# gpujs/gpu.js

GPU Accelerated JavaScript

Repository: https://github.com/gpujs/gpu.js
Canonical: https://ross.abutalabs.com/products/gpujs
Homepage: https://gpu.rocks
Language: JavaScript
License: MIT
License Family: permissive
Topics: gpu, webgl, javascript, math, gpgpu, glsl, nodejs
Last push: 2026-08-05T15:59:42+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 96, longevity 100
- inputs: {"age_days": 3858, "days_push": 28, "days_rel": 28, "gap_med": 0.0, "n_releases_24m": 21}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15370, forks 661 (observed 2026-08-28T04:11:09.714505+00:00)

## What it is
GPU.js is a JavaScript acceleration library for GPGPU that automatically transpiles JavaScript functions into shader language (GLSL) and compiles them to run on the GPU, with automatic CPU fallback when no GPU is available. It works in both the browser (via WebGL) and Node.js, often delivering 1-15x speedups for parallelizable computations like matrix math.

## Use cases
- accelerate matrix multiplication in javascript
- run parallel computations on the gpu from the browser
- speed up heavy math loops in node.js
- transpile javascript functions to webgl shaders
- add gpu acceleration with cpu fallback
- compute large 2d arrays faster in javascript

## When to choose
- you need GPU acceleration for parallelizable numeric JavaScript code without writing shaders
- you want a single codebase that runs in browser and Node.js with automatic CPU fallback
- your workload involves matrix math, image processing, or large array computations

## When to avoid
- your code is inherently sequential or branch-heavy, where GPU gains are minimal
- you need WebGPU or CUDA-specific features rather than WebGL-based kernels
- you need a full machine learning framework with autodiff and model training

## Facets
- artifact type: library
- maturity: active
- function: gpu-computing, math, compiler, machine-learning
- domain: gpu-computing, web-development, developer-tools, performance
- platform: browser, cross-platform
- tags: gpgpu, webgl, glsl, shader-transpilation, cpu-fallback, parallel-computing, algorithms, nodejs, javascript, web

## Member repositories
- gpujs/gpu.js (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.714505+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:06:20.998147+00:00, confidence not recorded.
  - readme: https://github.com/gpujs/gpu.js (fetched 2026-08-28T04:11:09.714505+00:00, sha b27b67aed491)
  - homepage: https://gpu.rocks (fetched 2026-08-29T08:04:28.432493+00:00, sha 8f53096d2ea2)
  - site_page: https://gpu.rocks/install (fetched 2026-08-29T08:04:28.441676+00:00, sha 2b0b856e6027)
- Data as of 2026-08-30T08:39:29.467469+00:00.