GPU-enhanced Neuroscience Software Tools

GPU 增强型神经科学软件工具

• 批准号: 8315527
• 负责人: John Melonakos
• 金额: $ 49.96万
• 依托单位: ACCELEREYES, LLC
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-10 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8315527
• 关键词: Address; Advanced Development; Algorithms; Bioinformatics; Biological; Biological Sciences; Brain; Budgets; Code; Collaborations; Collection; Communities; Computer software; Computers; Data; Data Set; Documentation; Feedback; Functional Imaging; Functional Magnetic Resonance Imaging; Genetic Engineering; Genome; Genomics; Goals; Image; Imagery; Individual; Libraries; Magnetic Resonance Imaging; Maps; Medical Imaging; Microscopy; Neurosciences; Neurosciences Research; Performance; Phase; Positron-Emission Tomography; Process; Programming Languages; Proteome; Research; Research Personnel; Scientist; Side; Software Tools; Speed; Structure; Surveys; Technology; Testing; Time; Visualization software; Work; base; biological research; computerized data processing; drug discovery; image processing; online tutorial; statistics; success; three-dimensional modeling; tool

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. 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.

描述（由申请人提供）：此应用程序旨在为神经科学家提供高性能，支持gpu的计算和可视化软件工具。今天，估计有150万生命科学MATLAB用户，其中很大一部分使用MATLAB来解决与神经科学相关的问题。MATLAB用户，特别是那些处理大型神经科学数据集的用户，如脑MRI、fMRI、DW-MRI、PET、CT图像、显微镜图像和基因组学数据集，目前在使用MATLAB进行神经科学研究时存在两个主要问题：1)与其他编程语言（如C/ c++）相比，MATLAB速度较慢；2)MATLAB可视化无法处理大量数据或轻松呈现解剖结构的3D模型。因此，神经科学家经常承担昂贵和耗时的工作，将神经科学MATLAB代码移植到C/ c++，代价是减缓研究工作，合作，并最终从研究人员解决生物学问题的主要焦点中转移。基于计算机处理器的最新进展，特别是由于NVIDIA的Tesla， AMD的Firestream和英特尔即将推出的许多集成核心（MIC）处理器，新的处理技术浪潮使个人研究人员可以直接在MATLAB中获得更快的速度和增强的可视化效果。在过去的四年里，我们开发并发布了我们的第一款产品，Jacket: the GPU Engine for MATLAB，它使科学家能够在GPU上执行低级的MATLAB计算。在第一阶段，我们成功地在GPU上加速了神经科学家常用的一组构建块MATLAB函数，例如MATLAB的信号处理、图像处理和统计工具箱中的那些函数。在第二阶段，我们计划利用第一阶段的成功为MATLAB社区提供更全面的gpu增强神经科学功能套件。通过对Jacket用户社区的各种调查，我们确定了在MATLAB神经科学社区取得研究进展所需的3个主要能力：更快的医学图像处理，更快的生物信息学算法，以及利用状态的可视化功能