2020/09/13 by Brayford, David, Bernau, Christoph, Hesse, Wolfram +1
#Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.2009.06061
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.