Efficient model of tumor dynamics simulated in multi-GPU environment

The application of computer simulation as a tool in predicting cancer dynamics (e.g. during anticancer therapy) requires tumor models, which are nontrivial and, simultaneously, not computationally demanding. To this end, both the level of details and computational efficiency of the model should be well balanced. The restrictions on computational time are forced by very demanding data assimilation process in the phase of parameters learning and their correction on the basis of incoming medical data. Herein we present a very efficient multi-GPU/CUDA implementation of three-dimensional (3-D) cancer model which allows for simulating both the growth and treatment phases of tumor dynamics. We demonstrate that the interaction between the tissue and the discrete network of blood vessels is a crucial component, which influences considerably the simulation time. Here we present a new solution which eliminates this flaw. We show also that the efficiency of our model does not depend on the complexity of tumor setup. As an example, we confront the growth of tumor in a simple and homogeneous environment with melanoma evolution, which proliferates in a complex environment of human skin. Consequently, the 3-D simulation of a tumor growth up to 1 cm in diameter requires an hour of computations on a midrange multi-GPU server.