GPU-enhanced Neuroscience Software Tools

描述(由申请者提供)：该应用程序是为神经科学家提供高性能的、支持GPU的计算和可视化软件工具。今天，估计有150万生命科学领域的MatLab用户，其中很大一部分人使用MatLab解决与神经科学相关的问题。MatLab的用户，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和CT图像体积、显微图像和基因组数据集，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速度慢；2)MatLab可视化无法处理大量数据或轻松地渲染解剖结构的3D模型。因此，神经学家经常付出昂贵和耗时的努力，将神经科学的MatLab代码移植到C/C++，代价是减缓研究工作和合作，并最终分散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的特斯拉、AMD的Firestream和英特尔即将推出的多集成内核(MIC)处理器，新一波处理技术使研究人员能够直接在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，我们已经开发并发布了我们的第一个产品，Jacket：用于MatLab的GPU引擎，它使科学家能够在GPU上执行低级别的MatLab计算。在第一阶段，我们成功地加速了一组神经科学家常用的构建模块的MatLab函数，例如在MatLab的信号处理、图像处理和统计工具箱中找到的那些函数。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增强的神经科学功能套件。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项主要能力：更快的医学图像处理、更快的生物信息学算法，以及利用 最先进的图形直接在MatLab中实现。

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.