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Antigravity Agent
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---
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id: wiki-2026-0508-multi-threaded-architecture
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title: Multi-threaded Architecture
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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: [Multithreading, Concurrent Architecture, MT Architecture]
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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: [architecture, concurrency, threading, performance]
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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: cpp
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framework: stdthread-tbb-rayon
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---
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# Multi-threaded Architecture
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## 매 한 줄
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> **"매 work를 multiple threads에 분산하여 throughput · responsiveness를 동시에 확보."**. 1990s SMP era에서 출발하여 2026 현재 manycore (Apple M4 Max 16-core, AMD Threadripper 96-core), GPU offload, async/await coroutine model이 주류. Game engine · server · ML inference · browser engine 모두 multi-threaded 설계가 default.
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## 매 핵심
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### 매 thread model 종류
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- **OS thread (1:1)**: pthread, std::thread — kernel-scheduled, expensive context switch.
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- **Green thread / fiber**: Go goroutine, Java 21 virtual thread — userland scheduler, M:N mapping.
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- **Coroutine / async task**: C++20 coroutine, Rust async, Kotlin coroutine — stackless, await-resume.
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- **Task-based**: Intel TBB, .NET TPL, Apple GCD — work-stealing scheduler, no thread management.
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### 매 architectural pattern
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- **Producer-consumer**: bounded queue로 backpressure.
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- **Pipeline**: stage별 thread, ring buffer로 연결 (LMAX Disruptor).
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- **Fork-join**: divide & conquer, work-stealing.
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- **Actor**: 매 message passing (Akka, Erlang, Pony) — no shared state.
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- **Data parallelism**: SIMD + thread pool — Rayon `par_iter()`, OpenMP `#pragma omp parallel for`.
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### 매 응용
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1. Game engine — render thread + game thread + audio thread + IO thread.
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2. Browser — process-per-tab + GPU process + utility processes.
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3. Database — connection pool + worker threads + background flush.
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## 💻 패턴
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### Thread pool with work queue (C++20)
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```cpp
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#include <thread>
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#include <queue>
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#include <mutex>
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#include <condition_variable>
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#include <functional>
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class ThreadPool {
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std::vector<std::jthread> workers;
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std::queue<std::function<void()>> tasks;
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std::mutex mtx;
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std::condition_variable cv;
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bool stop = false;
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public:
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explicit ThreadPool(size_t n) {
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for (size_t i = 0; i < n; ++i)
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workers.emplace_back([this](std::stop_token st) {
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while (!st.stop_requested()) {
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std::function<void()> task;
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{
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std::unique_lock lk(mtx);
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cv.wait(lk, [&]{ return stop || !tasks.empty(); });
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if (stop && tasks.empty()) return;
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task = std::move(tasks.front()); tasks.pop();
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}
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task();
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}
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});
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}
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template<class F> void submit(F&& f) {
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{ std::lock_guard lk(mtx); tasks.emplace(std::forward<F>(f)); }
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cv.notify_one();
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}
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};
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```
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### Rust Rayon data parallelism
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```rust
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use rayon::prelude::*;
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fn process_batch(items: &[Item]) -> Vec<Result> {
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items.par_iter()
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.filter(|i| i.valid())
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.map(|i| expensive_compute(i))
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.collect()
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}
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// auto: work-stealing across all cores
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```
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### Go goroutine + channel (fan-out / fan-in)
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```go
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func pipeline(input <-chan Job) <-chan Result {
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out := make(chan Result, 100)
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var wg sync.WaitGroup
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for i := 0; i < runtime.NumCPU(); i++ {
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wg.Add(1)
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go func() {
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defer wg.Done()
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for job := range input {
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out <- process(job)
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}
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}()
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}
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go func() { wg.Wait(); close(out) }()
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return out
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}
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```
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### Lock-free SPSC ring buffer
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```cpp
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template<typename T, size_t N>
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class SPSCQueue {
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alignas(64) std::atomic<size_t> head{0};
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alignas(64) std::atomic<size_t> tail{0};
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T buffer[N];
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public:
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bool push(T v) {
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auto t = tail.load(std::memory_order_relaxed);
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auto next = (t + 1) % N;
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if (next == head.load(std::memory_order_acquire)) return false;
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buffer[t] = std::move(v);
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tail.store(next, std::memory_order_release);
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return true;
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}
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};
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```
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### Game engine 3-thread architecture
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```cpp
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// Main thread: input + game logic
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// Render thread: GPU command buffer
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// IO thread: asset streaming
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struct FrameSync {
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std::atomic<uint64_t> game_frame{0};
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std::atomic<uint64_t> render_frame{0};
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std::counting_semaphore<2> render_ready{0};
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};
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// double-buffer scene state to allow N+1 game tick parallel with N render
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```
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## 매 결정 기준
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| 상황 | Approach |
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| CPU-bound, divisible work | Rayon / OpenMP / TBB |
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| IO-heavy (10k+ connections) | async/await (Tokio, asyncio, Node) |
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| Real-time game loop | dedicated threads + lock-free queue |
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| Mixed workload | task-based (TBB, GCD) |
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| Simple parallel-for | thread pool + work queue |
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| Distributed across machines | actor (Akka) or message queue |
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**기본값**: task-based scheduler (TBB/Rayon/Tokio) — manual thread management 회피.
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## 🔗 Graph
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- 부모: [[Concurrent_Rendering]] · [[Distributed-Systems|Distributed_Computing]]
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- 변형: [[Fiber_Architecture]]
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- 응용: [[Game_Loop]] · [[V8 엔진 힙 아키텍처|V8 Heap Architecture]] · [[Browser]]
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- Adjacent: [[SharedArrayBuffer_보안_이슈와_Cross-Origin_Isolation]] · [[Memory_Leaks]]
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## 🤖 LLM 활용
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**언제**: throughput-critical workload, multi-core utilization, real-time game/server, ML inference batching.
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**언제 X**: simple sequential script, IO-light short-lived task, single-core embedded — 매 overhead 큼.
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## ❌ 안티패턴
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- **Shared mutable state without sync**: data race · UB.
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- **Coarse global lock**: 매 single-thread보다 느림 (lock contention).
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- **Thread per request (10k+)**: stack memory 폭발 — async 또는 thread pool 사용.
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- **busy-wait spin**: CPU 100% 소모 — condition variable / semaphore.
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- **False sharing**: 같은 cache line의 다른 atomic — alignas(64) cache padding.
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## 🧪 검증 / 중복
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- Verified (Herb Sutter "The Free Lunch Is Over" 2005, Intel TBB docs, Rust async book 2026).
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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 — full content (thread models, patterns, decision matrix) |
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