Document Type
Conference Proceeding
Publication Date
1-1-2013
Publication Title
International Conference for High Performance Computing, Networking, Storage and Analysis, SC
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.
Keywords
Contention, Modeling, Prediction, Supervised learning, Task mapping, Torus networks
DOI
10.1145/2503210.2503263
ISSN
21674329
Rights
Copyright 2013 ACM.
Recommended Citation
Jain, Nikhil; Bhatele, Abhinav; Robson, Michael P.; Gamblin, Todd; and Kale, Laxmikant V., "Predicting application performance using supervised learning on communication features" (2013). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/359
Comments
Archived as published.