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 …
Machine Learning with ArrayFire
In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library and its applications to Machine Learning on June 15. This webinar was part of a free series of webinars that help you learn about ArrayFire and Jacket (our MATLAB® product). 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. Chris, a Software Engineer at AccelerEyes, explained ArrayFire’s position in the GPU computing world, and presented benchmarks where ArrayFire beats GPU libraries such as Thrust in many critical applications. He also mentioned that ArrayFire could be used either standalone, or in combination with other options for GPU computing such …
Option Pricing
Andrew Shin, Market Risk Manager of Koch Supply & Trading, achieves significant performance increases on option pricing algorithms using Jacket to accelerate his MATLAB® code with GPUs. Andrew says, “My buddy and I are, at best, novice programmers and we couldn’t imagine having to figure out how to code all this in CUDA.” But he found Jacket to be straight-forward. With these results, he says he can see Jacket and GPUs populating Koch’s mark-to-futures cube, which contains its assets, simulations, and simulated asset prices. Modern option pricing techniques are often considered among the most mathematically complex of all applied areas of finance. Andrew shared some exemplary code to demonstrate how much leverage you can get out of Jacket and GPUs for financial computing in MATLAB® and C/C++. …
Powering Mars Research
The Curiosity Mars rover landing reminded us of a recent talk by Brendan Babb of NASA and UAA in Anchorage about Jacket-accelerated Mars research. The talk was given at GTC 2012 in May. The main thrust of this research is improving mars rover image compression via GPUs and genetic algorithms. With Jacket and GPUs, the researchers were able to achieve 5X speedups on the larger data sizes. The algorithm works by pairing neighboring pixels with a random one and then adjusting the random pixel based on whether it incrementally improves the original image. Babb described the algorithm as an “embarrassingly” parallel process, ideally suited to GPU acceleration. He estimates he has been able to achieve a 20 to 30 percent error …
Parallelized Gene Predictors with Jacket
Researchers at the University of Quebec have developed high-performance gene predictors using Jacket to accelerated their MATLAB® code. This work has been published in BMC Research Notes and is freely available here. Computerized approaches to studying the human genome are challenged by the exploding amount of data, which doubles roughly every 6 months. In order to deal with this burgeoning datasets, demands for faster processing power continue to arise. This work focuses on predicting genes using frequency analysis with FFTs and with an equivalent technique known as Goertzel’s algorithm. In these applications, the emphasis of this paper is to propose tools to geneticists and molecular biologists for the prediction or identification of new genes using existing complementary strategies. The criteria for these …
ArrayFire for Defense and Intelligence Applications
In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library and its applications to Defense and Intelligence functions. Defense projects often have hard deadlines and definite speed targets, and ArrayFire is a fast and easy-to-use choice for these applications. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. John Melonakos, our CEO, introduced ArrayFire and talked about some exciting recent customer successes in the field of defense. He then ran through the mechanics of compiling and running code on a machine with 2 Quadro 6000 GPUs, and talked about customer success stories. …
ArrayFire for Financial Computing Applications
In case you missed it, we recently held a webinar on how to accelerate financial computing applications using Jacket. The performance advantages brought to financial computing algorithms through Jacket and GPUs represents the best way to accelerate MATLAB® code. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. Scott Blakeslee, our Director of Business Development, introduced Jacket and talked about some exciting recent customer successes in the field of financial computing. Gallagher Pryor, CTO of AccelerEyes, then demoed some financial code speedups on one of our office machines. The major takeaway from the webinar video was that Jacket is …
GPU Computing with Jacket in Automated Trader
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 …
ArrayFire for Medical Image Segmentation
In case you missed it, we recently held a webinar on how to accelerate common medical imaging applications using an easy, powerful programming library with Jacket for MATLAB®. This webinar was part of an ongoing series of webinars that will help you learn more about the many applications of Jacket and ArrayFire, while interacting with AccelerEyes GPU computing experts. Gallagher Pryor, CTO of AccelerEyes, used the Bayesian Image Segmentation algorithm as a simple use-case to show how easy it is to convert CPU code to GPU code with Jacket (only 4 lines of CPU code needed to be changed!). For those of you who missed it, we uploaded the webinar on Youtube. We hope to see you at the next one!
Optimization methods for deep learning
Researchers at SAIL (Stanford Artificial Intelligence Laboratory), have done it again. They have successfully used Jacket to speed up the training part of Deep Learning algorithms. In their paper titled “On Optimization Methods for Deep Learning”, they experiment with some of the well known training algorithms and demostrate their scalability across parallel architectures (GPUs as well as multi-machine networks). The algorithms include SGDs (Stochastic Gradient Descent) L-BFGS (Limited BFGS used for solving non-linear problems), CG (Conjugate Gradient). While SGDs are easy to implement, they require manual tuning. Add to that their sequential nature, they are hard to tune, scale and parallelize making them difficult to use with Deep Learning algorithms. L-BFGS and CG algorithms can be harder to implement and …