As of 0.16.0 this module has been renamed from arrayfire_js to arrayfire-js. The old arrayfire_js module will be available on the npm for a while.
Please follow this issue for tracking the progress towards release of 1.0.0-beta.
"ArrayFire is a high performance software library for parallel computing with an easy-to-use API. Its array based function set makes parallel programming more accessible."
You can read its introduction int its documentation's index page. It's basically a math accelerator C++ library supporting CPU and GPU based backends on Windows, Linux and Mac. And it's just awesome. It's extremely simple to write the most complex mathematical, statistical, logical computations, image transformations and computer vision algorigthms with it, just a few lines of code. It has excellent batching capability that takes simple operations, make a big computation from them, and runs all at once on the GPU device.
ArrayFire.js is the Node.js bindings for ArrayFire, it uses CMake.js as of its build system. It takes Node.js' insane level of productivity and mix that with ArrayFire's insane level of performance and simplicity. You'll get something like Matlab just in familiar JavaScript with performance level of x100+ compared to V8 computation preformance (with a good GPU).
- Download and install ArrayFire (3.x RTM is supported right now). Don't forget to add
%AF_PATH%\libdirectory to PATH on Windows! - On Linux or Mac install dependencies (see Linux and Mac docs)
- Don't forget to install CMake
Before installing location of the ArrayFire installation directory have to be configured for CMake.js. There are two options:
1. Using npm config
for current user:
npm config set cmake_af_path "path_to_arrayfire_installation_directory"
for all users (global)
npm config set cmake_af_path "path_to_arrayfire_installation_directory" --global
2. Setting AF_PATH environment variable
AF_PATH="path_to_arrayfire_installation_directory"
On Windows the installer do this for you, so there is nothing to do on this platform, though.
The above have to be done only once. After you can install ArrayFire.js from the npm:
npm install arrayfire-js --save
// CPU
var af = require("arrayfire-js")("CPU");
// OpenCL
var af = require("arrayfire-js")("OpenCL");
// CUDA
var af = require("arrayfire-js")("CUDA");Port of the PI calculator from ArrayFire documentation:
C++
// sample 40 million points on the GPU
array x = randu(20e6), y = randu(20e6);
array dist = sqrt(x * x + y * y);
// pi is ratio of how many fell in the unit circle
float num_inside = sum<float>(dist < 1);
float pi = 4.0 * num_inside / 20e6;
af_print(pi);JavaScript
Notice: Remember, in Node.js everything that blocks or might blocks should be asynchronous, so it is advised to call asynchronous variants of ArrayFire.js functions, however there are synchronous counterparts available too for supporting REPL scenarios. (I suggest use ES6 generators instead of callback hell or even instead of bare promises).
const numberOfPoints = 20000000;
// ...
let x = af.randu(numberOfPoints, af.dtype.f32);
let y = af.randu(numberOfPoints, af.dtype.f32);
let dist = af.sqrt(x.mul(x).add(y.mul(y)));
let numInside = yield af.sumAsync(dist.lt(1));
let piVal = (4.0 * numInside) / numberOfPoints;
console.log(`PI = ${piVal}`);It's included in the examples folder. To run on:
- io.js, enter:
iojs examples/es6/bechmarks/pi.js - Node.js 0.12 or above, enter:
node --harmony examples/es6/bechmarks/pi.js - Node.js below 0.12, enter:
node examples/es5/bechmarks/pi.js
There is an example of a neural network with batch backpropagation trained to learn the famous MNIST data set. It will run on the fastest device available.
It's in the examples folder. To run on:
- io.js, enter:
iojs examples/es6/machine-learning/neuralNetwork.js - Node.js 0.12 or above, enter:
node --harmony examples/es6/machine-learning/neuralNetwork.js - Node.js below 0.12, enter:
node examples/es5/machine-learning/neuralNetwork.js
Performance on Linux Mint x64, i5 3570, Radeon R9:
- CPU platform: 0.8 sec / epoch
- OpenCL platform on CPU: 1.0 sec / epoch
- OpenCL platform on GPU: 0.28 sec / epoch
http://arrayfire.github.io/arrayfire-js
PRs are welcome, but please read the Contributing Guide first.
Copyright (c) 2014-2015, ArrayFire
Copyright (c) 2015 Gábor Mező aka unbornchikken (gabor.mezo@outlook.com)
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
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list of conditions and the following disclaimer.
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contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
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