Abstract: 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 in this paper 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.
