XPS: FULL: Collaborative Research: PARAGRAPH: Parallel, Scalable Graph Analytics

XPS：完整：协作研究：段落：并行、可扩展图形分析

• 批准号: 1629548
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 54.69万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629548&HistoricalAwards=false
• 关键词: XPS; FULL; Collaborative; Research; PARAGRAPH

Many real world problems can be effectively modeled as complex relationship networks or graphs where nodes represent entities of interest and edges mimic the interactions or relationships among them. The number of such problems and the diversity of domains from which they arise is growing. However developing high-performance applications to extract useful information from such datasets is very challenging. Graphical processing units are very attractive for such applications because they offer higher computational performance and energy efficiency than standard multi-core processors. However, the development of high-performance applications for them is currently much more challenging than parallel program development for standard multi-core processors. Effective application development to use graphical processing units generally requires that developers possess considerable expertise on their architectural characteristics and use specialized programming models and performance optimization techniques. Thus, simultaneously achieving high performance and high user productivity for data analytics applications for such devices is a daunting challenge.This project proposes a scalable high-level software framework to enable the productive development of high-performance applications for graphical processing units. It features two distinct abstractions to address the performance and productivity challenges in developing graph/data analytics applications: 1) a frontier-centric abstraction that is based on a common iterative characteristic of many of these applications, with a dynamically moving active frontier of vertices (or edges) where computation is centered, and 2) an abstraction based on sparse linear algebra primitives, exploiting the dual relationship between sparse matrices and graphs. A benchmark suite of graph analytics applications will be developed and evaluated using both abstractions, enabling insights into the effectiveness of these alternate high-level abstractions for a range of analytics applications. The benchmark suite and the software framework will be publicly released.

许多现实世界问题可以有效地建模为复杂的关系网络或图表，其中节点代表了感兴趣的实体，并模仿了它们之间的相互作用或关系。这些问题的数量以及它们出现的域的多样性正在增长。但是，开发高性能应用程序以从此类数据集中提取有用的信息非常具有挑战性。 图形处理单元对此类应用非常有吸引力，因为它们提供的计算性能和能源效率高于标准多核处理器。但是，目前，为他们的高性能应用程序开发与标准多核处理器的并行程序开发更具挑战性。使用图形处理单元的有效应用程序开发通常要求开发人员在其建筑特征上具有丰富的专业知识，并使用专门的编程模型和性能优化技术。因此，在此类设备的数据分析应用程序中同时实现高性能和高用户生产力是一个令人生畏的挑战。本项目提出了一个可扩展的高级软件框架，以使图形处理单元的高性能应用程序的生产性开发。 It features two distinct abstractions to address the performance and productivity challenges in developing graph/data analytics applications: 1) a frontier-centric abstraction that is based on a common iterative characteristic of many of these applications, with a dynamically moving active frontier of vertices (or edges) where computation is centered, and 2) an abstraction based on sparse linear algebra primitives, exploiting the dual relationship between sparse matrices and graphs.将使用这两个抽象来开发和评估图形分析应用程序的基准套件，从而对这些替代高级抽象在一系列分析应用程序中的有效性进行见解。基准套件和软件框架将公开发布。