Researchers in the Pervasive Parallelism Laboratory at Stanford University recently published work describing a novel framework for parallel computing with a paper entitled, “A Domain-Specific Approach to Heterogeneous Parallelism.” As part of their research, they compared Jacket to the GPU support in the Parallel Computing Toolbox™. The results clearly show that Jacket’s optimizations make a big difference in performance. In this blog post, we highlight 4 algorithms included in their research: NAME DESCRIPTION INPUT Gaussian Discriminant Analysis (GDA) Generative learning algorithm for modeling the probability distribution of a set of data as a multivariate Gaussian 1,200×1,024 Matrix Restricted Boltzmann Machine (RBM) Stochastic recurrent neural network, without connections between hidden units 2,000 Hidden Units 2,000 Dimensions Support Vector Machine (SVM) Optimal …
LIBJACKET on Amazon EC2 GPU Cloud Instances
Amazon recently added GPUs to their Elastic Compute Cloud. We decided to throw LIBJACKET into this GPU cloud to see how it would fare. The $2/hr pay-on-demand pricing is a great option for many Jacket programmers. This post is full of screenshots detailing the steps we took to get going with GPU computing in Amazon’s cloud: Sign up with Amazon EC2 Launch a GPU instance Login to the instance using ssh Setup the environment Download, build, and test LIBJACKET! Everything in this post applies equally well to running Jacket for MATLAB® on EC2. Simply install MATLAB + Jacket in your Amazon GPU instance and start working over ssh.
GPU accelerated lattice Boltzmann model for shallow water flow and mass transport
Dr. Kevin Tubbs and Professor Tsai at Louisiana State University recently published an interesting paper using GPUs and Jacket to accelerate lattice Boltzmann models for shallow water flow and mass transport. More details about this work are provided in the full success story page on the website. Jacket makes GPU programming easy. “Very little recoding was needed to promote the LBM code to run on the GPU,” say the authors at one point in their paper. In this blog post, we share the highlights of this work. Using these methods, the authors are able to simulate shallow water flow and mass transport. For instance, checkout these videos of a dam break: The authors completed this work with a relatively older …
Computer Vision Demos at SC’10 with 8-GPU Colfax CXT8000
We just returned from SC’10, the biggest supercomputing show of the year. At the show, we demoed Jacket driving computer vision demos on an 8-GPU Colfax CXT8000 system… pure eye candy! We had CPU and GPU versions of the demos running on 8 different monitors, each attached to the 8 Tesla C2050 GPUs in the system. Input data for the various demos was sourced from 3 webcams and 2 Blu-ray video inputs. Checkout the demo details, below: Demo 1 Sobel edge detection with image dilation and interpolation overlaid on Blu-ray video in realtime. Demo 2 Feature detection on a 4-level pyramid of 640×480 realtime webcam video. Demo 3 Gradient descent feature tracking , a stripped down version of KLT, tracking …
Beam Propagation Methods – Jacket is 3.5X faster than the CPU and 2X faster than PCT
A couple weeks ago, a GPU-enabled code appeared on MATLAB Central entitled, “A CUDA accelerated Beam Propagation Method [BPM] Solver using the Parallel Computing Toolbox.” In this post, we share a video which showcases how Jacket is much better than PCT at GPU computing, by analyzing performance on this Beam Propagation Method code. To reproduce these results, download the source code here: CUDA_BPM_NOV_04_2010_AccelerEyes These benchmarks were run on an NVIDIA Tesla C2070 GPU versus a quad-core Intel CPU. MATLAB + PCT R2010B were used for the PCT-GPU experiments. MATLAB + Jacket 1.6 (prerelease) were used for the Jacket-GPU experiments. Take Home Message Due to Jacket’s extensive library of GPU functions and its optimized GPU runtime, it performs 3.5X faster than …
Speeding up critical code
With Jacket 1.5, we released a big new feature: GCOMPILE. This allows you to convert critical sections of your MATLAB code directly into GPU kernels to further increase speed. In an earlier post we introduced the prototype and have been working with several beta users over the past month to get it ready. In this post, we’ll give some more details and start to look at the speedups you can quickly and easily achieve. You can find more information about it on the wiki. Some of the best features of GCOMPILE are the ability to use IF statements, WHILE loops, and FOR loops in your code now. Make sure to check out the wiki pages about these and the other …
A Jacket built for Speed
Just a few months ago, Jacket 1.4 was released sporting an improved MTIMES routine that brought about radical improvements to Jacket’s matrix multiplication. The quest for performance never ends though. Now, in the release of Jacket 1.5, MTIMES is even faster than before for SGEMM routines. Checkout the MTIMES Benchmarks wiki for more information. I you are attending GTC, you may want to attend this session also!
Torben’s Corner – A GPU Computing Gem for Jacket Programmers!
In January, we introduced you to Torben’s Corner – a resource wiki created and maintained by Jacket programming guru, Torben Larsen at Aalborg University in Denmark. Many Jacket programmers have gained valuable insights from Torben’s Corner, including GPU performance charts, coding guidelines, special tricks. Since January, many wonderful additions have been added to Torben’s Corner. We think you will find value in not only this new information but the entire resource. Here is a quick summary of the most recent additions with links to the information: Benchmarking Update Torben’s Corner maintains a long list of benchmarks specifically detailing speedups of Jacket relative to standard MATLAB. This became an enormous task due to the sheer number of functions supported by Jacket …
GPU Giddy – Excitement Building for GTC
GTC is coming up… The GPU Technology Conference (GTC) starts later this month and is sure to generate a new level of excitement and energy around GPU computing. The conference includes over 250 technology sessions presented by industry, government, and academic technology leaders. AccelerEyes is pleased to be well represented at this year’s conference by our technical leadership and a number of our customers. If you plan to attend the conference be sure to include the sessions outlined below on your agenda. In addition to being well represented, we are also flattered to see that others in the market have recognized that GPU Computing with MATLAB delivers clear productivity gains and that the performance improvements made possible by GPUs is …
Tesla C2050 versus C1060 on Real MATLAB Applications
Following our recent Jacket v1.4 Fermi architecture release, many of you requested data comparing the new NVIDIA Fermi-based Tesla C2050 versus the older Tesla C1060. Over the years, AccelerEyes has developed an extensive suite of benchmark MATLAB applications, which are included in every Jacket installation. Using this suite of tests, we compared performance of the C2050 vs C1060 and are pleased to report the results here. We hope this information will be useful to Jacket programmers. All tests were run on the same standard workstation with Jacket 1.4. The only thing that changed was the actual GPU board. In every case the C2050 beat the C1060. Double-precision examples on the Fermi-based board outperformed the older board by 50% in every …