arrayfire for Mac

ArrayFire is a general-purpose tensor library that simplifies the software development process 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:

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, oneAPI, OpenCL, and native CPU on Windows, Mac, and Linux Built-in visualization functions through

Commercially friendly open-source licensing Enterprise 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 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 (AMD, Intel, NVIDIA, 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.

The complete source code can be found here.

Perceptron

The complete source code can be found here.

For more code examples, visit the examples/ directory.

Experimental backends

Community-contributed backends that are not part of ArrayFire releases live on their own branches. They build from source only and are not covered by the release test matrix.

Pull requests for these backends should target their branch, not master.

Documentation

You can find the complete documentation here.

Quick links:

List of functions Tutorials Examples Blog

Language support

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

† †

†  Community maintained wrappers

__In-Progress Wrappers__

Contributing

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 Mission 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, Developer Commits, or Governance.

Citations and Acknowledgements