Jacket v2.0 Now Available

Scott Announcements, OpenCL Leave a Comment

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

Scott Announcements, Benchmarks 1 Comment

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 Webinar Series

Scott Announcements, CUDA, Events, OpenCL Leave a Comment

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 …

Jacket Demo – CPU vs GPU runtimes on MATLAB® code

John Melonakos Benchmarks, CUDA 1 Comment

To explore the differences between CPU-only computing and GPU-accelerated computing, the new Jacket Demo is really convenient.  The Jacket Demo automatically launches two MATLAB® sessions, one running on the CPU-only and the other running on the GPU with Jacket. This side-by-side demo shows the computational speed of each processor as well as a visual depiction of the algorithm’s progression.  A variety of different demos are provided. The Jacket Demo is included in every Jacket installation (found in the examples directory and launchable from the Start Menu in Windows). Checkout this video of the Jacket Demo in action on an i7 CPU with a Tesla C2050 GPU.  Enjoy!

Action Recognition with Independent Subspace Analysis

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Researchers at the Stanford Artificial Intelligence Laboratory (SAIL) have had more success (building on previous work) using Jacket to speed up their algorithm. In a paper at this year’s CVPR 2011, entitled “Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis”, they explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. KTH Hollywood2 UCF Youtube Best published Results 92.1% 50.9% 85.6% 71.2% Stanford group Results 93.9% 53.3% 86.5% 75.8% Testing their algorithm on four well-known benchmark datasets, they were able to achieve better performance than existing results that have been published so far. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) …

Filtered Back-Projection and Non-Uniform FFTs

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In order to investigate changes of forest biomass, scientists use microwave tomography to image the vegetation. At the smallest scale, individual plants can be imaged to investigate branching and growth, but even synthetic aperture radar can reveal large-scale changes in regional ecology. To the right, you can see the experimental setup to image an individual plant. Filtered back-projection is at the core of all of these techniques: using the inverse Radon transform to reconstruct regular images from Fourier samples. Below you can see the final reconstructed image. Since these samples are often not on a uniform Cartesian grid, the non-uniform version of the FFT comes into play (NUFFT), and all of this requires some serious number crunching: bring in the …

Music Beat Analysis

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Did you ever wonder how the music visualizer in your media player works? Watching it pulsate in synchrony with the beats of the song is almost as entertaining as listening to the song itself! Researchers have been attempting to detect beats in audio signals for many years, and there are many techniques available, from the simplest (and least accurate) to more complicated algorithms that are highly accurate. All algorithms, though, perform some form of signal processing and frequency analysis, applications highly suited to GPU Computing. The beat visualizer described here was developed by researchers at Rice University, and is simple and fast. An incoming signal is broken down into six frequency bands for analysis. After smoothing out these bands and …

Tree cats see your code!

John Melonakos ArrayFire Leave a Comment

From time-to-time we stumble across funny quirks while using MATLAB®.  The latest came as one of our developers accidentally mis-keyed a few characters.  With 5 characters on the command line, you too can get a message about tree cats seeing your bad code (followed by a nasty seg fault, so beware).  Try this: >> a()@a tree_cat sees bad code * Subsref [4] * M_ID 0(5) which * M_LRB 5(1) * ExprList [1] * M_ID 6(1) e * M_RRB 7(1) tree_cat sees bad code * Subsref [4] * M_ID 0(5) which * M_LRB 5(1) * ExprList [1] * M_ID 6(1) e * M_RRB 7(1) Top Secret:  Part of Jacket’s GPU runtime involves monkeys obtaining bananas for optimal performance. While we can’t …

High Performance Compressive Sensing

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A few weeks ago, we published a blog entry that demonstrated the ability of Jacket to speed up “compressive sensing”, a technology that has wide applications in areas such as Image processing, reconstruction and spectroscopy. Here, we discuss the work of Nabor Reyna Jr. and Wotao Yin from Rice University using Jacket to speed up “compressive sensing” algorithms in reconstruction. This work deals with reconstruction of signals using partial Fourier matrices (RecPF).  The major computational components of the algorithm involve shrinkage and FFTs.  Jacket is employed to accelerate this compute-heavy code, and the resultant version (gRecPF) was about 5x faster! To reduce the cost involved in generating the random matrices involved in the above method, a second method (RecPC) that …

New Product Updates – Jacket v1.8, LibJacket v1.1

John Melonakos Announcements, CUDA Leave a Comment

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 …