A Biomedical Imaging Acceleration Testbed

生物医学成像加速测试台

• 批准号: 0946463
• 负责人: David Kaeli
• 金额: $ 130万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-15 至 2013-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0946463&HistoricalAwards=false
• 关键词: Biomedical; Imaging; Acceleration; Testbed

Biomedical imaging generates enormous amounts of data - a CT scanner can generate more than 5GB of raw data in 20 seconds - timely extraction of useful information from raw data requires a new computing paradigm. Graphical Processing Units (GPUs) have been used to accelerate many of these applications, but there remain a number of challenge associated with the current state of the art in biomedical imaging acceleration: (1) As each new many-core computing platform comes on the market, the ability to leverage this class of platforms turns into a time-consuming process and generally requires extensive knowledge of the underlying system hardware to extract all the potential performance available. (2) As new biomedical imaging applications are developed, each programming task becomes a repetitive time-consuming effort. What is needed is a methodology to semi-automatically parallelize biomedical imaging codes that can leverage a set of available libraries (the libraries need to be available in a format that can be easily remapped to the most current hardware). (3) Presently, there is a lack of an effective strategy to perform a cost/performance benefit analysis that could be used to justify moving a biomedical imaging application to a new hardware platform. There is also the question of whether multiple GPUs could provide advantages to a particular application.Intellectual Merit: This effort will develop a testbed that can aggressively address these three issues. The activity will leverage the extensive knowledge base available within the Center for Subsurface Sensing and Imaging Systems (CenSSIS), an ERC at Northeastern University (CenSSIS), and Engineering Research Center (ERC) to accelerate a range of key biomedical imaging applications/algorithms. The goals are: (1) develop a methodology for rapid parallelization of biomedical imaging applications by following a set of prescribed steps, and then applying best practices in GPU programming, (2) produce a rich library of parallelized biomedical imaging codes, (3) provide the capability to "right-size" a multi-GPU system to best meet the goals of any biomedical imaging application, (4) deliver these capabilities in a web-based framework that will allow a larger community to leverage the technology available in this Testbed. The project will develop a distributed Testbed where each partner will provide either biomedical imaging or GPU parallelization expertise (or both). The outcome should include a new set of parallel libraries for the biomedical research community, as well as a Testbed model that can be replicated across other research communities that require acceleration using many-core platforms. Broader Impacts: The broader aspects of this proposal include having an accompanying educational program on biomedical acceleration, which will leverage the availability of the Testbed hardware and software. The broader impact of this work (if successful) would ultimately be a seamless high performance computing system for scientists in biomedical imaging who can focus on their respective projects (rather than investing time to learn about technologies that would help them utilize hpc systems.) This project will directly engage students and researchers from underrepresented groups. This project will also leverage the ongoing Research Experiences for Undergraduates programs within CenSSIS, which have provided rich summer experiences for undergraduates from minority-serving institutions, providing a pathway to pursue graduate research in high performance computing a biomedical imaging.

生物医学成像会产生大量的数据--CT扫描仪可以在20秒内生成超过5GB的原始数据--从原始数据中及时提取有用的信息需要一种新的计算模式。图形处理单元（GPU）已被用于加速这些应用中的许多应用，但仍存在与生物医学成像加速的当前技术水平相关的许多挑战：（1）随着每个新的众核计算平台上市，利用这类平台的能力变成了一个时间-这是一个消耗过程，并且通常需要对底层系统硬件的广泛了解，以提取所有可用的潜在性能。(2)随着新的生物医学成像应用的开发，每个编程任务都变成了重复的耗时工作。所需要的是一种半自动并行化生物医学成像代码的方法，该方法可以利用一组可用的库（库需要以可以容易地重新映射到最新硬件的格式提供）。(3)目前，缺乏一个有效的策略来执行成本/性能效益分析，可以用来证明移动生物医学成像应用程序到一个新的硬件平台。还有一个问题是，多个GPU是否可以为特定的应用程序提供优势。智力优势：这项工作将开发一个测试平台，可以积极解决这三个问题。该活动将利用地下传感和成像系统中心（CenSSIS），东北大学（CenSSIS）的ERC和工程研究中心（ERC）内的广泛知识库，以加速一系列关键的生物医学成像应用/算法。目标是：（1）通过遵循一组规定的步骤，然后在GPU编程中应用最佳实践，开发用于生物医学成像应用的快速并行化的方法，（2）产生并行化生物医学成像代码的丰富库，（3）提供“适当大小”多GPU系统的能力，以最好地满足任何生物医学成像应用的目标，（4）在一个基于网络的框架中提供这些功能，使更大的社区能够利用该测试平台中可用的技术。该项目将开发一个分布式测试床，每个合作伙伴将提供生物医学成像或GPU并行化专业知识（或两者兼而有之）。其成果应该包括一套新的生物医学研究社区的并行库，以及一个可以在其他需要使用众核平台加速的研究社区中复制的测试床模型。更广泛的影响：该提案的更广泛的方面包括附带的生物医学加速教育计划，该计划将利用Testbed硬件和软件的可用性。这项工作（如果成功的话）的更广泛的影响最终将是为生物医学成像科学家提供一个无缝的高性能计算系统，他们可以专注于各自的项目（而不是花时间学习有助于他们利用HPC系统的技术）。该项目将直接吸引来自代表性不足群体的学生和研究人员参与。该项目还将利用CenSSIS内正在进行的本科生研究经验计划，为少数民族服务机构的本科生提供丰富的暑期经验，为高性能计算生物医学成像的研究生研究提供途径。