XPS: SDA: Collaborative Research: A Scalable and Distributed System Framework for Compute-Intensive and Data-Parallel Applications

XPS：SDA：协作研究：用于计算密集型和数据并行应用的可扩展分布式系统框架

• 批准号: 1337244
• 负责人: Jun Wang
• 金额: $ 37.5万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1337244&HistoricalAwards=false
• 关键词: XPS; SDA; Collaborative; Research; Scalable

Whereas traditional high-performance computing (HPC) applications are computationally intensive, recent HPC applications require more data-intensive analysis and visualization to extract knowledge. In many cases, these applications execute the same computational algorithm as in the past (e.g., parallel search or parallel rendering) but now must do so for significantly larger data sets. For example, the life sciences, along with the cross-cutting area of scientific visualization, constitute an emerging category of HPC applications that not only perform sophisticated calculations but also ingest a sea of data. Running these new HPC data-parallel applications on today's computing platforms imposes new challenges and demands additional functionality.However, today's HPC platforms still adopt a compute-centric model and do not handle these new challenges well. Such a model often moves a large amount of data to various parallel computational processes. Consequently, long CPU wait times for I/O to complete and enormous data-movement overhead become major stumbling blocks to high performance and scalability. This project encompasses the creation of a scalable cross-layer software framework to enable both computationally intensive and data-intensive parallel HPC applications to run on distributed file systems. This framework consists of two interwoven research tasks: (1) an adaptive, data locality-aware, middleware system that dynamically schedules compute processes to access local data by monitoring physical data locations and (2) a framework that captures the computation and data I/O processing relationship from parallel applications and coordinates the scheduling of the corresponding process and I/O execution for maximum parallel efficiency. The success of this project contributes enhanced productivity and return on investment on HPC resources via the elimination of both CPU wait time and network transfer of frequently accessed data in scientific applications. An open-source, sustainable, and reusable software framework is delivered to speed-up the discovery and innovation process in areas such as bioinformatics, climate, high-energy physics, cosmology, astrophysics, and chromodynamics. The synergy in the two proposing institutions, Virginia Tech and the University of Central Florida, and their collaborating DOE national laboratories, will catalyze new and beneficial perspectives in the graduate education of students and prepare a 21st-century workforce in HPC.

传统的高性能计算 (HPC) 应用程序属于计算密集型，而最近的 HPC 应用程序需要更多的数据密集型分析和可视化来提取知识。在许多情况下，这些应用程序执行与过去相同的计算算法（例如并行搜索或并行渲染），但现在必须针对更大的数据集这样做。例如，生命科学以及科学可视化的交叉领域构成了 HPC 应用程序的新兴类别，它们不仅执行复杂的计算，而且还摄取大量数据。在当今的计算平台上运行这些新的 HPC 数据并行应用程序带来了新的挑战并需要额外的功能。但是，当今的 HPC 平台仍然采用以计算为中心的模型，并不能很好地应对这些新挑战。这种模型通常将大量数据转移到各种并行计算过程中。因此，I/O 完成的较长 CPU 等待时间和巨大的数据移动开销成为高性能和可扩展性的主要障碍。该项目包括创建可扩展的跨层软件框架，使计算密集型和数据密集型并行 HPC 应用程序能够在分布式文件系统上运行。该框架由两个相互交织的研究任务组成：(1) 一个自适应的、数据位置感知的中间件系统，通过监视物理数据位置动态调度计算进程以访问本地数据；(2) 一个捕获计算和数据 I/来自并行应用程序的O处理关系，并协调相应进程的调度和I/O执行以获得最大并行效率。该项目的成功消除了科学应用中频繁访问的数据的 CPU 等待时间和网络传输，从而提高了 HPC 资源的生产力和投资回报。提供开源、可持续和可重用的软件框架，以加速生物信息学、气候、高能物理、宇宙学、天体物理学和色动力学等领域的发现和创新过程。弗吉尼亚理工大学和中佛罗里达大学这两个提议机构及其合作的 DOE 国家实验室的协同作用，将促进学生研究生教育的新的有益观点，并为 21 世纪 HPC 领域做好准备。