Update: While the post below discusses LibJacket (no longer a product), you can do the same thing in the newer, but different, ArrayFire library. Improved performance benchmarks and a simpler API are the results of moving from LibJacket to ArrayFire. Mcclanahoochie just posted some code and instructions for pairing OpenCV with LibJacket to get accelerated computer vision. You can do really fast image processing on video cam feeds too, see picture below: Really cool stuff. Computer vision is really hot with applications emerging in defense, radiology, games, automotive, and other consumer applications. Computer vision algorithms like these are also going mobile. For instance, we have started to build LibJacket for Mobile applications, which runs on Tegra, PowerVR, and other mobile …
New Product Updates – Jacket v1.8, LibJacket v1.1
Announcements Jacket v1.8 for MATLAB® now available LibJacket v1.1 for C/C++/Python/Fortran now available Request a FREE GPU computing consultation Introduction Enhance your code with the fastest, most comprehensive library for GPU computing: Jacket – the best GPU computing in MATLAB®. Take a tour and compare! LibJacket – the best way to kick start your CUDA development. Take a tour! Both products enable: Manipulating vectors, matrices, and ND arrays Support for single- and double-precision, boolean, real, and complex numbers Hundreds of routines for arithmetic, linear algebra, statistics, imaging, signal processing, and more (full list: Jacket, LibJacket) Thousands of lines of optimized code for any CUDA-capable GPU New Product Features Expanded support for the Signal Processing, Image Processing, and Statistics Libraries included with …
Jacket Lectures – Learn and Teach GPU computing
We are pleased to share 6 in-depth Jacket lectures, helpful both in learning and teaching Jacket. Download the lectures (PDF format), here: http://www.accelereyes.com/support/lectures Jacket is used in course instruction at many universities around the world. Professors and course instructors use Jacket to provide engineering students with GPU acceleration of MATLAB® algorithms and to bring HPC to MATLAB courses. The six lectures are entitled “Parallel High Performance Computing with Emphasis on Jacket Based GPU Computing” and have topics including: Parallel computing introduction Jacket introduction Basic programming with Jacket Advanced programming with Jacket Multiple GPU programming Benchmarking If you are looking at accelerating MATLAB code or parallel computing with MATLAB, you definitely will want to add these lectures to your arsenal of …
Getting More out of GPU Computing with LIBJACKET v1.0
LIBJACKET v1.0 is here! It is the Matrix Companion to CUDA, providing a high-productivity performance layer for GPU computing. Download now to start a free 15-day trial. It integrates seamlessly with any CUDA code, but can also be used to avoid writing complicated GPU kernels yourself via its matrix interface. Soak up its features, here. We’re celebrating this launch by offering two big promotions, one for existing Jacket programmers and one for the broader GPU computing community: Existing Jacket customers get 50% off libJacket. Buy a Tesla, Get a Free libJacket subscription. Learn more about these offers. Here are some other links of interest to this launch: Tour Documentation Function benchmarks Press release Over the years, we’ve been thrilled to …
Speeding Up Compressed Sensing Algorithms
Are you looking for ways to speed up compressed sensing? If you work in the areas of medical image reconstruction, image acquisition or sensor networks, you probably are. This paper, Parallel Implementation of Compressed Sensing Algorithm on CUDA-GPU, compares CPUs running Matlab and GPUs running Jacket using a Basis Pursuit Algorithm for compressed sensing. They compared an Intel Core 2 Duo T8100 (2.1GHz and 3.0 GB memory) running Matlab with a NVIDIA GeForce series 8400m GS (256 MB video memory, DDR2 and bus width of 64bit) using an older version of Jacket, Version 1.3. The CPU and GPU setups were used to run their Basis Pursuit Algorithm on six MRI images. These are some samples: The implementation using Jacket …
Our Point of View & Twitter Comedy
“Great businesses have a point of view, not just a product or service.” ~37 Signals At AccelerEyes, our point of view is that GPU software can and should deliver great results on real applications. With this point of view, we’ve kept our heads down solely focused on delivering a great runtime system for GPUs. All our energy has been devoted to the task of emitting optimized low-level code from high-level matrix notation. These efforts are now paying off in a big way! Jacket is consistently delivering awesome results in real applications, read examples here and here. Alternative choices apparently have a different point of view. Yesterday’s twitter stream contained a comical, but all-to-common indication of frustration with the recent GPU …
Improved Fat/Water Reconstruction Algorithm with Jacket
Case Western Reserve University researchers turned to GPUs running Jacket to develop a fast and robust Iterative Decomposition of water and fat with an Echo Asymmetry and Least-squares (IDEAL) reconstruction algorithm. The complete article can be found here. The authors report that “GPU usage is critical for the future of high resolution, small animal and human imaging” and Jacket “enables GPU computations in MATLAB.” Their research was performed on a desktop system with 32GB RAM, dual Intel Xeon X5450 3.0 GHz processors, an NVIDIA Quadro FX5800 (4GB RAM, 240 cores, 400 MHz clock), and MATLAB R2009a 64bit. Jacket v1.1, an older version, was used to produce these results. Reconstruction tests with different sized images were performed to evaluate computation times …
Hybrid GPU & Multicore Processing for LU Decomposition
One of the hot areas in supercomputing is hybrid compute: balancing the computational load between one or more CPUs and GPUs. Along these lines Nolan Davis and Daniel Redig at SAIC recently presented work on Hybrid GPU/Multicore Solutions for Large Linear Algebra Problems where they developed a novel algorithm for LU decomposition, one of the most important routines in linear algebra. Here’s a snapshot view of their setup: System Specs: GPU Nvidia® Tesla™ 2050 448 processing cores3 GB dedicated memory Multicore Host 24 cores64 GB system memory Red Hat® Enterprise Linux 5 Two AMD Opteron™ 6172 12-core processors Host-to-GPU Communications PCIE 2.0 16 channels at 500 MB/sec/laneTheoretical peak bandwidth of 8 GB/sec Their initial results are very promising. For …
CUDA over Remote Desktop now available for Tesla GPUs
Update: Jacket over Remote Desktop is now available for Quadro devices too! Read this post. Jacket over Remote Connections is also documented extensively on the AccelerEyes Wiki. Over the past several years, many Jacket programmers have requested support for Remote Desktop in Windows. We are pleased to report that recent NVIDIA drivers now enable Jacket to run over Remote Desktop, for some system configurations. Specifically, the requirements to make this work include: Windows Vista, Windows 7, Windows HPC Server 2008, or Windows HPC Server 2008 R2 The latest NVIDIA driver (as required by Jacket) Tesla GPU TCC-mode enabled on at least one (Tesla) GPU To enable TCC, the Tesla cannot be connected to a display. This means you need to …
Unraveling Speedups: Two Important Questions
One Jacket programmer recently emailed the following to us: Our chief scientists asked me a question that I’d like to pass on to you. I think I know the answer, but you guys can be much more definitive than I can. He recently read about people achieving ~10x speedups by converting parts of their code to MEX files. He was wondering how much of the observed speedup is due to that MEX and how much is due to CUDA and the GPU. Two Questions You Should Ask Yourself When contemplating an effort to optimize a piece of code, it is important to unravel the effort into two separate questions. Both need to be addressed to improve performance: How well-written is …