UC Irvine

Individual-based simulations are an important class of applications where a complex system is modeled as a collection of autonomous entities, each having its own identify and behavior in the underlying simulated space. The main drawback of such simulations is that they are extremely compute-intensive. We consider the class of individual-based simulations where the simulated entities interact with one another indirectly through the underlying simulated space, significant performance improvement is attainable through parallelism on a network of machines. We present a data distribution and an approach to reduce the communication overhead, which leads to significant performance improvements while preserving the accuracy of the simulation. © 2009 Springer.