In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of ArrayFire, while interacting with AccelerEyes GPU computing experts. ArrayFire is the world’s most comprehensive GPU software library. In this webinar, James Malcolm, who has built many of ArrayFire’s core components, walked us through the basic principles and syntax for ArrayFire. He also provided an overview of existing efforts in GPU software, and compared them to the extensive capabilities of ArrayFire. For example, the same application that takes 26 lines to write in Thrust, can be coded up in just 3 lines in ArrayFire! ArrayFire has supported …
Exciting Updates from AccelerEyes
We are pleased to announce today that MathWorks and AccelerEyes have started working together to provide the best overall solution for GPU computing in MATLAB® through the Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™ from MathWorks. This new relationship will result in great product updates for end users of the Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™. Since 2007, AccelerEyes has been a leader in developing GPU software, including Jacket. AccelerEyes has sold Jacket as a 3rd-party add-on to the MathWorks MATLAB® product. Effective today, AccelerEyes will discontinue new Jacket product sales. All existing Jacket license holders will continue to receive support and maintenance from AccelerEyes for 1 year. All existing Jacket licenses are perpetual and will not expire. Future GPU computing updates …
CUDA GPUs Boost Mars Research
With the recent news release from NASA about the Mars Curiosity rover, and as a continuation of our previous post “Powering Mars Research”, Brendan Babb is here again to provide us with an exciting look into Jacket’s role in Mars research from the Curiosity rover . Brendan Babb and colleague Frank Moore, at the University of Alaska in Anchorage, work with NASA’s Jet Propulsion Lab to improve image quality and image compression of the Mars Rover images. Here is what Brendan had to tell us about the use of Jacket in his GPU computing challenges… Brendan Babb: I was thrilled to watch the new Mars Rover Curiosity successful landing with my visiting nieces and nephews. The new rover will take pictures, …
Jacket v2.3 Now Available
We are pleased to announce the new release of Jacket v2.3. This new version of Jacket brings even greater performance improvements through GPU computing for MATLAB® codes. (Click here to download v2.3) With v2.3, new support has been added for CUDA 5.0. This newer version of CUDA enables computation on the latest Kepler K20 GPUs of the NVIDIA Tesla product line. This morning we received an email from a Jacket user who said, “V2.3 + CUDA 5 = wow. Just upgraded and re-ran one of the routines that previously took just under 4 minutes – now less than 2 minutes!” This is a must-have release for all Jacket users. The performance improvements are generally felt across the board. Existing Jacket …
Fast Computation of Isotropic Gradients with Jacket’s Convolutions
Researchers from the École Polytechnique de Montréal showed that Jacket is very efficient to rapidly calculate 2D or 3D isotropic gradients in MATLAB® code. From a mathematical point of view, the isotropic gradients are characterized by their very precise orientation compared to the standard 1D finite difference discretizations. Using convolution functions developed by AccelerEyes, the method becomes very simple to apply and provides a very fast evaluation of isotropic gradients of functions or images. This type of isotropic discretization currently has an application in computational fluid dynamics. They are useful for simulating immiscible multiphase flows using the Lattice Boltzmann Method (LBM), where the orientation of the various fluid interfaces has to be computed very frequently and precisely. In multiphase flow …
Genomics Applications on the GPU
Recently, AccelerEyes held a free webinar that dealt with accelerating genomics MATLAB applications on the GPU. We recently added new genomics examples to Jacket, and wanted to use this webinar to showcase these examples and run through some code. This was part of the free series of AccelerEyes webinars that provide a great opportunity for you to interact with AccelerEyes engineers, see demos executing live on GPUs, and learn about AccelerEyes products and services. Over the course of the last decade, GPUs have continued to advance at a large pace, and are leaving CPUs behind in some ways, specifically in terms of their ability to perform massively parallel computations. Jacket is proven to be very efficient at harnessing this ability …
Image Processing with ArrayFire and OpenCV on the GPU
ArrayFire is a great way to supplement OpenCV for faster processing on the GPU. Mcclanahoochie recently posted an interactive demo showing the use of OpenCV with ArrayFire for computing Local Contrast Enhancement on the GPU from webcam video. Mcclanahoochie also shows how easy it is to convert OpenCV Mat images into ArrayFire GPU array images, as seen in the code snippit below: All the source code is available on Google Code, linked to from his website. Simply download ArrayFire and OpenCV and try it out for yourself!
Time delay estimation algorithms with Jacket
Time delay estimation (TDE) techniques have many diverse signal processing applications: for instance, in such fields as radar, sonar, seismology, geophysics, and ultrasonics for identifying and localizing radiating sources. In this case study, we evaluate the performance of two algorithms developed by Markus Nentwig to find delay and scaling factor between two cyclic signals. The first algorithm uses linear least-squares fitting to estimate the delay. The second algorthm is iterative and relies on FFT-based cross-correlation. A MATLAB® implementation of both approaches can be found in Algorithm 1 and Algorithm 2, respectively. As the author pointed out, the algorithms are not suited for real-time applications since the whole signal needs to be known in advance. However, they can be very useful …
SAR Image Formation Algorithms on the GPU
Since the 1950s Synthetic aperture radar (SAR) systems have gained extreme popularity in both civilian and military domains due to their all-weather, day-or-night capabilities as well as the ability to render different views of a “target”. However, the raw SAR data (phase-history data) must be preprocessed since all point targets at each pulse instance are superimposed and create a complex interference that is not very useful for target location. SAR image formation algorithms compress this target information in range (frequency) and along-track (azimuth) directions to obtain interpretable images. In the paper titled “SAR image formation toolbox for MATLAB®“, Gorham L.A. and Moore L.J. of the Air Force Research Lab discuss the implementation of the matched filter and backprojection image formation …
Optics Applications with ArrayFire
In case you missed it, we recently held a webinar on the Jacket GPU Computing Engine for MATLAB® and its applications to Optics and Photonics on Aug 1. From beam propagation methods to lens design, optics engineers are enjoying the benefit of GPU computing with Jacket to accelerate MATLAB® codes. This was part of a free series of webinars that help you learn about ArrayFire (for C/C++/Fortran/Python) and Jacket (for use with MATLAB®). Anyone can attend these webinars, for they are absolutely free and open for anyone to attend and interact with AccelerEyes engineers. Learn more at http://www.accelereyes.com/webinars. Jacket allows you to envision really fast applications for GPU computing, and the team at AccelerEyes recently helped Northrop Grumman Corporation achieve …