In response to user requests for additional ArrayFire capabilities, we have decided to extend the library to non OpenCL supported CPUs systems. This means that ArrayFire code will be portable to both devices that have OpenCL setup and devices without it. This is done through the creation of additional backends. This will allow ArrayFire users to write their code once and have it run on multiple systems. We are currently supporting  multiple systems and architectures  of NVIDIA GPUs (Tesla, Fermi, and Kepler), AMD's APUs, and Intel-based systems (x86 and PHI).

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<h4> New Capabilities</h4>
As part of this update process we are also looking at extending ArrayFire capabilities to mobile devices and low power system. We have already started to port our code to support the Qualcomm Snapdragon. Performance results are promising at this stage and we expect to publish initial results in the upcoming weeks (so keep tuned). The market's need for fast systems has brought a new and wide range of systems that all require fast algorithms at a high-level of productivity. We plan on following suit.
<h4> Language Support</h4>
To further increase the portability of ArrayFire we have also increasing the number of supported programming languages, including R, Fortran, Java. The wrappers for these languages are open-source can be found at <a title="https://github.com/arrayfire" href="https://github.com/arrayfire">https://github.com/arrayfire</a> . If you are interested in creating a wrapper for your favorite language and require assistance, feel free to contact us at <a href="mailto:dev@arrayfire.com">dev@arrayfire.com</a> and we help you.

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