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

在这个项目中，PI正在开发在分布式环境中对大量数据进行迭代计算的理论基础。基于已开发的理论，PI旨在为迭代计算构建高度可扩展和高效的分布式框架。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，旨在挑战同步是必要的和迭代计算必须在一个？一次又一次的迭代？的方式。这些编程模型和支持的分布式框架的目标是减轻程序员在指定迭代更新的执行顺序和通信机制方面的负担，并在机器集群中自动优化计算的执行。本项目开发的技术将立即具有重要的应用，具有更广泛的社会影响，如道路交通预测、生物信息发现、在线营销和计算机法医分析。

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.