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POSTER: Automated Load Balancer Selection Based on Application Characteristics

Published:26 January 2017Publication History
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Abstract

Many HPC applications require dynamic load balancing to achieve high performance and system utilization. Different applications have different characteristics and hence require different load balancing strategies. Invocation of a suboptimal load balancing strategy can lead to inefficient execution. We propose Meta-Balancer, a framework to automatically decide the best load balancing strategy. It employs randomized decision forests, a machine learning method, to learn a model for choosing the best load balancing strategy for an application represented by a set of features that capture the application characteristics.

References

  1. O. Pearce, T. Gamblin, B. R. de Supinski, M. Schulz, and N. M. Amato. Quantifying the effectiveness of load balance algorithms. In 26th ACM international conference on Supercomputing, ICS '12, pages 185--194, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B. S. Siegell and P. A. Steenkiste. Automatic selection of load balancing parameters using compile-time and run-time information, 1996.Google ScholarGoogle Scholar

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  1. POSTER: Automated Load Balancer Selection Based on Application Characteristics

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    • Published in

      cover image ACM SIGPLAN Notices
      ACM SIGPLAN Notices  Volume 52, Issue 8
      PPoPP '17
      August 2017
      442 pages
      ISSN:0362-1340
      EISSN:1558-1160
      DOI:10.1145/3155284
      Issue’s Table of Contents
      • cover image ACM Conferences
        PPoPP '17: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
        January 2017
        476 pages
        ISBN:9781450344937
        DOI:10.1145/3018743

      Copyright © 2017 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 January 2017

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