CSR: Small: Theoretical Foundations and Distributed Frameworks for Iterative Computations on Massive Data

CSR：小：海量数据迭代计算的理论基础和分布式框架

• 批准号: 1217284
• 负责人: Lixin Gao
• 金额: $ 45万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217284&HistoricalAwards=false
• 关键词: CSR; Small; Theoretical; Foundations; Distributed

In this project, the PI is developing theoretical foundations for performing iterative computations on massive data in a distributed environment. Based on the developed theory, the PI aims to build highly scalable and efficient distributed frameworks for iterative computations. The distributed framework takes the burden of describing the iterative process away from programmers and performs the iterative updates in an efficient manner. A series of programming models will be developed aiming to challenge the conventional wisdom that synchronization is essential and iterative computations have to be performed in an ?iteration by iteration? manner. The goal of these proposed programming models and supporting distributed frameworks is to lift the burden of the programmers in specifying execution order of iterative updates and communication mechanisms, and automatically optimize the execution of the computation in a cluster of machines. The technologies developed in this project will have immediate important applications with broader societal impacts such as road traffic prediction, biological information discovery, online marketing, and computer forensic analysis.

在这个项目中，PI 正在开发在分布式环境中对海量数据执行迭代计算的理论基础。基于所开发的理论，PI 旨在构建高度可扩展且高效的分布式迭代计算框架。分布式框架减轻了程序员描述迭代过程的负担，并以有效的方式执行迭代更新。将开发一系列编程模型，旨在挑战传统观念，即同步是必不可少的，并且迭代计算必须在迭代中进行？方式。这些提出的编程模型和支持分布式框架的目标是减轻程序员指定迭代更新的执行顺序和通信机制的负担，并自动优化机器集群中计算的执行。 该项目开发的技术将具有直接的重要应用，并产生更广泛的社会影响，例如道路交通预测、生物信息发现、在线营销和计算机取证分析。