SDCI HPC Improvement: High-Productivity Performance Engineering (Tools, Methods, Training) for NSF HPC Applications

高性能计算（HPC）的前景将通过在可扩展的HPC计算机系统上以其性能范围的高端执行的科学和工程（SE）应用来实现。& SE应用程序代码的性能优化将通过性能工程的过程来实现，其中并行性能测量、分析和调优的工具被有效地用于发现性能低效率的来源并将其去除。&并行性能工具的研究和开发为性能观察、分析和优化创造了强大的技术，并产生了可移植、可互操作和可扩展的技术解决方案。现在，重要的是将成功的、强大的并行性能基础设施转移到性能工程框架，与HPC网络基础设施集成，并针对HPC性能问题解决的记录用户需求。此外，如果要最大限度地利用HPC资源，还必须进行以人为本的投资，以帮助培训应用程序开发人员成为优秀的性能工程师。更广泛的影响这一性能软件基础将由社区驱动的教育和培训计划补充，以提高多个S E领域性能工程工作中的人力生产力。&拟议的项目还将创建一个性能技术和工程的培训计划，该计划将在匹兹堡超级计算中心进行试点和改进，并随着时间的推移与TeraGrid教育，推广，培训（EOT）计划相结合。该计划的目标将是教育应用程序开发人员和学生健全的性能评估方法，教他们工程高性能代码解决方案的最佳实践的基础上专家调优策略，并培训他们有效地使用性能工具。该项目将为分布式访问开发培训材料和基础设施，并制定一系列教程，并带来自己的代码研讨会，这些研讨会将亲自提供并通过网格提供。此外，应用程序参与将是这项活动的一个重要组成部分。该项目将与本科生和研究生直接在S E应用程序的性能分析，并与领先的大型应用程序的开发人员，以集成性能工程在他们的项目。&将创建一个性能存储库，其中包含广泛应用程序和平台的详细特征数据，并可供所有HPC中心用于性能数据挖掘。项目的成功将通过三个指标来衡量：在高影响力的S E应用程序上实现的应用程序性能的改进，跨S E域的应用程序开发人员的性能能力的提高，以及NSF Track 1和Track 2中心对性能基础设施的接受和普及。&&

Intellectual MeritThe promise of high-performance computing (HPC) will be realized by science and engineering (S&E) applications executing on scalable HPC computer systems at the high end of their performance range. Performance optimization of S&E application codes will be achieved through a process of performance engineering, where tools for parallel performance measurement, analysis, and tuning are used productively to discover sources of performance inefficiency and remove them. Parallel performance tools research and development has created powerful techniquesfor performance observation, analysis, and optimization, and produced technology solutions that are portable, interoperable,and scalable. It is now important to transfer successful, robust parallel performance infrastructure to a performance engineering framework, integrated with HPC cyberinfrastructure and directed at documented user requirements for HPC performance problem solving. In addition, if HPC resources are to be maximized, human-centric investments must also be made to help train application developers to be good performance engineers.Broader ImpactThis performance software foundation will be complemented by a community-driven education and training initiative to increase human productivity in performance engineering efforts across multiple S&E fields. The proposed project will also create a training program for performance technology and engineering, which willbe piloted and refined at the Pittsburgh Supercomputing Center and integrated with the TeraGrid Education, Outreach,Training (EOT) program over time. This program's objectives will be to educate application developers and students in sound performance evaluation methods, to teach them best practices for engineering high-performance code solutions based on expert tuning strategies, and to train them to use the performance tools effectively. The project will develop training materials and infrastructure for distributed access, as well as institute a series of tutorials and bring your own code workshops that will be offered in-person and over the AccessGrid. In addition, application engagement will be an important component of this activity. The project will work with undergraduate and graduate students directly in performance analysis of S&E applications, and with developers of leadinglarge-scale applications to integrate performance engineering in their projects. A performance repository containing detailed characterization data for a broad set of applications and platforms will be created and made available for use across all HPC centers for performance data mining. Project success will be measured by three metrics: the improvements in application performance achieved on high-impact S&E applications, the increased performance competency of application developers across S&E domains, and the acceptance and ubiquity of the performance infrastructure among the NSF Track 1 and Track 2 centers.