GPU-enhanced Neuroscience Software Tools
GPU-enhanced Neuroscience Software Tools
批准号:
8628180
负责人:
John Melonakos
金额:
$49.96万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-10 至 2015-02-28
关键词:
AddressAdvanced DevelopmentAlgorithmsBioinformaticsBiologicalBiological SciencesBrainBudgetsCodeCollaborationsCollectionCommunitiesComputer softwareComputersDataData SetDocumentationFeedbackFunctional ImagingFunctional Magnetic Resonance ImagingGenetic EngineeringGenomeGenomicsGoalsImageryIndividualLibrariesMagnetic Resonance ImagingMapsMedical ImagingMicroscopyNeurosciencesNeurosciences ResearchPerformancePhasePositron-Emission TomographyProcessProgramming LanguagesProteomeResearchResearch PersonnelScientistSideSoftware ToolsSpeedStructureSurveysTechnologyTestingTimeVisualization softwareWorkX-Ray Computed Tomographybasebiological researchdrug discoveryimage processingonline tutorialpublic health relevancesignal processingstatisticssuccessthree-dimensional modelingtool
中文摘要
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英文摘要
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 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.
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会议论文
Accelerating biomedical image processing using massively parallel processors
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批准号:9138396
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项目类别:
-
资助金额:$14.64万
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财政年份:2016
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负责人:John Melonakos
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依托单位:
GPU-enhanced Neuroscience Software Tools
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批准号:8315527
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项目类别:
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资助金额:$49.96万
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财政年份:2010
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负责人:John Melonakos
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依托单位:
GPU-based Computational Advancements for Neuroscience MATLAB Programs
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批准号:8003884
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项目类别:
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资助金额:$23.64万
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财政年份:2010
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负责人:John Melonakos
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依托单位:
GPU-enhanced Neuroscience Software Tools
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批准号:8444396
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项目类别:
-
资助金额:$49.96万
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财政年份:2010
-
负责人:John Melonakos
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依托单位:
海外基金