Predicting Application Performance using Supervised Learning on Communication Features
International Conference for High Performance Computing, Networking, Storage and Analysis (SC) 2013
Publication Type: Talk
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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 a perfect correlation with execution time. Hence, these metrics, when derived offline for different mappings using simulations, 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 by using communication data, such as the communication graph and network hardware counters. We use supervised learning algorithms, such as forests of randomized decision trees, to correlate performance with prior and new metrics and their combinations. We propose new hybrid metrics that provide high correlation with application performance. For three different communication patterns and a production application, we demonstrate a very strong correlation between the new proposed metrics and the execution time of these codes.
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