A Biomedical Imaging Acceleration Testbed

生物医学成像产生了海量的数据--一台CT扫描仪可以在20秒内产生超过5 GB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)已被用于加速其中许多应用程序，但与生物医学成像加速的当前技术水平相关的挑战仍然存在：(1)随着每个新的多核计算平台上市，利用这类平台的能力变成了一个耗时的过程，通常需要广泛的底层系统硬件知识来提取所有可用的潜在性能。(2)随着新的生物医学成像应用程序的开发，每个编程任务都成为重复的耗时工作。所需要的是一种半自动并行化生物医学成像代码的方法，该生物医学成像代码可以利用一组可用的库(这些库需要以可以容易地重新映射到最新硬件的格式可用)。(3)目前，缺乏有效的策略来执行成本/性能收益分析，该分析可以用来证明将生物医学成像应用程序迁移到新的硬件平台的合理性。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：这一努力将开发一个能够积极解决这三个问题的试验台。该活动将利用地下传感和成像系统中心(CenSSIS)、东北大学ERC(CenSSIS)和工程研究中心(ERC)提供的广泛知识库来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，开发一种快速并行化生物医学成像应用的方法；(2)生成丰富的并行化生物医学成像代码库；(3)提供对多GPU系统进行适当调整的能力，以最好地满足任何生物医学成像应用的目标；(4)在基于网络的框架中提供这些功能，该框架将允许更大的社区利用该试验床中可用的技术。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并行专业知识(或两者兼而有之)。其结果应该包括一套新的生物医学研究社区的并行图书馆，以及一个可以在其他需要使用多核心平台加速的研究社区中复制的测试床模式。更广泛的影响：这项提议的更广泛方面包括附带的生物医学加速教育计划，该计划将利用Tested硬件和软件的可用性。这项工作的更广泛影响(如果成功)最终将是生物医学成像科学家的无缝高性能计算系统，他们可以专注于各自的项目(而不是投入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。该项目还将利用CenSSIS正在进行的本科生研究经验项目，该项目为来自少数族裔服务机构的本科生提供了丰富的暑期经验，为在高性能计算和生物医学成像领域进行研究生研究提供了一条途径。

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.