ArrayFire: a general purpose GPU library.
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By arrayfire

c cpp c-plus-plus cuda arrayfire scientific-computing gpu opencl gpgpu performance hpc

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.

Several of ArrayFire's benefits include:

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 runs 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.

Getting ArrayFire

Instructions to install or to build ArrayFire from source can be found on the wiki.

Conway's Game of Life Using ArrayFire

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); // Init 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
The complete source code can be found here.


array predict(const array &X, const array &W) {
return sigmoid(matmul(X, W));

array train(const array &X, const array &Y,
double alpha = 0.1, double maxerr = 0.05,
int maxiter = 1000, bool verbose = false) {
array Weights = constant(0, X.dims(1), Y.dims(1));

for (int i = 0; i < maxiter; i++) {
array P = predict(X, Weights);
array err = Y - P;
if (mean<float>(abs(err) < maxerr) break;
Weights += alpha * matmulTN(X, err);
return Weights;


array Weights = train(train_feats, train_targets);
array test_outputs = predict(test_feats, Weights);
display_results(test_images, test_outputs,
test_targets, 20);

The complete source code can be found here.

For more code examples, visit the examples/ directory.


You can find the complete documentation here.

Quick links:

Language support

ArrayFire has several official and community maintained language API's:

†  Community maintained wrappers

In-Progress Wrappers


The community of ArrayFire developers invites you to build with us if you are
interested and able to write top-performing tensor functions. Together we can
fulfill The ArrayFire
for fast scientific computing for all.

Contributions of any kind are welcome! Please refer to the
wiki and our Code of Conduct
to learn more about how you can get involved with the ArrayFire Community
through Sponsorship,
or Governance.

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

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