Abstract
Besides generating faster solutions, parallel computers can be used to solve bigger or more complex problems. In particular, they can be utilized to run simulations at finer resolutions and to model physical phenomena more realistically. We describe in this article the development of a parallel cellular automata algorithm used in the three-dimensional simulation of multicellular tissue growth. Computational models of this genre are becoming ever more important because they provide an alternative approach to continuous models and an ability to describe the dynamics of complex biological systems evolving in time. We report on the different components of the model where cellular automata is used to model different types of cell populations that execute persistent random walks on the computational grid, collide, aggregate, and proliferate until they reach confluence. We elaborate on the main issues encountered in the parallelization of the algorithm as well as its implementation on a parallel machine with a particular focus on maintaining determinism. By delaying the exchange of cells in the shared boundaries between neighboring processors till after all events related to cell division and motion are accounted for in a given time step, good parallel performance results are obtained on a high-performance cluster.