Global Graphs: A Middleware for Data Intensive Computing

在许多科学和工程领域，在高端计算系统上开发有效和高效软件所需的时间和精力往往是瓶颈。该项目正在构建一种名为全局图的新型中间件框架，以解决这一瓶颈。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。这种灵活性可以通过数据和流程视图的离散规模来利用，具体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，还是高效地实现大规模问题，或者介于这两个极端之间的东西。另一个结果将是开发一个性能反馈引擎，它将为程序员提供对程序中要关注的部分的洞察，以进行性能调整。拟议的工作对需要处理大规模数据集的一系列领域具有重要影响，包括数据挖掘、科学计算和XML数据管理。这项工作的更广泛的结果将是培养有能力的本科生和研究生。私人投资机构正在积极鼓励代表人数不足的少数群体参与这一努力。

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.