Getting Started with ArrayFire – a 30-minute Jump Start

ArrayFireArrayFire, C/C++, CUDA, OpenCL 1 Comment

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

John MelonakosAnnouncements 4 Comments

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 …

Jacket v2.3 Now Available

John MelonakosAnnouncements, CUDA 1 Comment

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 …

Genomics Applications on the GPU

ArrayFireBenchmarks, Case Studies, CUDA Leave a Comment

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 …

SAR Image Formation Algorithms on the GPU

ArrayFireArrayFire, Case Studies, CUDA 1 Comment

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 …

Hiring Tons

John MelonakosAnnouncements Leave a Comment

Join the hottest GPU software company. We’re rapidly expanding and looking for talented developers who are passionate about making the programming world more efficient. The things we work on at AccelerEyes provide orders of magnitude more productivity for other developers, greatly increasing the amount of science, engineering, and analytics which are produced each year, across the globe, and across every technical computing industry. Specifically, we are looking to hire many developers in the following two roles: Application Engineering – the most vital job. It requires an ability to produce applications in a variety of disciplines, such as healthcare, finance, oil & gas, defense, etc). You will be the most expert users of ArrayFire and Jacket, and will spread your understanding …

Top 10 List at GTC 2012

John MelonakosAnnouncements, Events Leave a Comment

It’s going to be hard to sleep tonight.  So much GPU goodness awaits the coming 3 days of the GPU Technology Conference.  Here are my top 10 things to do at GTC 2012: Sessions to Attend #1:  S0287 – Jacket for Multidimensional Scaling in Genomics – This is a great opportunity to learn about accelerating MATLAB® on the GPU.  Come learn why thousands of scientists, engineers, and analysts are using Jacket to do more with less coding hassle. (Day: Tuesday, 05/15; Time: 5:30 pm – 5:55 pm; Location: Room K) #2:  S0415 – An Accelerated Weeks Method for Numerical Laplace Transform Inversion – Learn how the researchers have been able to utilize Jacket in MATLAB® to more efficiently and robustly implement the Weeks method. (Day: Wednesday, 05/16; Time: 9:30 …

GPU Computing with Jacket in Automated Trader

John MelonakosBenchmarks, Case Studies Leave a Comment

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 …

Tree cats see your code!

John MelonakosArrayFire 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 …

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

John MelonakosAnnouncements, 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 …