III: EAGER: A Framework for Large Data Analysis

III：EAGER：大数据分析框架

• 批准号: 1144985
• 负责人: Alin Dobra
• 金额: $ 10万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144985&HistoricalAwards=false
• 关键词: III; EAGER; Framework; Large; Data

Modern multicore architectures, that provide high raw gigaflops and teraflops, have deep memory hierarchies and low overhead threading capabilities. Lack of support for directly exploiting these capabilities leads to severe under-utilization especially for data intensive applications. This project expects to develop methods that efficiently use the available computational power to provide cost improvement for large scale data processing systems. This project will develop a highly efficient computation framework called GLADE that will support a large class of data intensive applications, and will be based on a novel computational model called generalized linear aggregates. The commutative and associative properties of Generalized Linear Aggregates facilitate highly efficient parallel and distributed computation as well as exploitation of deep memory hierarchies, especially when multiple queries are simultaneously executed as is typical in many data-processing tasks. The resulting one to two orders of magnitude improvement in computational efficiency can be expected to yield corresponding reduction in cost and energy requirements of data processing tasks which in turn will make it feasible to analyze much larger data sets than currently possible.The proposed work will make the synergistic combination of high performance computing and large scale data analysis widely available to researchers, and other interested groups in government, industry, and education. The enabling of a large number of data intensive application using inexpensive computers that cost in low tens of thousands of dollars will broaden the use of data analysis, exploration and mining for a wide variety of existing and emerging applications. Examples of such applications include network intrusion detection, social network analysis, climate data, ecosystem analysis, and customer relationship management. Additional information about the project can be found at: http://sites.google.com/site/sanjayranka/glade.

现代多核架构，提供高原始千兆和万亿次浮点运算，具有深度内存层次结构和低开销线程能力。缺乏对直接利用这些功能的支持会导致严重的利用率不足，特别是对于数据密集型应用程序。该项目期望开发有效利用现有计算能力的方法，为大规模数据处理系统提供成本改进。该项目将开发一种名为GLADE的高效计算框架，该框架将支持大量数据密集型应用，并将基于一种名为广义线性聚合的新型计算模型。广义线性聚合的交换和关联特性促进了高效的并行和分布式计算以及对深度内存层次结构的利用，特别是在许多数据处理任务中典型的同时执行多个查询时。由此产生的计算效率的一到两个数量级的改进可以预期产生相应的数据处理任务的成本和能源需求的减少，这反过来又将使分析比目前可能的大得多的数据集成为可能。这项提议的工作将使高性能计算和大规模数据分析的协同结合广泛地提供给研究人员，以及政府、工业和教育领域的其他感兴趣的团体。大量的数据密集型应用程序使用成本低至数万美元的廉价计算机，这将扩大数据分析、勘探和挖掘的使用范围，为各种现有的和新兴的应用程序。这类应用的例子包括网络入侵检测、社会网络分析、气候数据、生态系统分析和客户关系管理。有关该项目的更多信息可以在http://sites.google.com/site/sanjayranka/glade上找到。