GPU-based Computational Advancements for Neuroscience MATLAB Programs

基于 GPU 的神经科学 MATLAB 程序计算进步

• 批准号: 8003884
• 负责人: John Melonakos
• 金额: $ 23.64万
• 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 2011-09-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8003884
• 关键词: Address; Advanced Development; Algorithms; Arts; Bioinformatics; Biological; Brain; Code; Collaborations; Collection; Computer software; Computers; Data; Data Analyses; Data Set; Development; Digital Signal Processing; Documentation; Educational process of instructing; Functional Magnetic Resonance Imaging; Genomics; Goals; Healthcare Industry; Image; Imagery; Individual; Industry; Learning; Magnetic Resonance Imaging; Maps; Microscopy; Modeling; Neurosciences; Neurosciences Research; Performance; Phase; Positron-Emission Tomography; Process; Programming Languages; Reference Standards; Research; Research Personnel; Science; Scientist; Shapes; Side; Simulate; Software Tools; Speed; Structure; System; Technology; Texture; Time; Visualization software; Work; base; computational neuroscience; computerized data processing; cone-beam computed tomography; design; empowered; high standard; image processing; improved; microbial alkaline proteinase inhibitor; open source; programs; public health relevance; research and development; statistics; success; three-dimensional modeling; tool; trend

DESCRIPTION (provided by applicant): The purpose of this project is to advance the development of Jacket: The GPU Engine for MATLAB to include functionality aimed at enhancing computational neuroscience. We will develop tools which will allow MATLAB(R) programmers to access the performance and speed benefits of graphics processing units (GPUs). Today, there are an estimated 1.5 million MATLAB users in the healthcare industry, 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 volumes as well as microscopy imagery, 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. However, due to recent advances in computer processors, specifically due to NVIDIA's Tesla, AMD's Firestream, and Intel's upcoming Larrabee GPUs, a new wave of desk-side processing technology makes it possible for individual researchers to get increased speed and enhanced visualizations directly in MATLAB. Over the last two 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 propose to extend Jacket by GPU-enabling the most common MATLAB functions used by neuroscientists, such as those found in MATLAB's Signal Processing, Image Processing, and Statistics Toolboxes. In Phase II, we plan to GPU-enable higher-level neuroscience- targeted MATLAB tasks, such as those available in the open source SPM (Statistical Parametric Mapping) toolkit and MATLAB's Bioinformatics Toolbox. Also, in Phase II, we plan to greatly enhance MATLAB's visualizations by GPU-enabling the Handle Graphics API and by using recent ray tracing technologies, such as those emerging in NVIDIA's NVIRT, to provide state-of-the-art volume rendering functions. In order to achieve these goals, further research and development is needed to build these tools and optimize them to achieve the best performance and highest standards of stability and user-friendliness. PUBLIC HEALTH RELEVANCE: The purpose of this project is to advance the development of Jacket: The GPU Engine for MATLAB to include functionality aimed at enhancing computational neuroscience. MATLAB users, especially those dealing with large neuroscience datasets, such as brain MRI, fMRI, DW-MRI, PET, and CT volumes as well as microscopy imagery, currently have two major problems in using MATLAB to conduct neuroscience research: 1) computational speed, and 2) lack of high-performance state-of-the-art visualizations. Due to recent advances in computer processors, specifically due to NVIDIA's Tesla, AMD's Firestream, and Intel's upcoming Larrabee GPUs, a new wave of desk-side processing 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 most common MATLAB functions used by neuroscientists, such as those found in MATLAB's Signal Processing, Image Processing, and Statistics Toolboxes. These efforts will accelerate neuroscience efforts worldwide by empowering neuroscientists to focus on science, rather than computational implementations.

描述（由申请人提供）：该项目的目的是推进 Jacket 的开发：MATLAB 的 GPU 引擎，包括旨在增强计算神经科学的功能。我们将开发工具，使 MATLAB(R) 程序员能够获得图形处理单元 (GPU) 的性能和速度优势。如今，医疗保健行业估计有 150 万 MATLAB 用户，其中很大一部分使用 MATLAB 来解决神经科学相关问题。 MATLAB 用户，尤其是处理大型神经科学数据集（例如脑 MRI、fMRI、DW-MRI、PET 和 CT 体积以及显微图像）的用户，目前在使用 MATLAB 进行神经科学研究时遇到两个主要问题：1) 与 C/C++ 等其他编程语言相比，MATLAB 速度较慢；2) MATLAB 可视化无法处理大量数据或渲染 3D 轻松制作解剖结构模型。因此，神经科学家经常花费昂贵且耗时的努力将神经科学 MATLAB 代码移植到 C/C++，其代价是减慢研究工作和协作，并最终偏离研究人员解决生物学问题的主要焦点。然而，由于计算机处理器的最新进展，特别是 NVIDIA 的 Tesla、AMD 的 Firestream 和英特尔即将推出的 Larrabee GPU，新一波的桌面处理技术使个人研究人员能够直接在 MATLAB 中获得更高的速度和增强的可视化效果。在过去的两年里，我们开发并发布了我们的第一个产品 Jacket：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，我们建议通过 GPU 扩展 Jacket，支持神经科学家使用的最常用 MATLAB 函数，例如 MATLAB 信号处理、图像处理和统计工具箱中的函数。在第二阶段，我们计划通过 GPU 支持更高级别的神经科学目标 MATLAB 任务，例如开源 SPM（统计参数映射）工具包和 MATLAB 生物信息学工具箱中提供的任务。此外，在第二阶段，我们计划通过支持 GPU 的 Handle Graphics API 以及使用最新的光线追踪技术（例如 NVIDIA NVIRT 中出现的技术）来大幅增强 MATLAB 的可视化效果，以提供最先进的体渲染功能。为了实现这些目标，需要进一步的研究和开发来构建这些工具并对其进行优化，以实现最佳性能以及最高标准的稳定性和用户友好性。 公共健康相关性：该项目的目的是推进 Jacket：MATLAB GPU 引擎的开发，以包含旨在增强计算神经科学的功能。 MATLAB 用户，尤其是那些处理大型神经科学数据集（例如脑 MRI、fMRI、DW-MRI、PET 和 CT 体积以及显微图像）的用户，目前在使用 MATLAB 进行神经科学研究时面临两个主要问题：1）计算速度，2）缺乏高性能、最先进的可视化。由于计算机处理器的最新进展，特别是 NVIDIA 的 Tesla、AMD 的 Firestream 和英特尔即将推出的 Larrabee GPU，新一波的桌面处理技术使个人研究人员能够直接在 MATLAB 中获得更高的速度和增强的可视化效果。在这项工作中，我们将通过 GPU 扩展 Jacket，支持神经科学家使用的最常见的 MATLAB 函数，例如 MATLAB 信号处理、图像处理和统计工具箱中的函数。这些努力将使神经科学家能够专注于科学而不是计算实现，从而加速全球神经科学的发展。