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In recent years, several HPC facilities have started continuous monitoring oftheir systems and jobs to collect performance-related data for understandingperformance and operational efficiency. Such data can be used to optimize theperformance of individual jobs and the overall system by creating data-drivenmodels that can predict the performance of pending jobs. In this paper, wemodel the performance of representative control jobs using longitudinalsystem-wide monitoring data to explore the causes of performance variability.Using machine learning, we are able to predict the performance of unseen jobsbefore they are executed based on the current system state. We analyze theseprediction models in great detail to identify the features that are dominantpredictors of performance. We demonstrate that such models can beapplication-agnostic and can be used for predicting performance of applicationsthat are not included in training.

Analytics of Longitudinal System Monitoring Data for Performance Prediction