EAGER: Workload Analysis of Blue Waters

蓝水是美国国家科学基金会支持的最大的领导级超级计算机。它是由伊利诺伊大学收购并运营的。它的目的是在科学和工程的广泛范围内和跨领域的“大挑战”问题中极大地推进基本理解。它通过提供适合并能够解决最具挑战性的计算和数据分析问题的计算系统和环境来实现这一点。鉴于蓝水在国家研究组合中发挥的重要而独特的作用，对其科学工作量有详细的技术了解是很重要的。布法罗的纽约州立大学（SUNY）承担了这样一项研究。这种工作负载特性将指导软件和系统配置级别的性能优化，以最大限度地提高工作性能和工作流程，并有助于为未来的计算机体系结构研究和开发提供信息。此外，分析可以具体地告知未来领导级系统部署的系统平衡权衡。该研究将利用Blue Waters之前的研究和数据，但将通过扩展nsf资助的和公开可用的XDMoD （XD Metrics on Demand）服务来整合一个全面的方法，以包括特定于Blue Waters架构的数据。这项研究的结果不仅将为Blue Waters提供详细的操作和性能分析，而且还将被用作在其他先进的高性能计算系统上进行类似研究的模板，这些系统正在彻底改变计算科学。这种“知识转移”将通过使用Open XDMoD来促进所提出的分析，它已经在世界各地的HPC中心广泛使用。此外，该项目的一个成果是修改Open XDMoD，以便从OVIS/LDMS监视框架中摄取作业级性能数据。OVIS/LDMS广泛部署在Cray HPC系统上，这一成果将使那些拥有Cray系统的中心能够充分利用Open XDMoD来提供全面的资源管理。

Blue Waters is the largest NSF-supported leadership-class supercomputer. It was acquired and is operated by the University of Illinois. Its purpose is to greatly advance fundamental understanding represented in "grand challenge" problems within and across a wide range of science and engineering. It does this by offering a computing system and environment suitable for and capable of solving the most challenging computational and data analysis problems. Given the important and unique role that Blue Waters plays in the nation's research portfolio, it is important to have a detailed technical understanding of its scientific workload. This award to State University of New York (SUNY) at Buffalo undertakes such a study. This workload characterization will guide performance optimization at the software and system configuration level to maximize job performance and workflow, as well as to help inform future computer architecture research and development. Additionally, the analysis could concretely inform the system balance trade-offs of future leadership-class system deployments. This study will leverage prior Blue Waters studies and data, but will incorporate a comprehensive approach by extending the NSF-funded and publicly available XDMoD (XD Metrics on Demand) service to include data specific to Blue Waters architecture.The results of this study will not only provide detailed operational and performance analytics for Blue Waters, but will also be used as a template for similar studies carried out on other advanced high performance computing systems that are revolutionizing computational science. This "transfer of knowledge" will be facilitated through the use of Open XDMoD for the proposed analysis, which is already in wide use by HPC centers worldwide. In addition, one of the outcomes of this project will be the modification of Open XDMoD to ingest job level performance data from the OVIS/LDMS monitoring framework. OVIS/LDMS is widely deployed on Cray HPC systems and this outcome will enable those centers with Cray systems to fully leverage Open XDMoD to provide comprehensive resource management.