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