WebGPU SortingWebGPU Sorting Library and Demo

Quick Start ​

import { GPUContext, BitonicSorter } from 'webgpu-sorting';

// Initialize WebGPU context
const gpu = new GPUContext();
await gpu.initialize();

// Create sorter
const sorter = new BitonicSorter(gpu);

// Sort data on GPU
const data = new Uint32Array([5, 2, 8, 1, 9, 3, 7, 4, 6, 0]);
const { sortedData } = await sorter.sort(data);

console.log(sortedData); // [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Browser Support ​

Why GPU Sorting? ​

GPU sorting becomes advantageous when:

• Array size is large enough - Buffer upload and readback overhead is amortized
• Batch processing - Multiple sorts can share GPU context
• Real-time applications - Low-latency sorting for visualizations, simulations
• Integer-heavy workloads - Radix sort excels on Uint32Array data

Use the interactive demo to measure the crossover point on your own hardware instead of relying on fixed benchmark claims.

Architecture Overview ​

graph TB
    subgraph CPU["CPU Side"]
        A[Input Array] --> B[Generate Data]
        B --> C[Create GPU Buffer]
    end

    subgraph GPU["GPU Compute Shader"]
        D[Read Buffer] --> E[Bitonic/Radix Sort]
        E --> F[Parallel Passes]
        F --> G[Write Sorted Buffer]
    end

    subgraph Output
        G --> H[Read Back to CPU]
        H --> I[Validation & Timing]
    end

    C --> D

Learn more in the Architecture documentation.