Load Balancing of the Parallel Execution of Two Dimensional Partitioned Cellular Automata

Load Balancing is generally referred as the technique to properly partition computation among processing elements in order to achieve optimal resource usage and thus reduce computation time. In this paper, we present a dynamic load balancing application in the context of the parallel execution of Cellular Automata where the domain space is partitioned in two dimensional regions that are assigned to different processing elements. Starting from general closed-form expressions that allow to compute the optimal workload assignment in a dynamic fashion when partitioning takes place along only one dimension, we extend the procedure to allow partitioning and balancing along both dimensions. As confirmed by the experimental results, two dimensional partitioning itself enables to speedup the execution, and further improvements are obtained when the load balancing occurs along both dimensions.