Parallel Cellular Automaton Tumor Growth Model

“In silico” experimentation allows us to simulate the effect of different therapies by handling model parameters. Although the computational simulation of tumors is currently a well-known technique, it is however possible to contribute to its improvement by parallelizing simulations on computer systems of many and multi-cores. This work presents a proposal to parallelize a tumor growth simulation that is based on cellular automata by partitioning of the data domain and by dynamic load balancing. The initial results of this new approach show that it is possible to successfully accelerate the calculations of a known algorithm for tumor-growth.