gpu.rocks valid

GPU.js

GPGPU in pure JavaScript: gpu.js compiles JavaScript functions into shader

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-26T08:04:23.681Z. Cached copy · Source on gpu.rocks

# GPU.js

> GPGPU in pure JavaScript: gpu.js compiles JavaScript functions into shader
> code and runs them on the GPU, falling back to the CPU when there is no
> GPU available. gpu.rocks also hosts a free, hands-on GPGPU course that runs
> entirely in the browser.

Every page below is also available as Markdown by appending `.md` to its URL.
The whole course in one file: https://gpu.rocks/llms-full.txt

## Site

- [GPU.js — GPU accelerated JavaScript](https://gpu.rocks/index.md): GPGPU operations using pure JavaScript. gpu.js compiles your JavaScript functions into shader code and runs them on your GPU — with a CPU fallback.
- [Benchmark — GPU.js](https://gpu.rocks/benchmark.md): Thirty GPGPU workloads timed in your browser on every backend gpu.js can reach — WebGPU, WebGL2, WebGL, WebAssembly and CPU — against hand-written implementations with no gpu.js in them. Every answer is checked against a plain-JavaScript oracle before it is timed.
- [Installation — GPU.js](https://gpu.rocks/install.md): Install GPU.js from npm, yarn, or a CDN script tag and write your first GPU-accelerated JavaScript kernel in the browser or Node.js.
- [Examples — GPU.js](https://gpu.rocks/examples.md): GPU.js examples: matrix multiplication and more GPGPU snippets you can read, run, and benchmark on your own graphics card.
- [Learn GPGPU in your browser — GPU.js Learn](https://gpu.rocks/learn.md): A free hands-on GPGPU course built on gpu.js: write real kernels in your browser, run them on your own GPU, and learn ideas that transfer to CUDA and WebGPU.

## Course — GPGPU 101

From zero to your first thousand threads

- [Hello, Kernel](https://gpu.rocks/learn/hello-kernel-f1399353.md): What a kernel is, what a thread is, and why this.thread.x replaces your for-loop.
- [Data In, Data Out](https://gpu.rocks/learn/data-in-data-out-42b68d01.md): Feeding arrays and images into kernels, shaping 1D/2D/3D output, and reading results back.
- [Pipelines & Textures](https://gpu.rocks/learn/pipelines-and-textures-9f4aeaa5.md): Chaining kernels so data stays on the GPU — the single biggest real-world speedup.
- [Measuring Speed Honestly](https://gpu.rocks/learn/measuring-speed-honestly-b9188894.md): Warm-up, transfer costs, and precision — when the GPU wins, and when the CPU quietly beats it.

## Course — Parallel Primitives

The handful of patterns everything else is built from

- [Thinking in Parallel](https://gpu.rocks/learn/thinking-in-parallel-c3876efb.md): Map and gather patterns, why kernels write only their own cell, and how to design around it.
- [Reductions](https://gpu.rocks/learn/reductions-3dadc130.md): Sum, min, max and mean over millions of values — the ladder pattern every platform uses.
- [Prefix Sums (Scan)](https://gpu.rocks/learn/prefix-sum-351cfa41.md): Running totals in parallel — the doubling ladder, exclusive scans, and the offsets every variable-sized output depends on.
- [Stream Compaction](https://gpu.rocks/learn/stream-compaction-0aed2e43.md): Filtering o
…

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