Today we are pleased to announce the release of ArrayFire v3.5, our 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, finance, and more. This release focuses on thread-safety, support for simple sparse-dense arithmetic operations, canny edge detector function, and a genetic algorithm example. A complete list of ArrayFire v3.5 updates and new features are found in the product Release Notes. Thread Safety ArrayFire now supports threading programming models. This is not intended to improve the performance since most of the parallelism is happening on the device, but it does allow you to use multiple devices in …
ArrayFire at SC16
SC16 is almost here! We’re getting excited to heading to Salt Lake City, Utah, to be a part of this excellent conference. It’s a great place for soaking up HPC knowledge, getting inspired, and connecting with the brightest minds in the industry. Here’s a quick run-down of where we’ll be. Visit our booth. We’re booth #717 in the exhibit hall during exhibit hours November 15 – 17. We’ll be showing off our latest demos and our engineers will be available for questions. Ask your questions, meet the team, or just bounce some ideas. Try our in-booth tutorials. Want to learn how to use ArrayFire to accelerate your code? Stop by and receive an in-booth tutorial from one of our ArrayFire experts. We’ll show …
ArrayFire v3.0 is here!
Today we are pleased to announce the release of ArrayFire v3.0. This new version features major changes to ArrayFire’s visualization library, a new CPU backend, and dense linear algebra for OpenCL devices. It also includes improvements across the board for ArrayFire’s OpenCL backend. A complete list ArrayFire v3.0 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, 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 …
ArrayFire on Tegra K1
We’re pleased to announce the arrival of ArrayFire for NVIDIA Tegra K1! This version of ArrayFire comes with all the capabilities and features of our standard version of ArrayFire. It includes all ArrayFire CUDA functionality—with the exception of linear algebra support—as well as fully operational graphics support. ArrayFire for Tegra currently works with Tegra K1 processors running Linux for Tegra. We invite and encourage you to test it out on your boards and give us feedback; any bug fixes or performance improvements will be promptly resolved, as this is a separate branch of ArrayFire. If you’d like to deploy ArrayFire on Android, feel free to contact us for further support. We are open to partnering with anyone wishing to deploy ArrayFire in other …
Joint Webinar with AMD: An Introduction to OpenCL Libraries
Back by popular demand! You’re invited to join us for a second webinar held jointly with AMD to discuss productive OpenCL Programming – An Introduction to OpenCL Libraries. We had so many people attend the first one, we decided to hold a second webinar! The webinar will be held on Monday, May 19 at 9 am PT / 11 am CT / 12 pm ET. Join ArrayFire COO Oded Green as he demonstrates best practices to help you quickly get started with OpenCL programming. Learn how to get the best performance from AMD hardware in various programming languages using the ArrayFire library. Oded will discuss the latest advancements in the OpenCL ecosystem, including cutting edge OpenCL libraries such as clBLAS, clFFT, clMAGMA and ArrayFire. …
ArrayFire v2.1 Official Release
It’s that time again—we’re pleased to announce the release of our newest version of ArrayFire: ArrayFire v2.1. ArrayFire v2.1 is now bigger, faster, and stronger, thanks to some key function additions, API changes, feature improvements, and bug fixes. ArrayFire is a CUDA and OpenCL library 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. Major Updates Support for CUDA 6.0 Support for Mac OS X New language support (available on github) ArrayFire for Java ArrayFire for R! ArrayFire for Fortran* ArrayFire Extras on Github All language wrappers …
ArrayFire v2.0 Official Release
We are thrilled to announce the official release ArrayFire v2.0, our biggest and best product ever! ArrayFire v2.0 adds full commercial support for OpenCL devices including all AMD APUs and AMD FireProTM graphics, CUDA GPUs from NVIDIA, and other OpenCL devices from Imagination, Freescale, ARM, Intel, and Apple. ArrayFire is a CUDA and OpenCL library 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. Announcing ArrayFire for OpenCL Support for all of ArrayFire’s function library (with a few exceptions) Same API as ArrayFire for CUDA enabling seamless interoperability Just-In-Time (JIT) compilation of …
ArrayFire v2.0 Release Candidate Now Available for Download
ArrayFire v2.0 is now available for download. The second iteration of our free, fast, and simple GPU library now supports both CUDA and OpenCL devices. Major Updates ArrayFire now works on OpenCL enabled devices New and improved documentation Optimized for new GPUs–NVIDIA Kepler (K20) and AMD Tahiti (7970) New in ArrayFire OpenCL Same APIs as ArrayFire CUDA version Supports both Linux and Windows Just In Time Compilation (JIT) of kernels Parallel for: gfor Accelerated algorithms in the following domains Image Processing Signal Processing Data Analysis and Statistics Visualization And more New in ArrayFire CUDA New Signal and Image processing functions Faster transpose and matrix multiplication Better debugging support for GDB and Visual Studio Bug fixes to make overall experience better For a more complete list of the …
Jacket v2.1 Now Available
Optimization Library, Sparse Functionality, Graphics Library Improvements, CUDA 4.1 Enhancements, and much more… AccelerEyes announces the release of Jacket v2.1, adding GPU computing capabilities for use with MATLAB®. Jacket v2.1 delivers even more speed through a host of new improvements, maximizing GPU device performance and utilization.. Notable new features include an Optimization Library and additional functions to our Graphics Library. With Jacket v2.1, we have also extended support for sparse matrix subscripting and made improvements to host-to-device and device-to-host data transfer speeds for complex data. In addition, we have included various GFOR enhancements. Jacket v2.1 now includes NVIDIA CUDA 4.1 enhancements to provide improved functionality and performance (requires latest drivers). Jacket is the premier GPU software plugin for MATLAB®, better than alternative …
AccelerEyes Webinar Series
AccelerEyes invites you to participate in series of webinars designed to help you learn more about Jacket for MATLAB® and ArrayFire for C/C++/Fortran/Python, a comprehensive library of GPU-accelerated functions. GPU Programming for Medical Image Segmentation: January 18, 2012 at 3:00 p.m. EST There’s a huge volume of data generated using acquisition modalities like computer tomography (CT), magnetic resonance imaging (MRI), positron emission tomography or nuclear medicine. A common need is to manipulate and transmit this data using compression techniques in as little time as possible. During this webinar we will show Jacket’s superior speed and handling volumes from subscripting to convolutions. Come and learn how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®. OpenCL and CUDA Trade-Offs and Comparison: February 15, 2012 at …
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