Workload Partitioning Using Thread Indices in gpu.js

Processing massive datasets in gpu.js often exceeds hardware limits or browser execution timeouts if dispatched in a single call. To solve this, developers partition large workloads into smaller chunks and process them iteratively or across multidimensional grids. By leveraging built-in thread indices—such as this.thread.x and this.thread.y—along with offset parameters, each thread can compute its exact location within a target chunk. This guide explains how thread indices work in gpu.js, how to map them to partitioned data, and how to structure kernels to process data sequentially without exhausting GPU resources.

Understanding Thread Indices in gpu.js

When you define a kernel in gpu.js and assign an execution size using .setOutput([width, height, depth]), the library instantiates an execution grid. Inside the kernel function, each execution unit accesses its assigned coordinates via:

Because these coordinates represent local positions within the current kernel execution boundary, partitioning larger datasets requires translating these local coordinates into global indices.

The Chunk Offset Pattern

The most reliable way to partition data in gpu.js is to define a static chunk size for the kernel output, then pass a dynamically incremented chunkOffset as an argument from the CPU host loop.

1D Data Partitioning

For a flat array of size \(N\) that exceeds the execution limits of a single pass, choose a manageable chunk size \(C\) (for example, 65,536 elements).

const { GPU } = require('gpu.js');
const gpu = new GPU();

const totalElements = 1000000;
const chunkSize = 65536;

// Kernel output matches chunk size, not total data size
const computeChunk = gpu.createKernel(function(data, chunkOffset, totalSize) {
    const globalIndex = this.thread.x + chunkOffset;
    
    // Boundary check for the final, potentially partial chunk
    if (globalIndex >= totalSize) {
        return 0;
    }
    
    // Perform compute operation
    return data[globalIndex] * 2.0;
}).setOutput([chunkSize]);

// Host execution loop
const fullData = new Float32Array(totalElements).fill(1.5);
const results = [];

for (let offset = 0; offset < totalElements; offset += chunkSize) {
    const chunkResult = computeChunk(fullData, offset, totalElements);
    results.push(chunkResult);
}

In this pattern:

  1. this.thread.x ranges strictly from 0 to chunkSize - 1.
  2. globalIndex correctly locates the target element in the global dataset.
  3. The host loop pushes execution offsets sequentially, avoiding browser WebGL watchdog timeouts (TDR).

2D Workload Partitioning (Matrix Tiles)

For matrix operations or image processing, workloads are partitioned into 2D tiles. This prevents exceeding the maximum texture dimension allowed by the client's WebGL implementation.

const tileSize = 512;

const processMatrixTile = gpu.createKernel(function(matrix, offsetX, offsetY, width, height) {
    const globalX = this.thread.x + offsetX;
    const globalY = this.thread.y + offsetY;

    if (globalX >= width || globalY >= height) {
        return 0;
    }

    return matrix[globalY][globalX] + 1.0;
}).setOutput([tileSize, tileSize]);

// Process a large matrix in tiles
const matrixWidth = 2048;
const matrixHeight = 2048;

for (let y = 0; y < matrixHeight; y += tileSize) {
    for (let x = 0; x < matrixWidth; x += tileSize) {
        const tileResult = processMatrixTile(largeMatrix, x, y, matrixWidth, matrixHeight);
        // Store or copy tileResult to final buffer
    }
}

Stride-Based Partitioning Within a Single Kernel

If chunking across multiple kernel invocations is not necessary, but you need to process large data within a single kernel run using a fixed number of threads, use a strided loop.

Because gpu.js compiles JavaScript into GLSL, loop limits must be deterministic and statically bounded. Instead of dynamic while-loops, define a fixed maximum iteration count:

const maxIterationsPerThread = 4;
const threadCount = 256;

const stridedKernel = gpu.createKernel(function(data, totalSize) {
    let sum = 0;
    
    // Stride through the data
    for (let i = 0; i < 4; i++) {
        const targetIndex = this.thread.x + (i * 256);
        if (targetIndex < totalSize) {
            sum += data[targetIndex];
        }
    }
    
    return sum;
}).setOutput([threadCount]);

Key Considerations for Boundary Conditions

  1. Non-Divisible Lengths: Datasets rarely divide evenly by the chunk size. Always pass the total data length into the kernel and guard operations with a conditional check (if (globalIndex >= totalSize)) to avoid accessing unallocated memory.
  2. Memory Transfer Overhead: In chunked execution loops, passing large arrays repeatedly to the kernel can create CPU-GPU bus bottlenecks. If memory permits, load data using gpu.js textures or keep input buffers resident on the GPU to minimize upload penalties across chunks.