Phase II year
2012
(last award dollars: 2014)
Phase II Amount
$1,498,803
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 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. 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 perform low-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 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 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 visualization library for medical imaging and bioinformatics.
Public Health Relevance Statement: 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 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 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 visualization library for medical imaging and bioinformatics.
Project Terms: Address; Advanced Development; Algorithms; base; Bioinformatics; Biological; biological research; Biological Sciences; Brain; Budgets; Code; Collaborations; Collection; Communities; Computer software; computerized data processing; Computers; Data; Data Set; Documentation; drug discovery; Feedback; Functional Imaging; Functional Magnetic Resonance Imaging; Genetic Engineering; Genome; Genomics; Goals; Image; image processing; Imagery; Individual; Libraries; Magnetic Resonance Imaging; Maps; Medical Imaging; Microscopy; Neurosciences; Neurosciences Research; online tutorial; Performance; Phase; Positron-Emission Tomography; Process; Programming Languages; Proteome; Research; Research Personnel; Scientist; Side; Software Tools; Speed (motion); statistics; Structure; success; Surveys; Technology; Testing; three-dimensional modeling; Time; tool; Visualization software; Work
