Identifying the Culprits behind Network Congestion
| Abhinav Bhatele | Andrew Titus | Jayaraman Thiagarajan | Nikhil Jain | Todd Gamblin | Peer-Timo Bremer | Martin Schulz | Laxmikant Kale
IEEE International Parallel and Distributed Processing Symposium (IPDPS) 2015
Publication Type: Paper
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Abstract
Network congestion is one of the primary causes of performance degradation, performance variability and poor scaling in communication-heavy parallel applications. However, the causes and mechanisms of network congestion on modern interconnection networks are not well understood. We need new approaches to analyze, model and predict this critical behavior in order to improve the performance of large-scale parallel applications. This paper applies supervised learning algorithms, such as forests of extremely randomized trees and gradient boosted regression trees, to perform regression analysis on communication data and application execution time. Using data derived from multiple executions, we create models to predict the execution time of communication-heavy parallel applications. This analysis also identifies the features and associated hardware components that have the most impact on network congestion and in turn, on execution time. The ideas presented in this paper have wide applicability: predicting the execution time on a different number of cores, or different input datasets, or even for an unknown code, identifying the best configuration parameters for an application, and finding the root causes of network congestion on different architectures.
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