Parallel Computing with Generalized Cellular Automata

Abstract

Cellular Automata (CA) are fundamental computational modes of spatial phenomena in which space is represented by discrete lattice of cells. Each cell concurrently interacts with its neighborhood which in traditional CA, limited to cell's nearest neighbors. In this paper we discuss generalized cellular automata (GCA), an important but unexplored class of CA, in which the cells interaction domain extends beyond the nearest neighbors. The computational power necessary to run a large scale CA (and GCA) models has only recently been available thanks to parallel processing. This paper focuses on implementation and performance of GCA in biological modeling. In particular we present results of simulating the spread of epidemics and creation of spatial infection pattern that are important for disease control.

The simulation system is implemented on three different platforms: the MasPar MP-1 SIMD computer, the IBM SP-2 MIMD machine and network of workstations (NOW) that consist of sun SPARC station 5 and ultra SPARC2's connected via Ethernet. The system presented in this paper has been specialized for simulating a four species spatially explicit model, however, the implementation may be readily modified to represent other models. Simulation results are presented for simple epidemics and vector borne diseases spread by parasites.