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推导出一种用于性能优化和自适应应用程序映射的系统解决方案，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的性能。随着专家领域的科学家在分子动力学（MD）模拟，我们期望实现MD代码的性能水平甚至比已经手动推导出经过多年的发展和许多端口到各种architecture.The应用程序组件被视为动态自适应算法，其中存在一组变量和参数，可以搜索到开发一个优化的实现。 工作流是应用程序的实例，其中节点表示应用程序组件，节点之间的依赖关系表示执行顺序约束。通过以这种方式编码应用程序，我们可以用非常紧凑的表示来捕获大量可能的应用程序映射。 系统层探索可能实现的大空间，以获得最合适的解决方案。 由于映射的空间是非常大的，系统捕获和利用领域的领域科学家和设计师的编译器，运行时和性能模型修剪大多数可能的实现。 知识表示和机器学习利用这一领域的知识和过去的经验来有效地导航搜索空间。这种多学科的方法影响了编译器，运行时系统，机器学习，知识表示等子领域的最新技术，并加速了MD仿真的进步，从而提高了软件开发和移植的效率。 更广泛地说，这项研究使其他科学的系统性能优化成为可能。