In case you missed it, we recently held an ArrayFire Webinar, focused on exploring the tradeoffs of OpenCL vs CUDA. This webinar is part of an ongoing series of webinars held each month to present new GPU software topics as well as programming techniques with Jacket and ArrayFire. For those of you who missed it, we provide a recap here. Lots of questions were fielded by our team, so it’s a must-watch. We hope to see you at the next one! Recap Download the slides. Here is a transcript of the content portion of the webinar: AccelerEyes is pleased to present today’s ArrayFire webinar looking at OpenCL and CUDA Trade-offs and Comparisons. Everyday, we interact with many programmers in various stages of GPU …
ArrayFire Support for CUDA 4.1
The question above comes from María (@turbonegra). She follows us @accelereyes. Many of you are wondering when ArrayFire support for new CUDA version 4.1 will be released. The answer: work is currently under way. CUDA 4.1 includes a new Fermi compiler, and many people in the GPU ecosystem have reported slowdowns from upgrading to the new CUDA version. So we’ve delayed releasing ArrayFire and Jacket support for CUDA 4.1 because we want to verify performance and reliability across all our unit tests, performance regressions, and customer code samples. Our tests sweep across various driver versions and everything from mobile GeForce cards through server-grade Tesla and Fermi chips. We are still working through the testing and verification at the moment. While …
ArrayFire for Medical Image Segmentation
In case you missed it, we recently held a webinar on how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. Gallagher Pryor, CTO of AccelerEyes, used the Bayesian Image Segmentation algorithm as a simple use-case to show how easy it is to convert CPU code to GPU code with Jacket (only 4 lines of CPU code needed to be changed!). For those of you who missed it, we uploaded the webinar on Youtube. We hope to see you at the next one!
Jacket over Remote Desktop for Tesla and Quadro GPUs
We recently reported that Jacket could be used over Windows Remote Desktop connections as long as you had an NVIDIA Tesla device in TCC mode. With the latest NVIDIA driver updates, Tesla and Quadro devices can be put into TCC mode, making it possible to use Jacket over Remote Desktop with both Tesla and Quadro devices. We have tested this out with the NVIDIA Quadro 4000 as well as Quadro 6000 GPUs. The system had a Tesla C2050 connected to the display, and the Quadro in TCC mode. Here’s the ginfo output: >> ginfo Jacket v2.0 (build 80c7ba4) by AccelerEyes (64-bit Windows) License Type: Designated Computer ([JACKET_ROOT]jacketenginejlicense.dat) Addons: MGL4, JMC, SDK, DLA, SLA CUDA toolkit 4.0, driver 285.62 GPU1 Quadro …
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 …
Jacket v2.0 Now Available
New Multi-GPU functionality , added support for OpenCL devices, and much more… AccelerEyes announces the release of Jacket version 2.0, adding GPU computing capabilities for use with MATLAB®. Version 2.0 delivers even more speed through a host of new improvements, maximizing GPU device performance and utilization. Notable new features include a multi-GPU interface and support for OpenCL devices. With Jacket v2.0, your M-code is now portable across all major GPU devices, including AMD/ATI, Intel, and NVIDIA chips. Jacket is the premier GPU software plugin for MATLAB®, better than alternative solutions. It is relied upon by thousands of organizations for rapid prototyping and problem solving across a range of government, manufacturing, energy, media, biomedical, financial, and scientific research applications. Multi-GPU Details: …
Jacket on Lenovo Systems
Lenovo and AccelerEyes have a joint solution for optimizing M code on Lenovo workstations. The combined HPC solution combines high Intel Xeon CPU performance for daily productivity with unprecedented NVIDIA graphics (GPU) performance for parallel computing with Jacket. Jacket’s comprehensive benchmark suite, when run on Lenovo ThinkStation systems, shows tremendous amounts of speedups for a wide variety of computationally-intensive applications. Jacket is the world’s fastest and broadest GPU software accelerating the M-language commonly found in MATLAB®. Thousands of customers around the world have used Jacket to accelerate their MATLAB code. Lenovo ThinkStation systems are ideally suited for running real-world high-performance applications using Jacket. While the high-end CPUs are ideal for daily productivity tasks, Jacket and the Quadro GPUs perform HPC …
AccelerEyes Releases ArrayFire GPU Software
A free, fast, and simple GPU library for CUDA and OpenCL devices. AccelerEyes announces the launch of ArrayFire, a freely-available GPU software library supporting CUDA and OpenCL devices. ArrayFire supports C, C++, Fortran, and Python languages on AMD, Intel, and NVIDIA hardware. Learn more by visiting the ArrayFire product page. “ArrayFire is our best software yet and anyone considering GPU computing can benefit,” says James Malcolm, VP Engineering at AccelerEyes. “It is fast, simple, GPU-vendor neutral, full of functions, and free for most users.” Thousands of paying customers currently enjoy AccelerEyes’ GPU software products. With ArrayFire, everyone developing software for GPUs has an opportunity to enjoy these benefits without the upfront expense of a developer license. Reasons to use ArrayFire: …
AccelerEyes Webinar Series
AccelerEyes invites you to participate in series of webinars designed to help you learn more about Jacket for MATLAB® and LibJacket for C/C++/Fortran/Python, a comprehensive library of GPU-accelerated functions. Joint Webinar With NVIDIA: LibJacket CUDA Library On October 20th we co-hosted a joint webinar with NVIDIA. During this well-attended event, our GPU computing experts provided a general product overview and usage of the LibJacket CUDA library. Several impressive demos of LibJacket in action were provided as well. LibJacket supports hundreds of GPU computing functions and programmers in numerous industries have been able to speedup applications. Be sure to check out the Q&A session included in the recorded webinar posted on NVIDIA’s Developer Zone. Thanks again to NVIDIA for co-hosting this informative webinar! GPU Programming for …
Filtering Benchmarks – OpenCV GPU vs LibJacket
OpenCV is one of the most popular computer vision toolkits, and over the last year they’ve been integrating more GPU processing into the core. One of the most common image processing tasks is convolution. Since LibJacket and OpenCV both support this, one of my coworkers rolled up his sleeves and benchmarked the latest versions from both libraries: OpenCV/CPU, OpenCV/GPU, LibJacket. Jump over to his personal website for the full benchmark results and source code. From the graphs, the GPU implementations from OpenCV and LibJacket both easily outperform the default CPU version in OpenCV, but notice that LibJacket pushes performance even further and dominates OpenCV’s GPU implementation, especially when using separable filters. We’ve worked really hard the last few years to …