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.
['Harshitha Menon', 'Kavitha Chandrasekar', 'Laxmikant V. Kalé']
POSTER: Automated Load Balancer Selection Based on Application Characteristics