WasmGPU.compute.encodeDispatchBatch¶

Summary¶

WasmGPU.compute.encodeDispatchBatch records multiple dispatch commands in one compute pass. This is useful for multi-step compute workflows where one encoder should contain all stages. It validates all workgroup tuples before beginning the pass and can additionally check them against device limits. Commands with a zero dimension are skipped. Use this when you want fewer pass transitions and fewer queue submissions.

Kernel helpers that require native encoder operations such as clearBuffer() may impose additional external-encoder restrictions; consult the specific kernel page.

Syntax¶

WasmGPU.compute.encodeDispatchBatch(encoder: GPUCommandEncoder, commands: ReadonlyArray<ComputeDispatchCommand>, label?: string, validateLimits?: boolean): void
wgpu.compute.encodeDispatchBatch(encoder, commands, label, validateLimits);

Parameters¶

Name	Type	Required	Description
`encoder`	`GPUCommandEncoder`	Yes	Encoder that receives batched compute commands.
`commands`	`ReadonlyArray<ComputeDispatchCommand>`	Yes	Ordered dispatch commands encoded into one compute pass.
`label`	`string`	No	Optional label applied to the compute pass.
`validateLimits`	`boolean`	No	When `true`, validates each command's workgroups against device limits.

Returns¶

void - This method does not return a value.

Type Details¶

type ComputeDispatchCommand = {
    pipeline: GPUComputePipeline | ComputePipeline;
    bindGroups?: ReadonlyArray<GPUBindGroup | null | undefined>;
    workgroups: readonly [number, number, number] | { x: number; y?: number; z?: number };
    label?: string;
};

Example¶

const canvas = document.querySelector("canvas");
const wgpu = await WasmGPU.create(canvas);

const storage = wgpu.compute.createStorageBuffer({ data: new Float32Array(512), copySrc: true });
const pipeline = wgpu.compute.createPipeline({
    code: `@group(0) @binding(0) var<storage, read_write> a: array<f32>; @compute @workgroup_size(128) fn main(@builtin(global_invocation_id) gid: vec3<u32>) { if (gid.x < 512u) { a[gid.x] = a[gid.x] + 1.0; } }`,
    bindGroups: [{ entries: [{ binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: { type: "storage" } }] }]
});
const bindGroup = pipeline.createBindGroup(0, { 0: storage });

const encoder = wgpu.gpu.device.createCommandEncoder();
wgpu.compute.encodeDispatchBatch(encoder, [
    { pipeline, bindGroups: [bindGroup], workgroups: [4, 1, 1], label: "step-1" },
    { pipeline, bindGroups: [bindGroup], workgroups: [4, 1, 1], label: "step-2" }
], "two-step", true);
wgpu.gpu.queue.submit([encoder.finish()]);