var dst : texture_storage_2d<rgba8unorm, write>;
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@compute @workgroup_size(8, 8)
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fn blur(@builtin(global_invocation_id) gid : vec3u) {
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var sum = vec4f(0);
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for (var dy = -1; dy <= 1; dy++) {
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for (var dx = -1; dx <= 1; dx++) {
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let p = vec2i(gid.xy) + vec2i(dx, dy);
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sum = sum + textureLoad(src, p, 0);
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}
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}
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textureStore(dst, vec2i(gid.xy), sum / 9.0);
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}
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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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| Browser, cross-platform | WebGPU + WGSL |
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| Native cross-platform | Vulkan compute + GLSL/HLSL |
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| NVIDIA-only, max perf | CUDA |
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| Apple ecosystem | Metal compute (MSL) |
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| ML kernel research | Triton (Python) |
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| Production ML inference | Pre-built (cuDNN, MLX, vLLM kernels) |
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**기본값**: 매 web/cross-platform → WebGPU + WGSL. 매 ML research → Triton. 매 production NVIDIA ML → CUDA + cuDNN/cuBLAS.
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## 🔗 Graph
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- 부모: [[GPU Programming]] · [[Parallel Computing]]
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- 변형: [[Vertex Shader]] · [[Fragment Shader]] · [[CUDA]]
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- 응용: [[WebGPU]]
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- Adjacent: [[Triton]] · [[Vulkan]]
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## 🤖 LLM 활용
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**언제**: heavy parallel data (image, ML, sim), browser GPU compute, custom ML kernels.
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**언제 X**: small data (<10k items, CPU faster after transfer cost), branch-heavy serial logic, very small kernels (launch overhead).
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## ❌ 안티패턴
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- **Divergent branching in warp**: 매 thread 가 different path → serialization → 매 SIMT 의 X.
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- **Uncoalesced memory access**: random pattern → bandwidth waste — adjacent threads should read adjacent memory.
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- **Tiny dispatch**: 100 threads → launch overhead > work — batch.
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- **Forgetting workgroupBarrier**: race condition on shared memory.
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- **CPU↔GPU ping-pong**: every step copies back — keep data on GPU.
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## 🧪 검증 / 중복
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- Verified (WebGPU spec 2026 W3C / CUDA Programming Guide 12.x / Triton docs / Apple MSL).
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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 — WGSL/CUDA/Triton + workgroup model |
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