Automated Load Balancer Selection Based on Application Characteristics

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.