Global Graphs: A Middleware for Data Intensive Computing

全局图：数据密集型计算的中间件

• 批准号: 0917070
• 负责人: Srinivasan Parthasarathy
• 金额: $ 50万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917070&HistoricalAwards=false
• 关键词: Global; Graphs; Middleware; Data; Intensive

It is often the case that the time and effort required to develop effective and efficient software on high-end computing systems is the bottleneck in many areas of science and engineering. This project is building a novel middleware framework called Global Graphs that targets this bottleneck. Global Graphs takes a data-structure centric view of shared data where graph-based dynamic data structures drive the development of the rest of the system.A key scientific outcome of this proposed framework is to allow the programmer to have multiple views of the shared data as well as multiple views of the control and tasking model. This flexibility can be leveraged along a discrete scale of data and process views depending on whether the goal is to develop a quick prototype for validating ideas on small scale problems, or the goal is efficient realization on large scale problems, or something in between these two extremes. An additional outcome will be the development of a performance feedback engine that will provide the programmer insights into parts of the program to focus on for performance tuning.The proposed work has important implications for a range of domains requiring the processing of large scale datasets, including data mining, scientific computing and XML data management. The broader outcomes of this work will be to train capable undergraduate and graduate students. The PIs are actively encouraging under-represented minorities to participate in this effort.

通常情况下，在高端计算系统上开发有效和高效的软件所需的时间和精力是许多科学和工程领域的瓶颈。这个项目正在构建一个新的中间件框架，称为全局图，目标是这个瓶颈。 全局图采用以数据结构为中心的共享数据视图，其中基于图的动态数据结构驱动系统其余部分的开发。该框架的一个关键科学成果是允许程序员拥有共享数据的多个视图以及控制和任务模型的多个视图。 这种灵活性可以沿着离散规模的数据和过程视图一起利用，这取决于目标是开发用于验证小规模问题的想法的快速原型，还是目标是在大规模问题上的有效实现，或者是介于这两个极端之间的东西。一个额外的成果将是开发一个性能反馈引擎，将提供程序员的洞察到程序的一部分，专注于性能tuning.The拟议的工作有重要的影响，一系列的领域需要处理的大规模数据集，包括数据挖掘，科学计算和XML数据管理。这项工作的更广泛成果将是培养有能力的本科生和研究生。 PI积极鼓励代表性不足的少数群体参与这一努力。