The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques to predict the performance of similar scientific codes.

中文翻译：

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分析 PerSyst 可扩展 HPC 监控工具收集的性能属性

了解科学应用程序如何在大型 HPC 系统上执行的能力对于在 HPC 数据中心内分配资源非常重要。在本文中，我们描述了我们如何使用系统性能数据来识别：执行模式、可能的代码优化和系统监控的改进。我们还确定了采用机器学习技术来预测类似科学代码性能的候选者。