In response to user requests for ArrayFire capabilities, we have decided to extend the library to support CPUs that do not have OpenCL support. This means that in practice ArrayFire will be portable to both devices that have OpenCL setup and devices that do not have it setup. This has required on our part to optimize a subset of our functions so that performance is maximized for different architectures and systems. These includes different lines of NVIDIA GPUs (Tesla, Fermi, and Kepler), AMD's APUs, and Intel-based systems (x86 and PHI).

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As part of this 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).

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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 <a title="contact us" href="mailto:dev@arrayfire.com">dev@arrayfire.com</a>

<a title="contact us" href="dev@arrayfire.com">contact us</a> and we help you.

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