  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.
