ArrayFire v2.0 Official Release

ScottAnnouncements, ArrayFire, CUDA, OpenCL 1 Comment

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

Aaron TaylorAnnouncements, ArrayFire, CUDA, OpenCL Leave a Comment

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 …

Torben’s Corner – A GPU Computing Gem for Jacket Programmers!

John MelonakosBenchmarks Leave a Comment

In January, we introduced you to Torben’s Corner – a resource wiki created and maintained by Jacket programming guru, Torben Larsen at Aalborg University in Denmark.  Many Jacket programmers have gained valuable insights from Torben’s Corner, including GPU performance charts, coding guidelines, special tricks. Since January, many wonderful additions have been added to Torben’s Corner.  We think you will find value in not only this new information but the entire resource.  Here is a quick summary of the most recent additions with links to the information: Benchmarking Update Torben’s Corner maintains a long list of benchmarks specifically detailing speedups of Jacket relative to standard MATLAB. This became an enormous task due to the sheer number of functions supported by Jacket …

Torben’s Corner

Gallagher PryorAnnouncements Leave a Comment

We work very closely with our customers and really appreciate the feedback we receive and value the insight provided.  One Jacket programmer has started to post fantastic content on the Jacket Documentation Wiki under Torben’s Corner. This content is maintained by Torben Larsen‘s team at AAU focusing primarily on outlining performance observations between GPUs and CPUs.  This information is not only of great value to our technical team but also valuable to the entire Jacket community.  Thanks Torben for this great resource!


John MelonakosAnnouncements Leave a Comment

In an effort to keep people up-to-date with Jacket-related stuff, we are pleased to launch this new blog.  This blog will serve a few purposes: it is a place for things that don’t really belong in the documentation, but still need a good explanation it is a place for announcements and updates Other sources of information include: The Jacket User Guide – Official Jacket documentation The Jacket Wiki – Online Jacket documentation The Jacket Forums – Online forums where users can post questions, bugs, experiences, feature requests, etc. We look forward to the launch of this blog and working with they community to make GPU MATLAB computing a valuable addition to your projects.