GPU-enhanced Neuroscience Software Tools

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$1,498,803.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
2R44MH088087-02
Award Id:
n/a
Agency Tracking Number:
R44MH088087
Solicitation Year:
2012
Solicitation Topic Code:
NIMH
Solicitation Number:
PA11-133
Small Business Information
75 5th St NW, ATLANTA, GA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
827568226
Principal Investigator:
JOHN MELONAKOS
(770) 315-1099
john.melonakos@accelereyes.com
Business Contact:
JOHN MELONAKOS
(770) 315-1099
john.melonakos@accelereyes.com
Research Institution:
Stub




Abstract
DESCRIPTION (provided by applicant): This application is to deliver high-performance, GPU-enabled computation and visualization software tools to neuroscientists. Today, there are an estimated 1.5 million life science MATLAB users, with a substantial portion of those using MATLAB to solve neuroscience-related problems. MATLAB users, especially those dealing with large neuroscience datasets, such as brain MRI, fMRI, DW-MRI, PET, and CT image volumes, microscopy imagery, and genomics datasets, currently havetwo major problems in using MATLAB to conduct neuroscience research: 1) MATLAB is slow when compared to other programming languages such as C/C++, and 2) MATLAB visualizations are unable to handle large amounts of data or to render 3D models of anatomicalstructures with ease. Therefore, neuroscientists often undertake costly and time-consuming efforts to port neuroscience MATLAB code to C/C++, at the expense of slowing down research efforts, collaborations, and ultimately detracting from the researcher's primary focus of solving biological problems. Building upon recent advances in computer processors, specifically due to NVIDIA's Tesla, AMD's Firestream, and Intel's upcoming Many Integrated Core (MIC) processors, a new wave of processing technology makes it possible for individual researchers to get increased speed and enhanced visualizations directly in MATLAB. Over the last four years, we have developed and released our first product, Jacket: The GPU Engine for MATLAB, which enables scientists to performlow-level MATLAB computations on the GPU. In Phase I, we were successful at GPU accelerating a set of building block MATLAB functions commonly used by neuroscientists, such as those found in MATLAB's Signal Processing, Image Processing, and Statistics Toolboxes. In Phase II, we plan to leverage the success of Phase I to deliver a more comprehensive suite of GPU-enhanced neuroscience functions to the MATLAB community. Through various surveys of the Jacket user community, we have identified 3 primary competencies that are needed to make research advancements in the MATLAB neuroscience community: faster medical image processing, faster bioinformatics algorithms, and visualization capabilities that leverage state-of the-art graphics directly in MATLAB.PUBLIC HEALTH RELEVANCE: The purpose of this project is to advance the development of Jacket to deliver high performance GPU- enabled tools to neuroscientists. Today, there are an estimated 1.5 million life science MATLAB users, with a substantial portionof those using MATLAB to solve neuroscience-related problems. MATLAB users, especially those dealing with large neuroscience datasets, such as brain MRI, fMRI, DW-MRI, PET, and CT image volumes, microscopy imagery, and genomics datasets, currently have two major problems in using MATLAB to conduct neuroscience research: 1) MATLAB is slow when compared to other programming languages such as C/C++, and 2) MATLAB visualizations are unable to handle large amounts of data or to render 3D models of anatomical structures with ease. Due to recent advances in computer processors, specifically due to NVIDIA's Tesla, AMD's Firestream, and Intel's upcoming Many Integrated Core (MIC), a new wave of desk-side and server processor technology makes it possible for individual researchers to get increased speed and enhanced visualizations directly in MATLAB. In this work, we will extend Jacket by GPU- enabling the popular Statistical Parametric Mapping Toolbox and the Bioinformatics Toolbox and by enhancing our visualizationlibrary for medical imaging and bioinformatics.

* information listed above is at the time of submission.

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