  In recent years, several HPC facilities have started continuous monitoring of
their systems and jobs to collect performance-related data for understanding
performance and operational efficiency. Such data can be used to optimize the
performance of individual jobs and the overall system by creating data-driven
models that can predict the performance of jobs waiting in the scheduler queue.
In this paper, we model the performance of representative control jobs using
longitudinal system-wide monitoring data and machine learning to explore the
causes of performance variability. We analyze these prediction models in great
detail to identify the features that are dominant predictors of performance. We
demonstrate that such models can be application-agnostic and can be used for
predicting performance of applications that are not included in training.
