gpu.js

library2.16.0jsnpmunverified

GPU.js is a JavaScript library designed for General Purpose computing on Graphics Processing Units (GPGPU), enabling high-performance numerical computations in both web browsers and Node.js environments. It achieves this by automatically transpiling ordinary JavaScript functions into shader language (e.g., GLSL for WebGL) which then executes directly on the GPU, leveraging parallel processing capabilities for significant speedups, often 1-15x faster than CPU-bound operations. The library includes a robust fallback mechanism, ensuring that if a GPU is unavailable, computations seamlessly revert to standard JavaScript execution on the CPU. The current stable version is 2.16.0, with recent releases primarily focusing on maintenance, bug fixes (including security and memory leak issues in earlier 2.x versions), and performance enhancements. A key differentiator is its abstraction over complex shader programming, allowing developers to write GPU-accelerated code using familiar JavaScript syntax and a `this.thread.x/y/z` model for accessing thread indices within the kernel.

ai-ml data

npm install gpu.js

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · GPU.JS

gpu.js

ai-mljavascriptv2.16.0

Install

—

Import

—

Disk

—

Pass rate

0/ 6

Env Coverage0 / 6

glibc

18–22

musl

18–22

Install & Compatibility

Where this runs

tested against v? · npm install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

node 18–226 runs

build_error

glibc

node 18–226 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

GPU

✓ import { GPU } from 'gpu.js';

✗ const GPU = require('gpu.js');

For ESM environments (modern Node.js, bundlers), use named import. CommonJS `require` is also supported for Node.js.

GPU (browser global)

✓ const gpu = new GPU();

✗ import { GPU } from 'gpu.js';

When `gpu-browser.min.js` is loaded via a `<script>` tag, `GPU` is available as a global variable. Do not use `import` in this context.

IKernelFunction

✓ import { IKernelFunction } from 'gpu.js';

Import `IKernelFunction` from 'gpu.js' for type-checking kernel functions when using TypeScript, as seen in advanced examples.

This quickstart demonstrates how to set up `gpu.js`, define a GPU kernel for 512x512 matrix multiplication, and execute it, showcasing the basic API for offloading computations.

import { GPU } from 'gpu.js'; // Initialize GPU.js const gpu = new GPU(); // Define two 512x512 matrices for multiplication const matrixA = Array(512).fill(0).map(() => Array(512).fill(Math.random())); const matrixB = Array(512).fill(0).map(() => Array(512).fill(Math.random())); // Create a GPU accelerated kernel function for matrix multiplication const multiplyMatrix = gpu.createKernel(function(a: number[][], b: number[][]) { let sum = 0; for (let i = 0; i < 512; i++) { // this.thread.x and this.thread.y represent the current thread's coordinates sum += a[this.thread.y][i] * b[i][this.thread.x]; } return sum; }).setOutput([512, 512]); // Define the output dimensions of the kernel // Run the kernel with the input matrices const c = multiplyMatrix(matrixA, matrixB) as number[][]; console.log('Matrix multiplication completed on GPU (or CPU fallback).'); console.log('Resulting matrix dimensions:', c.length, 'x', c[0].length); // For a real application, you'd inspect 'c' for results.

Debug

Known issues

breakingGPU.js v2 introduced new concepts around 'pipelining' for chaining kernels efficiently and explicit texture cloning/cleanup. While these were performance features, they implied new memory management considerations for users chaining kernels, specifically regarding `cleanupPipelineTextureMemory()`.

fix

Review the 'Pipelining' and 'Cleanup pipeline texture memory' sections in the documentation for proper memory management when chaining kernels and using `pipeline: true`.

affects: >=2.0.0

gotchaExternal functions (functions not defined within the kernel's scope) cannot be directly called from within a `createKernel` function. The kernel's body is transpiled to GLSL, which does not have access to the surrounding JavaScript scope.

fix

Use `gpu.addFunction(yourFunction)` to make helper functions available inside kernels, or define helper functions directly inside the kernel if they are simple enough to be transpiled.

affects: >=1.0.0

gotchaThe `this` context inside a GPU.js kernel function (`createKernel` callback) refers to the GPU thread's properties (`this.thread.x`, `this.thread.y`, `this.thread.z`) and not the surrounding JavaScript context. Accessing external variables directly from `this` will fail.

fix

Pass external variables as arguments to the `createKernel` function or use `constants` in the kernel configuration. Do not attempt to access `this.someVariable` if `someVariable` is not a kernel property.

affects: >=1.0.0

breakingVersions 2.8.4 and 2.8.5 addressed significant memory leaks related to texture deallocation. Earlier versions could consume excessive GPU memory, especially with frequent kernel runs or pipeline usage, potentially leading to performance degradation or crashes.

fix

Upgrade to GPU.js version 2.8.5 or newer to benefit from memory leak fixes. Ensure `kernel.destroy()` is called when a kernel is no longer needed to explicitly free resources.

affects: <2.8.4

breakingVersion 2.8.3 included a fix for a security issue. Details are not publicly disclosed beyond 'Fix Security Issue' in the release notes, but users on older versions are advised to upgrade for security patching.

fix

Upgrade to GPU.js version 2.8.3 or newer to mitigate potential security vulnerabilities.

affects: <2.8.3

Errors

Common errors & fixes

TypeError: Cannot read properties of undefined (reading 'x')

Attempting to access `this.thread.x` (or `y`/`z`) outside of a GPU.js kernel function, or when `this` context is incorrectly bound.

fix

Ensure `this.thread` is only accessed within the callback function passed to `gpu.createKernel()` or a function called by it that was added via `gpu.addFunction()`.

WebGL: INVALID_VALUE: texImage2D: width or height is 0

The output dimensions specified in `.setOutput()` resulted in a zero-dimension texture, or input arrays were empty/invalid.

fix

Verify that the arguments passed to `.setOutput([width, height, depth])` are positive integers reflecting the desired output size of the computation. Also, check input array dimensions.

Error: Function 'someFunction' is not supported.

A JavaScript function or method called inside the kernel could not be transpiled into an equivalent GLSL shader function by GPU.js.

fix

Review the list of supported Math methods and language features in the GPU.js documentation. For custom functions, use `gpu.addFunction()` to provide a transpilable implementation or refactor the logic to use supported operations.

Error: GLSL failed to compile

The transpiled GLSL code for the kernel contains syntax errors or uses unsupported features for the target WebGL environment, often due to complex JavaScript logic that can't be directly translated.

fix

Simplify the kernel logic. Avoid complex closures, object manipulations, or unsupported data structures within the kernel. Enable debugging (`gpu.setDebug(true)`) and check browser console logs for detailed GLSL compilation errors.

Upgrade

Version history

2.16.0latest on npm

Audit

Dependencies

No dependency data recorded yet.

Agent activity

3 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/gpujs/gpu.js ↗

packagehttps://www.npmjs.com/package/gpu.js ↗

homepagehttps://gpu.rocks ↗