WebAssembly 如何实现多线程和并发编程？
WebAssembly 的多线程和并发编程通过 Web Workers 和共享内存实现：1. Web Workers 基础WebAssembly 本身是单线程的使用 Web Workers 实现多线程每个 Worker 有独立的 WebAssembly 实例Workers 之间通过消息传递通信2. 共享内存// 创建共享内存const sharedMemory = new WebAssembly.Memory({ initial: 10, maximum: 100, shared: true });// 主线程const mainBuffer = new Int32Array(sharedMemory.buffer);// Worker 线程const worker = new Worker('worker.js');worker.postMessage({ memory: sharedMemory }, [sharedMemory.buffer]);3. Worker 代码示例// worker.jsself.onmessage = function(e) { const { memory, wasmModule } = e.data; // 在 Worker 中加载 WebAssembly WebAssembly.instantiate(wasmModule, { env: { memory } }) .then(results => { const { process } = results.instance.exports; // 处理数据 const buffer = new Int32Array(memory.buffer); const result = process(0, buffer.length); // 发送结果回主线程 self.postMessage({ result }); });};4. 主线程代码// main.jsconst sharedMemory = new WebAssembly.Memory({ initial: 10, maximum: 100, shared: true });// 创建多个 Workersconst workers = [];for (let i = 0; i < 4; i++) { const worker = new Worker('worker.js'); worker.postMessage({ memory: sharedMemory, wasmModule: wasmBinary }, [sharedMemory.buffer]); workers.push(worker);}// 接收 Worker 结果workers.forEach(worker => { worker.onmessage = function(e) { console.log('Worker result:', e.data.result); };});5. 原子操作// 使用 Atomics 进行同步const buffer = new Int32Array(sharedMemory.buffer);// 原子加Atomics.add(buffer, 0, 1);// 原子比较并交换const expected = 0;const newValue = 1;const success = Atomics.compareExchange(buffer, 0, expected, newValue);// 原子等待Atomics.wait(buffer, 0, 0);// 原子通知Atomics.notify(buffer, 0, 1);6. 生产者-消费者模式// 生产者 Workerfunction producerWorker() { const buffer = new Int32Array(sharedMemory.buffer); const head = 0; const tail = 4; while (true) { // 等待空间 while (buffer[head] === buffer[tail]) { Atomics.wait(buffer, head, buffer[head]); } // 生产数据 buffer[buffer[head] % 10 + 8] = Math.random(); buffer[head] = buffer[head] + 1; // 通知消费者 Atomics.notify(buffer, head, 1); }}// 消费者 Workerfunction consumerWorker() { const buffer = new Int32Array(sharedMemory.buffer); const head = 0; const tail = 4; while (true) { // 等待数据 while (buffer[head] === buffer[tail]) { Atomics.wait(buffer, tail, buffer[tail]); } // 消费数据 const data = buffer[buffer[tail] % 10 + 8]; console.log('Consumed:', data); buffer[tail] = buffer[tail] + 1; // 通知生产者 Atomics.notify(buffer, tail, 1); }}7. 并行计算// 并行矩阵乘法async function parallelMatrixMultiply(A, B, numWorkers = 4) { const sharedMemory = new WebAssembly.Memory({ initial: 20, maximum: 100, shared: true }); const workers = []; const rowsPerWorker = Math.ceil(A.length / numWorkers); // 创建 Workers for (let i = 0; i < numWorkers; i++) { const worker = new Worker('matrix-worker.js'); const startRow = i * rowsPerWorker; const endRow = Math.min(startRow + rowsPerWorker, A.length); worker.postMessage({ memory: sharedMemory, startRow, endRow, A, B }, [sharedMemory.buffer]); workers.push(worker); } // 等待所有 Workers 完成 const results = await Promise.all( workers.map(worker => new Promise(resolve => { worker.onmessage = (e) => resolve(e.data.result); }) ) ); return results.flat();}8. 任务队列// 任务队列实现class TaskQueue { constructor(numWorkers = 4) { this.workers = []; this.taskQueue = []; this.activeWorkers = 0; for (let i = 0; i < numWorkers; i++) { const worker = new Worker('task-worker.js'); worker.onmessage = (e) => this.handleWorkerMessage(worker, e); this.workers.push(worker); } } addTask(task) { return new Promise((resolve, reject) => { this.taskQueue.push({ task, resolve, reject }); this.processQueue(); }); } processQueue() { while (this.taskQueue.length > 0 && this.activeWorkers < this.workers.length) { const { task, resolve, reject } = this.taskQueue.shift(); const worker = this.workers[this.activeWorkers]; this.activeWorkers++; worker.postMessage({ task, id: Date.now() }); worker.currentResolve = resolve; worker.currentReject = reject; } } handleWorkerMessage(worker, e) { const { result, error } = e.data; if (error) { worker.currentReject(error); } else { worker.currentResolve(result); } this.activeWorkers--; this.processQueue(); }}9. 性能优化合理分配任务：避免负载不均衡减少同步开销：尽量减少原子操作批量处理：减少消息传递次数内存复用：避免频繁的内存分配Worker 池：复用 Worker 实例10. 最佳实践使用共享内存减少数据复制合理使用原子操作进行同步避免死锁和竞态条件监控 Worker 性能和资源使用实现优雅的错误处理和恢复11. 调试多线程代码使用 Chrome DevTools 的 Worker 调试功能记录 Worker 之间的消息传递监控共享内存的状态使用性能分析工具识别瓶颈12. 挑战和限制调试复杂度增加需要处理同步和并发问题浏览器对 Worker 数量有限制移动设备性能可能受限
