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 …
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 …
Digital Holograms Faster than Ever
REAL3D is a digital holography project funded by the EU and brings together nine participants from academia and industry under the FP7. As part of the project Nitesh Pandey, Damien Kelly, Bryan Hennelly and Thomas Naughton from the National University of Ireland, Maynooth demonstrate utilizing pre-computation and quantization of chirp matrices with GPUs running Jacket from AccelerEyes speeds up the reconstructions of digital holograms. Digital holography is a powerful imaging technique with many new applications like true 3D display. It allows the capture of both amplitude and phase information of the light reflected off the surface of 3D objects. Researchers at the National University of Ireland, Maynooth are developing techniques based on digital holography for 3D display applications. Reconstruction of …
Feature Learning Architectures with GPU-acceleration
Stanford researchers in Andrew Ng’s group used GPUs and Jacket to speed up their work on Feature Learning Architectures. They wanted to know why certain feature learning architectures with random, untrained weights perform so well on object recognition tasks. The complete write up can be found in On random weights and unsupervised feature learning in ICML 2011. They decide to use GPUs and Jacket for this study because of “the need to quickly evaluate many architectures on thousands of images.” Jacket taps into the immense computing power of GPUs and speeds up research utilizing many images. This is the architecture used in the study: They started by studying the basis of good performance for systems and found convolutional pooling …
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 …