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将来的并行平台的巨大复杂性 - 这两种可扩展的并行体系结构都由数以万计的处理器和分布式系统组成，这些系统由这些和其他资源的集合组成。通过整合系统层 - 特定领域的环境，应用程序，编译器，运行时环境，绩效模型和仿真以及工作流程管理器 - 以及通过用于应用程序映射的系统策略，该项目将利用此类平行平台中可用的广泛的机器资源，以极大地提高应用程序计划的生产力。由程序员和编译器以临时的方式执行 - 因此，可以在由数以万计的处理节点组成的并行和分布式系统上获得可扩展性能。应用程序组件将被视为动态自适应算法，其中存在一组变体和参数，可以选择来开发优化的实现。 知识表示和机器学习技术利用此领域知识和过去的经验有效地浏览搜索空间。