SC13 Home > SC13 Schedule > SC13 Presentation - Predicting Application Performance Using Supervised Learning on Communication Features

SCHEDULE: NOV 16-22, 2013

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Predicting Application Performance Using Supervised Learning on Communication Features

SESSION: Application Performance Characterization

EVENT TYPE: Papers

TIME: 4:00PM - 4:30PM

SESSION CHAIR: Yao Zhang

AUTHOR(S):Nikhil Jain, Abhinav Bhatele, Michael Robson, Todd Gamblin, Laxmikant Kale

ROOM:405/406/407

ABSTRACT:
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.

Chair/Author Details:

Yao Zhang (Chair) - Argonne National Laboratory

Nikhil Jain - University of Illinois at Urbana-Champaign

Abhinav Bhatele - Lawrence Livermore National Laboratory

Michael Robson - University of Illinois at Urbana-Champaign

Todd Gamblin - Lawrence Livermore National Laboratory

Laxmikant Kale - University of Illinois at Urbana-Champaign

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The full paper can be found in the ACM Digital Library