Global Graphs: A Middleware for Data Intensive Computing

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科学和工程许多领域的瓶颈。这个项目正在构建一个名为Global Graphs的新型中间件框架来解决这个瓶颈问题。Global Graphs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。这个提议的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。这种灵活性可以在离散的数据和过程视图中发挥作用，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有效实现，或者介于这两个极端之间。一个额外的结果将是性能反馈引擎的开发，它将为程序员提供对程序部分的洞察力，以便重点进行性能调优。提出的工作对需要处理大规模数据集的一系列领域具有重要意义，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成果将是培养有能力的本科生和研究生。法律顾问党积极鼓励代表性不足的少数民族参与这一努力。

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.