Comparison of Jacket versus PCT for various domain-specific algorithms

Authors: H. Chafi, K. Sujeeth, K. Brown, H. Lee, A. Atreya, and K. Olukotun, Stanford University
Speedup: Up to 8.5X over MATLAB's Parallel Computing Toolbox™

Researchers in the Pervasive Parallelism Laboratory at Stanford University published work describing a novel framework for parallel computing with a paper entitled, "Domain-Specific Approach to Heterogeneous Parallelism." Part of their research involved benchmarking the performance of Jacket and the Parallel Computing Toolbox™. They compared performance for algorithms such as Gaussian Discriminant Analysis, Restricted Boltzmann Machines etc. and found Jacket to be faster in every case.

Accelerating LTE Simulation Using Jacket

Authors: Yuan Gao, Yin Sun, Chun Hui Zhou, Xin Su, Xi Bin Xu, Shi Dong Zhou, Tsinghua University
Speedup: 3X

Simulation in MATLAB is a driving force in several research projects. However, the accompanying long simulation times can tend to be a drag in many of these projects. In this article, we shall bring up the example of the work on 3GPP LTE System Simulation by Yuan Gao et al (from Tsinghua University, Beijing) and demonstrate how the use of Jacket can significantly improve the simulator performance and lead to faster validation times in simulation projects.

High Performance Compressive Sensing

Authors: Nabor Reyna and Wotao Yin from Rice University
Speedup: 5X

This work deals with reconstruction of signals using partial Fourier matrices (RecPF). The major computational components of the algorithm involve shrinkage and FFTs. Jacket is employed to accelerate this compute-heavy code.

Power Flow on the GPU with MATLAB

Authors: Indian Institute of Technology, Roorkee
Speedup: 35X

Power flow studies are one of the most important aspects of power system planning and operation. The power flow reveals the sinusoidal steady state characteristics of the entire system - voltages, real and reactive power generated, and absorbed and line losses- elucidating the voltage magnitudes and angles at each bus, the generation of each generating unit, and real and reactive power losses in the system. All this is necessary to ensure the security, economy, and control of electrical energy distribution. Learn how Jacket, GPUs, and MATLAB can deliver magnitudes of performance improvement over CPU-based solutions.

Using Jacket to design and simulate echo generators

Authors: A. Capozzoli, C. Curcio, A. Liseno at University of Naples Federico II
Speedup: 24X

Antenna array design involves repeated simulation to tune the many parameters involved, and waiting around for simulations to finish is no fun. Offloading the optimization problem onto the GPU cuts that time down significantly. In their recent paper, Capozzoli, Curcio, and Liseno of University of Naples Federico II demonstrated how a simple modification to their echo generator array simulation took advantage of the GPU to bring immediate speedups.

Laplace Transform Inversion on the GPU

Authors: Patrick Kano and Moysey Brio at Acunum Algorithms and Solutions
Speedup: 3.8X

The numerical inversion of the Laplace transform is a long standing problem due its implicit ill-posedness. Patrick Kano and Moysey Brio of Acunum Algorithms and Solutions, with their experience in computational methods and algorithm development, found a solution that not only works, but is very fast.

Compressed Sensing Algorithms

Authors: Kuldeep Yadav, Ankush Mittal, M.A. Ansar and Avi Srivastava, College of Engineering, Roorkee, India
Speedup: 8X

Compressed sensing is very critical in the areas of medical image reconstruction, image acquisition or sensor networks. An algorithm for compressed sensing developed using a Basis Pursuit Algorithm shows over 8X speedup when run on an NVIDIA GPU.

Improved Fat/Water Reconstruction Algorithm with Jacket

Authors: D. H. Johnson, S. Narayan, C. A. Flask and D. L. Wilson, Case Western Reserve University
Speedup:11.6X

Case Western Reserve University researchers turned to GPUs running Jacket to develop a fast and robust version of the "Iterative Decomposition of water and fat with an Echo Asymmetry and Least-squares" (IDEAL) reconstruction algorithm. This algorithm uses a lot of Image Processing algorithms for reconstruction, and was shown to achieve very high speedups.

The world of Quantitative finance is all about getting accurate results really, really fast. AccelerEyes is working with one of the largest banks in Spain to maximize their output using GPUs. Click the link below for an overview of the uses of GPU computing in finance.

Radar Image Formation

Authors: Gary Rubin and Earl Sager - System Planning Corporation
Speedup: ~45X

Radar imaging is computationally intensive. As a result, many imaging algorithms apply FFT-based approximations. While efficient, these algorithms sacrifice data fidelity for speed. Other algorithms better preserve information, but are often too slow for many applications. At System Planning Corporation (SPC) , we have implemented a Matlab SAR/ISAR imaging routine based on the Backprojection algorithm. Using Jacket, we have demonstrated speedups of roughly 45x for large datasets.

Radar Clutter Reduction

Authors: David Berger and Gary Rubin - System Planning Corporation
Speedup: 5 to 10x

System Planning Corporation (SPC) uses Jacket to accelerate radar processing algorithms. The system processes raw data from marine navigation radars using a variety of thresholding techniques to extract real targets from clutter. This involves highly data-parallel processing in which each radar pulse is subjected to the same computations; very few operations occur across multiple pulses. Using Jacket, SPC has achieved 10x speed improvements relative to a Core i7-920 CPU and 5x improvements relative to a realtime DSP implementation.

Novel Algorithms for LU Decomposition

Authors: Nolan Davis and Daniel Redig, SAIC
Speedup: 3.5X

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. They presented a Hybrid CPU/GPU computing approach, where problems too large to fit in GPU memory can also be solved faster than using only the CPU.

Geolocation

Authors: BAE Systems
Speedup: 17X

Geolocation is the identification of the real-world geographic location of a target of interest. In this application, the system receives the signal with an array of several antennas and computes the direction of arrival of the radio energy by measuring the time difference of arrival (or the phase difference) at the different antennas.

Tsunami Modeling

Authors: University of Minnesota, Boise State, Saint Scholastica , and NCAR
Speedup: 3 to 5X

Natural catastrophic disasters like tsunamis commonly strike with little warning. For most people, tsunamis are underrated as major hazards. People sometimes wrongly believe that they occur infrequently and only along distant coasts. Tsunamis are usually caused by earthquakes. Seismic signals can give some margin of warning since the speed of tsunami waves travels at 1/30 the speed of seismic waves. Still there is little time between the creation of the tsunami and its impact making fast processing critical to producing effective warning systems. Jacket was used to run an RBF simulation on the GPU with a time to solution not available by other alternatives. Using Jacket, the authors were able to access the GPU without leaving the MATLAB environment.

Pathology advances with GPUs

Authors: Laboratory for Spectral Diagnosis at Northeastern University
Speedup: 100X+

One element of the hyperspectral image analysis workflow that requires more than a traditional desktop workstation or personal computer is Hierarchical Cluster analysis (HCA). HCA requires a large amount of data space and substantial computation time (~11 hours) for typical datasets using a single processor personal computer. Rather than following the traditional approach of moving to a lower level programming language like C or C++ and complex parallel programming paradigms such as OpenMP or the Massage Passing Interface (MPI), the lab utilized graphics processing units, or GPUs, and the Jacket software platform. The solution allowed the lab to dramatically increase the performance of the analysis while substantially decreasing the amount of calendar time to reach the desired results.

Hepatitis C Virus - mutation modeling

Authors: CDC Research and Development Team
Speedup: ~20X

This case study provides a look at biological research regarding coordinated mutations of the Hepatitis C Virus (HCV). AccelerEyes provided collaborative R&D resources and greatly improved the speed of this HCV research with the use of parallelization, reducing the computing time from 40 days to less than 1 day. Most importantly, the conclusion of the case study illustrates the the relative price-performance of personal supercomputers that leverage GPUs and Jacket provides a compelling solution versus other architectures and approaches.

fMRI with SPM in Neuroimaging

Authors: Georgia Institute of Technology
Speedup: 3.5X

The Georgia Tech team explores the value of Jacket and GPUs for fMRI workflows within the popular SPM - Statistical Parametric Mapping software widely used in neuroscience research.

Medical Image Compression

Authors: Jaideep Singh, Ipseeta Aruni, R. Balasubramanian - IIT - Roorkee, India
Speedup: 38x

This study presents the acceleration of Haar wavelet-based image compression algorithm for medical imaging on the Graphics Processing Unit (GPU) using Jacket. Due to bandwidth and image size constraints of medical imaging systems, image compression plays a vital role in reducing the bit rate of transmission or storage. Wavelet-based image compression provides the most promising approach for high quality image compression.

Brain Displacement

Authors: Spencer Technologies
Speedup: 12X

Spencer describes how Jacket software facilitates the development of fast algorithms enabling observation of brain displacement across depth with sampling density that far surpasses previous benchmarks.

Multidimensional Scaling for Genomics

Authors: Leibniz Institute of Plant Genetics and Crop Plant Research
Speedup: 20 to 35X

Multidimensional scaling (MDS) is a general computing technique to turn a distance matrix into a set of reconstructed points with pair-wise relationships approximating the original distances by points located in a usually low-dimensional space. Jacket is used to enhance execution of the HiT-MDS procedure and delivers considerable performance improvement.

Drug Delivery Model

Authors: Georgia Institute of Technology
Speedup: 70X

In this work, MATLAB is used to simulate the delivery of a novel nanoparticle chemotherapy drug to cancerous tissue. Simulation allows scientists to predict experimental outcomes and thus reduce the cost of development and time to clinical relevance. The simulation model includes blood vessels, tumor cells, and healthy cells and an engine to calculate the spatial distributions of both drug and oxygen. Jacket is used to speed up the diffusion calculations for the drug and oxygen within the tissue.

Biomedical Infrared Spectroscopy

Authors: University of Manchester and Nofima Mat, Norway
Speedup: Hours of runtime reduction

The authors present an iterative algorithm that applies full Mie scattering theory and avoids noise accumulation in their iterative algorithm by integrating a curve-fitting step. Jacket along with NVIDIA GPUs are leveraged to reduce the time added by the curve-fitting step.

Feature Learning Architectures with GPU-acceleration

Authors: Andrew Ng, Stanford University
Speedup: Ability to process many images in parallel

Stanford researchers in Andrew Ng's group used GPUs and Jacket to speed up their work on Feature Learning Architectures. They decided 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.

Tomography of Vegetation - Filtered Back-Projection and Non-Uniform FFTs

Authors: Drs. Capozzoli, Curcio, di Vico, and Liseno, Universita di Napoli Federico II
Speedup: 10X

In order to investigate changes of forest biomass, scientists use microwave tomography to image the vegetation. At the smallest scale, individual plants can be imaged to investigate branching and growth, but even synthetic aperture radar can reveal large-scale changes in regional ecology. To the right, you can see the experimental setup to image an individual plant.

Action Recognition with Independent Subspace Analysis

Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University
Speedup: 4.4X

In a paper at this year's CVPR 2011, entitled "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis", the authors explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) network. An ISA is used for learning features from image patches without supervision.

Video Processing

Authors: Google and Stanford University
Speedup: 10 to 20X

Video content analysis is the basis for categorizing videos and enabling search by content. Growing interest in using sparse-coding methods to extract motion features in video in support of video content analysis led to the application of Jacket and GPUs to improve performance by substantially accelerating the solution of the L1-regularized least-squares optimization problem.

Action Recognition with Independent Subspace Analysis

Authors: Quoc Le, Will Zou, Serena Yeung, Andrew Ng, Stanford University
Speedup: 4.4X

In a paper at this year's CVPR 2011, entitled "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis", the authors explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) network. An ISA is used for learning features from image patches without supervision.

Music Beat Analysis

Authors: Vidhur Vohra - Georgia Tech
Speedup: 15X

Did you ever wonder how the music visualizer in your media player works? Watching it pulsate in synchrony with the beats of the song is almost as entertaining as listening to the song itself! Researchers have been attempting to detect beats in audio signals for many years, and there are many techniques available, from the simplest (and least accurate) to more complicated algorithms that are highly accurate. All algorithms, though, perform some form of signal processing and frequency analysis, applications highly suited to GPU Computing.

Feature Learning Architectures with GPU-acceleration

Authors: Andrew Ng, Stanford University
Speedup: Ability to process many images in parallel

Stanford researchers in Andrew Ng’s group used GPUs and Jacket to speed up their work on Feature Learning Architectures. They decided 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.

Digital Holography

Authors: Nitesh Pandey, Damien Kelly, Bryan Hennelly and Thomas Naughton from the National University of Ireland, Maynooth
Speedup:17X

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 large digital holograms can be computationally intensive to generate on CPUs, but GPUs running Jacket offer amazing possibilities.

3D Mantle Convection - Geodynamics

Authors: Boise State, University of Colorado, University of Minnesota
Speedup: 2.5 to 4.5X

The authors introduce a GPU implementation of a three-dimensional mantle convection modeling at a high Rayleigh number to the solid earth geophysics community. They outline code development time, compare performance of CPUs versus GPUs, and deliver powerful visualizations.

Lattice Boltzmann Models - Ground Water Simulations

Authors: Kevin R. Tubbs and Frank T-C. Tsai at Louisiana State University
Speedup: >20X

A lattice Boltzmann method for solving the shallow water equations and the advection-dispersion equation is developed and implemented on graphics processing unit (GPU)-based architectures. The proposed LBM is implemented to an NVIDIA Computing Processor in a single GPU workstation. GPU computing is performed using the Jacket GPU engine for MATLAB. Mass transport with velocity-dependent dispersion in shallow water flow is simulated by combining the MRT-LBM model and the TRT-LBM model. The GPU parallel performance increases as the grid size increases. The results indicate the promise of the GPU-accelerated LBM for modeling mass transport phenomena in shallow water flows.

Shallow Water Fluid Flow

Authors: Louisiana State University
Speedup: 10X

A lattice Boltzmann method (LBM) on high performance computing (HPC) environments for three-dimensional shallow water flow fields coupled to mass transport is developed. LBM is an attractive method for solving the multilayered shallow water equations because the extension to multilayer is straight forward with all of the simplicities and advantages of the LBM in mass transport in shallow water flows and the LBM performance on central processing unit (CPU)-based and graphics processing unit (GPU)-based HPC environments.