Introduction
WebGPU represents the future of graphics rendering and parallel computing in browsers. Unlike WebGL, it provides finer GPU control, improved resource management, and native compute shader support. This advanced tutorial walks you through a complete WebGPU application implementation, from setup to pipeline optimizations. You will learn to handle buffers, bind groups, and WGSL shaders for maximum performance. Every step includes functional, ready-to-use code.
Prerequisites
- Chrome 113+ or Edge 113+ with WebGPU enabled
- Strong knowledge of JavaScript/TypeScript and GLSL/WGSL
- Understanding of GPU concepts (buffers, shaders, pipelines)
- Node.js for tooling and bundling
WebGPU Context Initialization
async function initWebGPU(): Promise<GPUDevice> {
const adapter = await navigator.gpu?.requestAdapter();
if (!adapter) throw new Error('WebGPU non supporté');
const device = await adapter.requestDevice();
return device;
}This function initializes the GPU adapter and device. Always verify WebGPU availability before running critical code.
Creating the Vertex Buffer
function createVertexBuffer(device: GPUDevice, data: Float32Array): GPUBuffer {
const buffer = device.createBuffer({
size: data.byteLength,
usage: GPUBufferUsage.VERTEX | GPUBufferUsage.COPY_DST,
mappedAtCreation: true,
});
new Float32Array(buffer.getMappedRange()).set(data);
buffer.unmap();
return buffer;
}Creates an optimized vertex buffer using mappedAtCreation for efficient initial data upload.
Simple Compute WGSL Shader
@group(0) @binding(0) var<storage, read_write> data: array<f32>;
@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) id: vec3u) {
let i = id.x;
if (i < arrayLength(&data)) { data[i] *= 2.0; }
}Basic compute shader that doubles each value in the buffer. Use a workgroup_size suited to your GPU.
Compute Pipeline Configuration
function createComputePipeline(device: GPUDevice, shaderCode: string): GPUComputePipeline {
const shaderModule = device.createShaderModule({ code: shaderCode });
return device.createComputePipeline({
layout: 'auto',
compute: { module: shaderModule, entryPoint: 'main' },
});
}Compute pipeline with auto layout to simplify the bind group. Always reuse compiled shader modules.
Executing the Compute Pass
function dispatchCompute(device: GPUDevice, pipeline: GPUComputePipeline, bindGroup: GPUBindGroup, count: number) {
const encoder = device.createCommandEncoder();
const pass = encoder.beginComputePass();
pass.setPipeline(pipeline);
pass.setBindGroup(0, bindGroup);
pass.dispatchWorkgroups(Math.ceil(count / 64));
pass.end();
device.queue.submit([encoder.finish()]);
}Dispatches the compute shader with the correct number of workgroups. Always submit commands to the GPU queue.
Best Practices
- Always use reusable bind groups and minimize pipeline switches
- Prefer persistent buffers instead of recreating them every frame
- Test performance with GPU timing queries
- Explicitly manage synchronization with device.queue.onSubmittedWorkDone()
- Validate your GPU limits (maxBufferSize, maxComputeWorkgroupSize)
Common Mistakes to Avoid
- Forgetting to call unmap() on mapped buffers, causing memory leaks
- Workgroup size not a multiple of 32 or 64, leading to poor performance
- Not checking for GPU errors via device.lost
- Ignoring storage buffer limits on certain browsers
Going Further
Deepen your knowledge with our resources on advanced rendering and compute techniques. Discover our Learni courses dedicated to WebGPU and high-performance graphics.