Abstract
Among the many reasons for load imbalance in weather forecasting
models, the dynamic imbalance caused by localized variations on the
state of the atmosphere is the hardest one to handle. As an
example, active thunderstorms may substantially increase load at a
certain timestep with respect to previous timesteps in an
unpredictable manner -- after all, tracking storms is one of the
reasons for running a weather forecasting model. In this paper, we
present a comparative analysis of different load balancing
algorithms to deal with this kind of load imbalance. We analyze the
impact of these strategies on computation and communication and the
effects caused by the frequency at which the load balancer is
invoked on execution time. This is done without any code
modification, employing the concept of processor virtualization,
which basically means that the domain is over-decomposed and the
unit of rebalance is a sub-domain. With this approach, we were able
to reduce the execution time of a full, real-world weather model.