ArrayFire is a fast GPU library that off-loads compute intensive tasks onto many-core GPUs, thereby reducing application runtime and accelerating it many times. ArrayFire is built on top of NVIDIA CUDA software stack which is currently the best and most stable GPU Software Development Kit available for GPU-based computing. ArrayFire comes with a huge set of functions that span across various domains like image processing, signal processing, financial modeling, applications requiring graphics support. ArrayFire has an array based notation (supports N-dimensional arrays) and allows sub-referencing and assignment into these multi-dimensional arrays. The following code snippet shows how you can index into array objects. // Generate a 3×3 array of random numbers on the GPU array A = randu(3,3); array a1 …
GPU Computing with Jacket in Automated Trader
The Q1 2012 issue of Automated Trader contains an excellent “Mashup!” piece reviewing software for algorithmic trading. The article provides a wonderful glimpse into the 1-2 month adventure of Andy Webb, Automated Trader’s Founder, and Wrecking Crew building a fast trading platform from several technologies. We heartily recommend that those of you in financial computing go subscribe to get the full story and access to ongoing developments from these Automated Trader thought leaders! The full trading platform they built was quite extensive. The part that caught our eye was the core computational component of the pipeline. That component involved permuting 1,000 potential pairs with cointegration tests for 350 time windows on each potential pair. The single core MATLAB® version took 70 minutes …
Jacket Continues to Crush the Clone
This morning, I woke up to find the following comment in the MATLAB® Newsgroup: Over two years ago, MathWorks® started to build a clone of Jacket, which you now know as the GPU computing support in the Parallel Computing Toolbox (TM). At the time, there were many naysayers suggesting that Jacket would somehow be eclipsed by the clone. Made sense, right? Wrong! Here we are 2 years later and the clone is still a poor imitation. There are several technical reasons for this, but if you are serious about getting great performance from your GPU, Jacket is the better option. Look at all the real customers that are getting big benefit. Here are some other recent benchmarks from the Walking …
CUDA and OpenCL Benchmarks – Keeneland Workshop Day 1
Today was Day 1 of the Keeneland Workshop. Many great talks were given, across a broad range of GPU computing topics. With last week’s ArrayFire Webinar fresh in mind, it was interesting to see similar conclusions drawn in a presentation by Kyle Spafford of Oak Ridge National Laboratory. Kyle independently ran a number of benchmarks over a period of time which show how quickly OpenCL has matured and where it yet has room for improvement. The slide below comes from Kyle’s presentation. For numbers >1, CUDA is faster. For numbers <1, OpenCL is faster. Performance in most cases is close to equivalent. Just as we showed in the ArrayFire webinar, OpenCL performance is quite comparable with CUDA performance. The Achilles heel …
OpenCL vs CUDA Comparisons
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: …