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.
- 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 Scholar
Digital Library
- B. S. Siegell and P. A. Steenkiste. Automatic selection of load balancing parameters using compile-time and run-time information, 1996.Google Scholar
Index Terms
POSTER: Automated Load Balancer Selection Based on Application Characteristics
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