WasmGPU.compute.kernels.mulC64¶

Summary¶

mulC64 multiplies interleaved complex64 vectors element by element using complex multiplication.

Syntax¶

WasmGPU.compute.kernels.mulC64(a: StorageBuffer, b: StorageBuffer, opts?: VectorKernelOptions): StorageBuffer

Parameters¶

Name	Type	Required	Description
`a`	`StorageBuffer`	Yes	First input vector.
`b`	`StorageBuffer`	Yes	Second input vector.
`opts`	`VectorKernelOptions`	No	Element count, reusable output, encoder, label, and workgroup-limit validation.

Returns¶

StorageBuffer - Output containing a * b for each selected element. A newly allocated buffer enables COPY_SRC; a supplied opts.out is returned unchanged by identity.

Type Details¶

type VectorKernelOptions = {
    count?: number;
    out?: StorageBuffer;
    encoder?: GPUCommandEncoder;
    label?: string;
    validateLimits?: boolean;
};

Each complex value is stored as adjacent [real, imaginary] f32 components. When opts.count is omitted, the input logical lengths must match and that shared length is used. Optional output buffer with at least count * 8 bytes. The output must be distinct from every input. When opts.encoder is supplied, commands are recorded but not submitted by this call.

Example¶

const wgpu = await WasmGPU.create(document.querySelector("canvas"));
const a = wgpu.compute.createStorageBuffer({ data: new Float32Array([1, 0, 2, -1, 3, 0, 4, 2]) });
const b = wgpu.compute.createStorageBuffer({ data: new Float32Array([1, 0, 2, -1, 3, 0, 4, 2]) });
const out = wgpu.compute.kernels.mulC64(a, b);
const values = await wgpu.compute.readback.readF32(out);
console.log(Array.from(values));