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）：此应用程序是为了向神经科学家提供高性能，支持GPU的计算和可视化软件工具。如今，估计有150万生命科学MATLAB使用者，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，尤其是那些处理大型神经科学数据集的用户，例如Br​​ain MRI，fMRI，DW-MRI，PET和CT图像量，和CT图像量，显微镜图像和基因组学数据集，目前在使用MATLAB来进行神经科学研究的两个主要问题上有两个主要问题：1）与其他程序相比，MATLAB在其他程序中的范围很慢。数据或使解剖结构的3D模型轻松。因此，神经科学家通常会为端口神经科学MATLAB代码端口c/c ++做出昂贵且耗时的努力，而牺牲了减慢研究工作，合作，并最终损害了研究人员解决生物学问题的主要重点。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Intel即将到来的许多集成核心（MIC）处理器，新的处理技术使单个研究人员可以使MATLAB中的速度提高并增强可视化。在过去的四年中，我们已经开发并发布了第一个产品，即MATLAB的GPU引擎，它使科学家能够在GPU上执行低级MATLAB计算。在第一阶段，我们成功地加速了神经科学家常用的一组构建块MATLAB功能，例如在MATLAB的信号处理，图像处理和统计工具箱中发现的构建块功能。在第二阶段，我们计划利用第一阶段的成功，向MATLAB社区提供更全面的GPU增强神经科学功能。通过对夹克用户社区的各种调查，我们已经确定了3种主要能力，这些能力是在MATLAB神经科学社区中进行研究进步所需的：更快的医疗图像处理，更快的生物信息学算法和利用可视化功能，以利用最新的可视化功能 直接在MATLAB中的图形图形。 公共卫生相关性：该项目的目的是促进外套的开发，以向神经科学家提供高性能的工具。如今，估计有150万生命科学MATLAB使用者，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，尤其是那些处理大型神经科学数据集的用户，例如Br​​ain MRI，fMRI，DW-MRI，PET和CT图像量，和CT图像量，显微镜图像和基因组学数据集，目前在使用MATLAB来进行神经科学研究的两个主要问题上有两个主要问题：1）与其他程序相比，MATLAB在其他程序中的范围很慢。数据或使解剖结构的3D模型轻松。由于计算机处理器的最新进展，特别是由于NVIDIA的Tesla，AMD的Firestream和Intel即将推出的许多集成核心（MIC），因此在MATLAB中直接在MATLAB中直接提高速度并增强了可视化的新浪潮，使各个台式和服务器处理器技术成为可能。在这项工作中，我们将通过GPU启用流行的统计参数映射工具箱和生物信息学工具箱，并增强我们用于医学成像和生物信息学的可视化库来扩展外套。