ArrayFire Releases v3.6 Announced

ArrayFire Releases v3.6 Announced

ArrayFire announces the release of ArrayFire v3.6, our open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire includes several new features that improve the performance and usability for applications in machine learning, computer vision, signal processing, statistics, finance, and more.

This release includes new support for batched matrix multiply and new features, including topk function and anisotropic diffusion filter. A complete list of ArrayFire v3.6 updates and new features can be found in the product Release Notes.

With over 8 years of continuous development, the open source ArrayFire library is the top CUDA and OpenCL software library. ArrayFire supports CUDA-capable GPUs, OpenCL devices, CPUs, and other accelerators. With its easy-to-use API, this hardware-neutral software library is designed for maximum speed without the hassle of writing time-consuming CUDA and OpenCL device code. With ArrayFire’s library functions, developers can maximize productivity and performance. Each of ArrayFire’s functions has been hand-tuned by CUDA and OpenCL experts.

We use ArrayFire to run the low level parallel computing layer of SDL Neural Machine Translation Products,” said William Tambellini, Senior Software Developer at SDL. “ArrayFire flexibility, robustness and dedicated support makes it a powerful tool to support the development of Deep Learning Applications.”

Availability

Visit ArrayFire’s website to download ArrayFire v3.6 Installers or GitHub account page to download and build the source code. The ArrayFire software library operates under the BSD 3-Clause License which enables unencumbered deployment and portability of ArrayFire for all uses, including commercially.

Dedicated Support and Coding Services

ArrayFire offers dedicated support packages for ArrayFire users. ArrayFire serves many clients through consulting and coding services, algorithm development, porting code, and training courses for developers.

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Source: insideBigData

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