Adaptive load balancing of parallel applications with multi-agent reinforcement learning on heterogeneous systems

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered here are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.