Abstract:
Partitioning and load balancing are issues of great interest in distributed simulations based on spatially explicit individual-oriented models. The decomposition of the problem domain and the efficient data distribution on the computing nodes of the parallel architecture / distributed are crucial factors in performance figures for distributed simulation.
In this work we have developed a new methodology for partitioning and load balancing for large-scale distributed simulations of individual-oriented models that show spatially explicit movement patterns. In order to validate our strategies, the model of Huth & Wissel, which represents the coordinated and polarized movement of fish, has been used.
The partitioning method is to decompose the problem domain into compact partitions generated from the radial blanket approach and Voronoi diagrams. The distribution of partitions is performed by means of the proximity cluster partitions using a new definition meta-partitions equals to the number of computing cores. The strategy for dynamic load balancing is to detect the imbalance through an algorithm based on thresholds and re–configure meta-partitions to achieve rebalancing. Finally, there has been extensive experimentation to validate and verify the viability of the distributed simulation in different scenarios.