Predicting application performance using supervised learning on communication features

Task mapping on torus networks has traditionally focused on either reducing the maximum dilation or average number of hops per byte for messages in an application. These metrics make simplified assumptions about the cause of network congestion, and do not provide accurate correlation with execution time. Hence, these metrics cannot be used to reasonably predict or compare application performance for different mappings. In this paper, we attempt to model the performance of an application using communication data, such as the communication graph and network hardware counters. We use supervised learning algorithms, such as randomized decision trees, to correlate performance with prior and new metrics. We propose new hybrid metrics that provide high correlation with application performance, and may be useful for accurate performance prediction. For three different communication patterns and a production application, we demonstrate a very strong correlation between the proposed metrics and the execution time of these codes.