ArrayFire v3.4 Released
ArrayFire has released the latest version of its flagship software ArrayFire v3.4, an open-source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire improves features and performance for applications in machine learning, computer vision, signal processing, statistics, and finance.
This release focuses on 5 major components of the library that are common to many areas of mathematical, scientific, and financial computing: sparse matrix operations, random number generation, image processing, just-in-time (JIT) compilation, and visualisations. A complete list of ArrayFire v3.4 updates and new features can be found in the product release notes.
While we are happy for reaching this milestone, we are not complacent. Work on the next milestone has already commenced, and we hope to exceed the expectations of our community.
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.
What People Are Saying
Kent Knox, Senior Member of Technical Staff from AMD, says, ‘ArrayFire is a model example of how open sourcing scientific libraries should work. They have made their own code open to the public for review by the community at large, and they build upon existing open source math libraries to improve their own. With their investment in robust automation, they enhance the correctness and performance of the overall scientific ecosystem.’
Jason Ramapuram, a machine learning engineer, says, ‘ArrayFire has provided an elegant and simple solution for deploying GPU based machine learning applications. Being able to implement neural networks and auto-encoders without delving into the any CUDA/OpenCL/BLAS details has been immensely helpful for research purposes. All of this is bundled in a brilliant open source package with an amazingly helpful team that is very open to implementing and resolving any issues that arise.’
Visit ArrayFire’s website to download ArrayFire v3.4 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.
ArrayFire launched in 2007 to commercialize GPU and accelerated libraries for scientists, engineers, and financial analysts, building the first and best platform for productivity in GPU computing. With advanced language processing and runtime technology to transform CPU applications to high-performance GPU and accelerator codes, ArrayFire extends from desktop workstation performance to also fully leverage GPU and accelerator clusters. Based in Atlanta, Georgia, the privately held company markets ArrayFire for a range of defense, intelligence, biomedical, financial, research, and academic applications.