Abstract
With the increase of scale and complexity seen in a variety of leadership-class scientific computation and simulation applications, it has become more important to understand their I/O performance characteristics. The user-observed performance is a combination of properties of how the application is using the HPC facility, as well as how others' use of the facility causes variability in the static machine capabilities. Our work leverages statistical analysis of I/O performance data gathered with fine time resolution over a full week from Titan supercomputer. Based on observed properties of the distribution of I/O latencies, we build a three-state hidden Markov model (HMM) to characterize the end-to-end I/O performance on Titan. We parameterize our model using part of the field-gathered I/O performance data and validate it against the rest. The validation results demonstrate that our model can capture the dynamics of end-to-end I/O performance on Titan accurately.
