Automated load Balancer selection based on application characteristics

Harshitha Menon, Kavitha Chandrasekar, Laxmikant V. Kale

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationPPoPP 2017 - Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
PublisherAssociation for Computing Machinery
Pages447-448
Number of pages2
ISBN (Electronic)9781450344937
DOIs
StatePublished - Jan 26 2017
Event22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2017 - Austin, United States
Duration: Feb 4 2017Feb 8 2017

Publication series

NameProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP

Other

Other22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2017
CountryUnited States
CityAustin
Period2/4/172/8/17

ASJC Scopus subject areas

  • Software

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    Menon, H., Chandrasekar, K., & Kale, L. V. (2017). Automated load Balancer selection based on application characteristics. In PPoPP 2017 - Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 447-448). (Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP). Association for Computing Machinery. https://doi.org/10.1145/3018743.3019033