CSR---AES: Collaborative Research: Intelligent Optimization of Parallel and Distributed Applications (WP2)

CSR---AES：协作研究：并行和分布式应用的智能优化（WP2）

• 批准号: 0615412
• 负责人: Mary Hall
• 金额: --
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0615412&HistoricalAwards=false
• 关键词: CSR; AES; Collaborative; Research; Intelligent

CSR-AES: Intelligent Optimization of Parallel and Distributed ApplicationsABSTRACTThis project derives a systematic solution for performance optimization and adaptive application mapping to obtain scalable performance on parallel and distributed systems consisting of tens of thousands of processing nodes. With expert domain scientists in molecular dynamics (MD) simulation, we expect to achieve performance levels on MD codes even better than what has been derived manually after years of development and many ports to a variety of architectures.The application components are viewed as dynamically adaptive algorithms for which there exist a set of variants and parameters that can be searched to develop an optimized implementation. A workflow is an instance of the application where nodes represent application components and dependences between the nodes represent execution ordering constraints. By encoding an application in this way, we capture a large set of possible application mappings with a very compact representation. The system layers explore the large space of possible implementations to derive the most appropriate solution. Because the space of mappings is prohibitively large, the system captures and utilizes domain knowledge from the domain scientists and designers of the compiler, run-time and performance models to prune most of the possible implementations. Knowledge representation and machine learning utilize this domain knowledge and past experience to navigate the search space efficiently.This multidisciplinary approach impacts the state-of-the-art in the sub-fields of compilers, run-time systems, machine learning, knowledge representation, and accelerates advances in MD simulation with far more productive software development and porting. More broadly, this research enables systematic performance optimization in other sciences.

CSR-AES：并行和分布式应用程序的智能优化摘要该项目导出了性能优化和自适应应用程序映射的系统解决方案，以便在由数万个处理节点组成的并行和分布式系统上获得可扩展的性能。凭借分子动力学 (MD) 模拟方面的专家领域科学家，经过多年的开发以及对各种架构的许多移植，我们期望在 MD 代码上实现比手动得出的性能水平更好的性能水平。应用程序组件被视为动态自适应存在一组变体和参数的算法，可以搜索这些变体和参数来开发优化的实现。 工作流是应用程序的实例，其中节点代表应用程序组件，节点之间的依赖关系代表执行顺序约束。通过以这种方式对应用程序进行编码，我们可以以非常紧凑的表示形式捕获大量可能的应用程序映射。 系统层探索大量可能的实现，以得出最合适的解决方案。 由于映射空间过大，系统捕获并利用来自领域科学家和编译器、运行时和性能模型设计者的领域知识来修剪大多数可能的实现。 知识表示和机器学习利用该领域知识和过去的经验来有效地导航搜索空间。这种多学科方法影响了编译器、运行时系统、机器学习、知识表示、并通过更高效的软件开发和移植加速 MD 仿真的进步。 更广泛地说，这项研究使得其他科学领域的系统性能优化成为可能。