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

SDCI HPC 改进：NSF HPC 应用程序的高生产率性能工程（工具、方法、培训）

• 批准号: 0722072
• 负责人: Allen Malony
• 金额: $ 73.14万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-11-01 至 2011-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0722072&HistoricalAwards=false
• 关键词: SDCI; HPC; Improvement; Productivity; Performance

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.

智力优势高性能计算 (HPC) 的承诺将通过在可扩展 HPC 计算机系统上以其性能范围的高端执行的科学与工程 (S&E) 应用程序来实现。 S&E 应用程序代码的性能优化将通过性能工程过程来实现，其中有效地使用并行性能测量、分析和调优工具来发现性能低效的根源并消除它们。并行性能工具的研究和开发创造了强大的性能观察、分析和优化技术，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、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，并针对解决 HPC 性能问题的记录的用户需求。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 该性能软件基础将得到社区驱动的教育和培训计划的补充，以提高跨多个 S&E 领域的性能工程工作的人类生产力。拟议项目还将创建一个性能技术和工程培训计划，该计划将在匹兹堡超级计算中心进行试点和完善，并随着时间的推移与 TeraGrid 教育、推广、培训 (EOT) 计划相结合。该计划的目标是对应用程序开发人员和学生进行良好的性能评估方法的教育，教他们基于专家调优策略设计高性能代码解决方案的最佳实践，并培训他们有效地使用性能工具。该项目将开发用于分布式访问的培训材料和基础设施，并制定一系列教程并举办您自己的代码研讨会，这些研讨会将亲自通过 AccessGrid 提供。此外，应用程序参与将是该活动的重要组成部分。该项目将直接与本科生和研究生合作进行科学与工程应用程序的性能分析，并与领先的大规模应用程序的开发人员合作，将性能工程集成到他们的项目中。将创建一个性能存储库，其中包含广泛的应用程序和平台的详细特征数据，并可供所有 HPC 中心使用以进行性能数据挖掘。项目的成功将通过三个指标来衡量：高影响力的 S&E 应用程序的应用程序性能改进、S&E 领域应用程序开发人员性能能力的提高以及 NSF Track 1 和 Track 2 中心中性能基础设施的接受度和普遍性。