9148c358d0
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
5.4 KiB
5.4 KiB
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-for-the-web-community-group | GPU for the Web Community Group | 10_Wiki/Topics | verified | self |
|
none | A | 0.9 | applied |
|
2026-05-10 | pending |
|
GPU for the Web Community Group
매 한 줄
"매 W3C 의 WebGPU + WGSL 표준 의 incubator". 2017 부터 매 Apple/Google/Mozilla/Microsoft 의 collaboration 으로 매 modern GPU API (Metal/Vulkan/D3D12) 를 web 에 expose. 2023 Chrome 113 ship, 2024 Safari 18 / Firefox 141 follow — 2026 현재 매 cross-browser baseline 의 confirmed.
매 핵심
매 group mission
- API design: 매 WebGL 의 successor — 매 explicit, low-overhead, compute-capable.
- WGSL (WebGPU Shading Language): 매 SPIR-V/HLSL/MSL 의 portable layer.
- Security: 매 sandbox 안의 GPU access — robust buffer access, timeline fence.
- Specification: 매 W3C Recommendation track — 2026 매 CR 단계.
매 architecture pillars
- Adapter → Device → Queue: 매 explicit lifecycle.
- Bind group: 매 Vulkan descriptor set 의 web flavor.
- Render pipeline / Compute pipeline: 매 separate, immutable.
- Command encoder: 매 deferred recording, queue submission.
매 응용
- ML inference in browser (transformers.js + WebGPU).
- 3D scene rendering (Three.js WebGPURenderer, Babylon.js).
- Real-time video filter (compute shader).
- Scientific viz (volume rendering).
💻 패턴
Init device + adapter
const adapter = await navigator.gpu?.requestAdapter({
powerPreference: 'high-performance',
});
if (!adapter) throw new Error('WebGPU unavailable');
const device = await adapter.requestDevice({
requiredFeatures: ['shader-f16'],
requiredLimits: { maxBufferSize: 1 << 30 },
});
device.lost.then(info => console.error('Device lost:', info));
Compute shader (WGSL) — vector add
@group(0) @binding(0) var<storage, read> a : array<f32>;
@group(0) @binding(1) var<storage, read> b : array<f32>;
@group(0) @binding(2) var<storage, read_write> out : array<f32>;
@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) gid : vec3<u32>) {
let i = gid.x;
if (i >= arrayLength(&out)) { return; }
out[i] = a[i] + b[i];
}
Dispatch compute pass
const module = device.createShaderModule({ code: WGSL_SOURCE });
const pipeline = device.createComputePipeline({
layout: 'auto',
compute: { module, entryPoint: 'main' },
});
const bindGroup = device.createBindGroup({
layout: pipeline.getBindGroupLayout(0),
entries: [
{ binding: 0, resource: { buffer: bufA } },
{ binding: 1, resource: { buffer: bufB } },
{ binding: 2, resource: { buffer: bufOut } },
],
});
const encoder = device.createCommandEncoder();
const pass = encoder.beginComputePass();
pass.setPipeline(pipeline);
pass.setBindGroup(0, bindGroup);
pass.dispatchWorkgroups(Math.ceil(N / 64));
pass.end();
device.queue.submit([encoder.finish()]);
Render pipeline (triangle)
const pipeline = device.createRenderPipeline({
layout: 'auto',
vertex: { module, entryPoint: 'vs_main', buffers: [vbLayout] },
fragment: { module, entryPoint: 'fs_main', targets: [{ format }] },
primitive: { topology: 'triangle-list' },
});
Buffer mapping (CPU → GPU)
const buf = device.createBuffer({
size: data.byteLength,
usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST,
mappedAtCreation: true,
});
new Float32Array(buf.getMappedRange()).set(data);
buf.unmap();
Async readback
const stagingBuf = device.createBuffer({
size, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});
encoder.copyBufferToBuffer(gpuBuf, 0, stagingBuf, 0, size);
device.queue.submit([encoder.finish()]);
await stagingBuf.mapAsync(GPUMapMode.READ);
const out = new Float32Array(stagingBuf.getMappedRange().slice(0));
stagingBuf.unmap();
매 결정 기준
| 상황 | Approach |
|---|---|
| In-browser ML inference | WebGPU + WGSL compute (transformers.js) |
| 3D scene | Three.js WebGPURenderer (auto fallback) |
| Legacy device | WebGL2 fallback 의 detect |
| Native parity needed | wgpu (Rust) — 매 same WGSL |
| Mobile (iOS Safari) | feature-detect + lower limits |
기본값: WebGPU first, WebGL2 fallback, navigator.gpu feature-detect.
🔗 Graph
🤖 LLM 활용
언제: API surface lookup, WGSL syntax, pipeline boilerplate, fallback strategy. 언제 X: 매 cutting-edge proposal — spec churn 매 fast, source 의 cross-check.
❌ 안티패턴
- WebGPU 의 assume present: 매 feature-detect 필수 — 2026 mobile 매 still partial.
- Sync mapping on render thread: 매 jank —
mapAsync의 use. - One bind group per draw: 매 overhead — group by frequency-of-change.
- WebGL idiom: GL state machine 의 mental model 의 X — WebGPU 매 explicit, immutable.
🧪 검증 / 중복
- Verified (W3C GPU for the Web CG charter; WebGPU CR 2024-12; gpuweb/gpuweb GitHub).
- 신뢰도 A.
🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — WebGPU/WGSL pipeline + dispatch 정리 |