CSR---AES: Collaborative Research: Intelligent Design and Optimization of Parallel and Distributed Applications

CSR---AES：协作研究：并行和分布式应用的智能设计和优化

• 批准号: 0509326
• 负责人: Joel Saltz
• 金额: $ 5万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509326&HistoricalAwards=false
• 关键词: CSR; AES; Collaborative; Research; Intelligent

This project systematically addresses the enormous complexity of mapping applications to current and future parallel platforms - both scalable parallel architectures consisting of tens of thousands of processors and distributed systems comprised of collections of these and other resources. By integrating the system layers - domain-specific environment, application program, compiler, run-time environment, performance models and simulation, and workflow manager -- and through a systematic strategy for application mapping, the project will exploit the vast machine resources available in such parallel platforms to dramatically increase the productivity of application programmers.The key contribution of the project will be a systematic solution for performance optimization and adaptive application mapping -- a large step towards automating a process that is currently performed in an ad hoc way by programmers and compilers -- so that it is feasible to obtain scalable performance on parallel and distributed systems consisting of tens of thousands of processing nodes. The application components will be viewed as dynamically adaptive algorithms for which there exist a set of variants and parameters that can be chosen to develop an optimized implementation. Knowledge representation and machine learning techniques utilize this domain knowledge and past experience to navigate the search space efficiently.

该项目系统地解决了将应用程序映射到当前和未来并行平台的巨大复杂性-可扩展的并行架构由数万个处理器组成，分布式系统由这些和其他资源的集合组成。通过集成系统层-特定领域环境、应用程序、编译器、运行时环境、性能模型和模拟以及工作流管理器-并通过应用映射的系统策略，该项目将利用这些并行平台上可利用的大量机器资源，极大地提高应用程序员的生产力。该项目的主要贡献将是一个系统的解决方案用于性能优化和自适应应用程序映射--这是向自动化当前由程序员和编译器以特定方式执行的过程迈出的一大步--使得在由数万个处理节点组成的并行和分布式系统上获得可伸缩性能是可行的。应用程序组件将被视为动态自适应算法，其中存在一组变量和参数，可以选择这些变量和参数来开发优化的实现。 知识表示和机器学习技术利用该领域知识和过去的经验来有效地导航搜索空间。