SHF: Large: Collaborative Research: PXGL: Cyberinfrastructure for Scalable Graph Execution

SHF：大型：协作研究：PXGL：可扩展图形执行的网络基础设施

• 批准号: 1111798
• 负责人: Jeanine Cook
• 金额: $ 89.99万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111798&HistoricalAwards=false
• 关键词: SHF; Large; Collaborative; Research; PXGL

The most powerful computing systems in the world have historically been dedicated to solving scientific problems. Until recently, the computations performed by these systems have typically been simulations of various physical phenomena. However, a new paradigm for scientific discovery has been steadily rising in importance, namely, data-intensive science, which focuses sophisticated analysis techniques on the enormous (and ever increasing) amounts of data being produced in scientific, commercial, and social endeavors. Important research based on data-intensive science include areas as diverse as knowledge discovery, bioinformatics, proteomics and genomics, data mining and search, electronic design automation, computer vision, and Internet routing. Unfortunately, the computational approaches needed for data-intensive science differ markedly from those that have been so effective for simulation-based supercomputing. To enable and facilitate efficient execution of data-intensive scientific problems, this project will develop a comprehensive hardware and software supercomputing system for data-intensive science.Graph algorithms and data structures are fundamental to data-intensive computations and, consequently, this project is focused on providing fundamental, new understandings of the basics of large-scale graph processing and how to build scalable systems to efficiently solve large-scale graph problems. In particular, this work will characterize processing overheads and the limits of graph processing scalability, develop performance models that properly capture graph algorithms, define the (co-design) process for developing graph-specific hardware, and experimentally verify our approach with a prototype execution environment. Key capabilities of our system include: a novel fine-grained parallel programming model, a scalable library of graph algorithms and data structures, graph-optimized core architecture, and a scalable graph execution platform. The project will also address the programming challenges involved in constructing scalable and reliable software for data-intensive problems.

历史上，世界上最强大的计算系统一直致力于解决科学问题。直到最近，这些系统执行的计算通常是对各种物理现象的模拟。然而，一种新的科学发现范式的重要性稳步上升，即数据密集型科学，该科学将复杂的分析技术集中在科学，商业和社会努力中生产的巨大（且越来越多的）数据上。基于数据密集型科学的重要研究包括知识发现，生物信息学，蛋白质组学和基因组学，数据挖掘和搜索，电子设计自动化，计算机视觉和互联网路由等多样化的领域。不幸的是，数据密集型科学所需的计算方法与基于模拟的超级计算的有效性的计算方法明显不同。为了启用和促进数据密集型科学问题，该项目将开发一个用于数据密集型科学的全面硬件和软件超级计算系统。图形算法和数据结构是数据密集型计算的基础，因此，该项目侧重于提供基本的大型图形的基础知识，以构建大规模的研究和范围范围的范围范围范围的范围范围的范围。特别是，这项工作将表征处理开销和图形处理可伸缩性的限制，开发适当捕获图形算法的性能模型，定义用于开发图形特定硬件的（共设计）过程，并通过原型执行环境实验验证我们的方法。我们系统的关键功能包括：一种新颖的细粒并行编程模型，一个可扩展的图形算法和数据结构库，图形优化的核心体系结构以及可扩展的图形执行平台。该项目还将解决为数据密集型问题构建可扩展和可靠软件的编程挑战。