warp-drive-express

A simple yet powerful Node.js and Express.js middleware to offload CPU-intensive tasks to the GPU, boosting performance for parallel computations. Ideal for machine learning, data analysis, and scientific computing applications.

This middleware acts as a drop-in solution that prefers the GPU over the CPU for heavy computations, with a graceful fallback to the CPU when a GPU context is not available. It uses the powerful gpu.js library to perform GPGPU (General-Purpose computing on Graphics Processing Units).

Why use warp-drive-express?

• Performance Boost: Drastically speed up parallelizable tasks by leveraging the massive parallel processing power of modern GPUs.
• Easy Integration: A simple-to-use Express.js middleware that can be added to any existing application.
• Graceful Fallback: Automatically falls back to CPU execution if a GPU is not available, ensuring your application runs anywhere.
• Extensible: Register your own custom GPU and CPU kernels to tailor the middleware to your specific needs.

Quickstart Usage

1. Install the package:

   npm install warp-drive-express

2. Add the middleware to your Express.js application:

   const express = require('express');
   const { createWarpDriveExpress } = require('warp-drive-express');
   
   const app = express();
   
   // Add the middleware
   app.use(createWarpDriveExpress());
   
   // Your routes can now access the GPU kernels via req.warpDrive
   app.get('/my-computation', (req, res) => {
     const { matmul } = req.warpDrive.kernels;
     const result = matmul([1, 2], [3, 4]); // This will run on the GPU if available
     res.json(result);
   });
   
   app.listen(3000, () => {
     console.log('Server running on port 3000');
   });

Quick Example Usage

Here's an example of a route that calculates the Mandelbrot set, a computationally intensive task, using the warp-drive-express middleware.

app.get('/mandelbrot', (req, res) => {
  const { mandelbrot } = req.warpDrive.kernels;
  const { w = 200, h = 200, iter = 100 } = req.query;

  // The 'auto' mode will try to use the GPU first, and fallback to CPU
  const result = mandelbrot(parseInt(w), parseInt(h), parseInt(iter), 'auto');

  res.send(result); // The result will be an image buffer
});

Demo Server

For a more comprehensive set of examples and benchmarks, please see the demo server available on our GitHub repository. The demo server includes examples for:

• Mandelbrot set generation
• Matrix multiplication
• An A/B test to compare CPU vs. GPU performance

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