ArrayFire is the fastest GPU matrix library with the simplest API.

ArrayFire is the fastest GPU software

It contains the fastest implementations of data analysis and linear algebra routines that enable it outperform CPU libraries like IPP, MKL, Eigen, Armadillo, and more.

It is optimized for any CUDA-enabled GPU. The same code will run on laptops, desktops, or servers.

It includes thousands of lines of highly-tuned device code.

It performs run-time analysis of your code to increase arithmetic intensity and memory throughput while avoiding unnecessary temporary allocations.

It combines and enhances all the best CUDA libraries available, including the fastest FFT, BLAS, and LAPACK implementations.

ArrayFire is the easiest-to-use GPU software

A simple array notation you can learn in minutes.

A few lines of ArrayFire code accomplishes what would have taken 10-100X lines in raw CUDA.

It supports easily scaling to take advantage of multiple GPUs.

ArrayFire is the most comprehensive GPU software

It has hundreds of functions you need to make your code faster including arithmetic, linear algebra, statistics, and other algorithms (see more).

It supports single- and double-precision floating point values, complex numbers, and booleans (see more).

It supports manipulating vectors, matrices and arrays up to 4 dimensions

See it in action...

Here's an example to perform inner product on a matrix

 program inner_mul
   use arrayfire
   implicit none
   type(array) A
   real, allocatable, dimension(:,:) res
   A = randu(10, 2)
   call print(A)
   res = matmul(A, transpose(A))
   write(*,*) "Output of inner mul: "
   write(*,*) b(:,:)
 end program inner_mul

Download and Requirements

Download the latest stable version of the library and view documentation online. See Release Notes for a list of changes in each version. You can also download nightly builds including the latest features and bug fixes. Supported Platforms:

Linux (32 and 64-bit) - Ubuntu 10+, Fedora 10+, OpenSUSE 11+, RHEL 5+, CentOS 5+, SLES 10+

Requirements:

CUDA-enabled GPU
CUDA 4.2 toolkit and driver
Linux and Mac require gcc/g++ 4.2 or later

Getting Started:

The Getting Started tutorial walks through more detailed steps on getting your first example to compile and run.

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