"WebGPU 计算着色器与并行加速实践：WGSL、SAB 与性能度量"

WebGPU 计算着色器与并行加速实践概述通过 WebGPU 的 Compute Shader（WGSL）与 GPU 队列，实现端侧大规模并行计算（滤波、矩阵乘法、图像处理）。初始化与设备export async function initWebGPU() { if (!('gpu' in navigator)) throw new Error('WebGPU not supported') const adapter = await navigator.gpu.requestAdapter() const device = await adapter!.requestDevice() return { adapter, device } } WGSL 计算着色器const shader = /* wgsl */ ` @group(0) @binding(0) var<storage, read> input: array<f32>; @group(0) @binding(1) var<storage, read_write> output: array<f32>; @compute @workgroup_size(256) fn main(@builtin(global_invocation_id) gid: vec3<u32>) { let i = gid.x; output[i] = input[i] * 2.0; } ` 管线与命令编码export async function runCompute(device: GPUDevice, data: Float32Array) { const module = device.createShaderModule({ code: shader }) const pipeline = device.createComputePipeline({ compute: { module, entryPoint: 'main' } }) const input = device.createBuffer({ size: data.byteLength, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST }) const output = device.createBuffer({ size: data.byteLength, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC }) device.queue.writeBuffer(input, 0, data.buffer) const bindGroup = device.createBindGroup({ layout: pipeline.getBindGroupLayout(0), entries: [ { binding: 0, resource: { buffer: input } }, { binding: 1, resource: { buffer: output } } ] }) const commandEncoder = device.createCommandEncoder() const pass = commandEncoder.beginComputePass() pass.setPipeline(pipeline) pass.setBindGroup(0, bindGroup) pass.dispatchWorkgroups(Math.ceil(data.length / 256)) pass.end() device.queue.submit([commandEncoder.finish()]) return output } 读取结果与性能度量export async function readBack(device: GPUDevice, output: GPUBuffer, size: number) { const readBuffer = device.createBuffer({ size, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ }) const encoder = device.createCommandEncoder() encoder.copyBufferToBuffer(output, 0, readBuffer, 0, size) device.queue.submit([encoder.finish()]) await readBuffer.mapAsync(GPUMapMode.READ) const array = new Float32Array(readBuffer.getMappedRange()) const result = new Float32Array(array) readBuffer.unmap() return result } export async function measure(device: GPUDevice, data: Float32Array) { const start = performance.now() const output = await runCompute(device, data) const result = await readBack(device, output, data.byteLength) const end = performance.now() console.log('GPU compute time:', (end - start).toFixed(2), 'ms') return result } 技术参数与验证浏览器：Chrome 120+；硬件：支持 WebGPU 的 GPU规模：百万级元素乘法；GPU 加速耗时显著低于 CPU 循环应用场景图像滤波、矩阵运算、科学计算等端侧加速需求。注意事项资源释放与显存管理；选择合适的 workgroup_size；数据布局与对齐。常见问题Q: 与 WASM/SIMD 的关系？A: 数值型任务可结合，WebGPU 擅长大规模并行；WASM 适合密集但非图形工作负载。参考资料WebGPU/WGSL 文档与示例；GPU 编程最佳实践。---发布信息：已发布 · 技术验证 · 阅读 38 分钟 · CC BY-SA 4.0