ArrayFire is a general-purpose tensor library that simplifies the process of
software development for the parallel architectures found in CPUs, GPUs, and
other hardware acceleration devices. The library serves users in every technical
computing market.

The community of ArrayFire developers invites you to build with us if you're
interested and able to write top performing tensor functions. Together we can fulfill
The ArrayFire Mission
under an excellent Code of Conduct
that promotes a respectful and friendly building experience.

Several of ArrayFire's benefits include:

Hundreds of accelerated tensor computing functions, in the following areas:
- Array handling
- Computer vision
- Image processing
- Linear algebra
- Machine learning
- Standard math
- Signal Processing
- Statistics
- Vector algorithms
Easy to use, stable,
well-documented API
Rigorous benchmarks and tests ensuring top performance and numerical accuracy
Cross platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux
Built-in visualization functions through Forge
Commercially friendly open-source licensing
Commercial support from ArrayFire

ArrayFire provides software developers with a high-level
abstraction of data that resides on the accelerator, the af::array object.
Developers write code that performs operations on ArrayFire arrays which, in turn,
are automatically translated into near-optimal kernels that execute on the computational
device.

ArrayFire is successfully used on devices ranging from low-power mobile phones
to high-power GPU-enabled supercomputers. ArrayFire runs on CPUs from all
major vendors (Intel, AMD, ARM), GPUs from the prominent manufacturers
(NVIDIA, AMD, and Qualcomm), as well as a variety of other accelerator devices
on Windows, Mac, and Linux.

Installation

You can install the ArrayFire library in one of the following ways:

Package Managers

This approach is currently only supported for Ubuntu 18.04 and 20.04. Please
go through our GitHub wiki page for the detailed instructions.

Official installers

Execute one of our official binary installers for Linux, OSX, and Windows platforms.

Build from source

Build from source by following instructions on our wiki.

Examples

The following examples are simplified versions of
helloworld.cpp
and
conway_pretty.cpp,
respectively. For more code examples, visit the
examples/
directory.

Hello, world!

array A = randu(5, 3, f32); // Create 5x3 matrix of random floats on the GPU
array B = sin(A) + 1.5;     // Element-wise arithmetic
array C = fft(B);           // Fourier transform the result

float d[] = { 1, 2, 3, 4, 5, 6 };
array D(2, 3, d, afHost);   // Create 2x3 matrix from host data
D.col(0) = D.col(end);      // Copy last column onto first

array vals, inds;
sort(vals, inds, A);        // Sort A and print sorted array and corresponding indices
af_print(vals);
af_print(inds);

Conway's Game of Life

Visit the
Wikipedia page for a
description of Conway's Game of Life.

static const float h_kernel[] = {1, 1, 1, 1, 0, 1, 1, 1, 1};
static const array kernel(3, 3, h_kernel, afHost);

array state = (randu(128, 128, f32) > 0.5).as(f32); // Generate starting state
Window myWindow(256, 256);
while(!myWindow.close()) {
  array nHood = convolve(state, kernel); // Obtain neighbors
  array C0 = (nHood == 2);               // Generate conditions for life
  array C1 = (nHood == 3);
  state = state * C0 + C1;               // Update state
  myWindow.image(state);                 // Display
}

Documentation

You can find our complete documentation here.

Quick links:

Language support

ArrayFire has several official and third-party language API`s:

Native

Official wrappers

We currently support the following language wrappers for ArrayFire:

Wrappers for other languages are a work-in-progress:
.NET,
Fortran,
Go,
Java,
Lua,
NodeJS,
R,
Ruby

Third-party wrappers

The following wrappers are being maintained and supported by third parties:

Contributing

Contributions of any kind are welcome! Please refer to
the wiki
to learn more about how you can get involved with ArrayFire.

Citations and Acknowledgements

If you redistribute ArrayFire, please follow the terms established in
the license. If you wish to cite ArrayFire in an academic
publication, please use the following citation document.

ArrayFire development is funded by AccelerEyes LLC and several third parties,
please see the list of acknowledgements for an expression of our gratitude.

Support and Contact Info

Slack Chat
Google Groups
ArrayFire Services: Consulting | Support | Training

Trademark Policy

The literal mark “ArrayFire” and ArrayFire logos are trademarks of
AccelerEyes LLC (dba ArrayFire).
If you wish to use either of these marks in your own project, please consult
ArrayFire's Trademark Policy

melonakos/arrayfire