The Case of Performance Variability on Dragonfly-based Systems

Abstract

Performance of a parallel code running on a large supercomputer can vary significantly from one run to another even when the executable and its input parameters are left unchanged. Such variability can occur due to perturbation of the computation and/or communication in the code. In this paper, we investigate the case of performance variability arising due to network effects on supercomputers that use a dragonfly topology – specifically, Cray XC systems equipped with the Aries interconnect. We perform post-mortem analysis of network hardware counters, profiling output, job queue logs, and placement information, all gathered from periodic representative application runs. We investigate the causes of performance variability using deviation prediction and recursive feature elimination. Additionally, using time-stepped performance data of individual applications, we train machine learning models that can forecast the execution time of future time steps.