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: 0615155
• 负责人: Joel Saltz
• 金额: $ 25.3万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0615155&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：对并行和分布式应用程序的智能优化，该项目得出了一个系统的解决方案，用于性能优化和自适应应用映射，以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性能。借助专家领域的分子动力学（MD）模拟，我们期望在MD代码上达到性能水平，甚至比多年开发后手动得出的绩效水平更好，并且多种端口到各种体系结构。应用程序组件被视为动态适应性存在一组变体和参数的算法可以搜索以开发优化的实现。 工作流程是该应用程序的一个实例，其中节点代表应用程序组件和节点之间的依赖性表示执行排序约束。通过以这种方式编码应用程序，我们捕获了具有非常紧凑的表示形式的大量可能的应用映射。 系统层探索了可能实现的巨大空间，以得出最合适的解决方案。 由于映射的空间非常大，因此该系统从编译器的领域科学家和设计师，运行时间和性能模型中捕获并利用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此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，知识表示，知识的最新方法并加速了MD模拟的进步，并具有更高生产力的软件开发和移植。 更广泛地说，这项研究可以在其他科学中进行系统的性能优化。