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

Analyzing Performance Properties Collected by the PerSyst Scalable HPC Monitoring Tool