Using High Performance Algorithms for the Hybrid Simulation of Disease Dynamics on CPU and GPU

In the current work the authors present several approaches to the high performance simulation of human diseases propagation using hybrid two-component imitational models. The models under study were created by coupling compartmental and discrete-event submodels. The former is responsible for the simulation of the demographic processes in a population while the latter deals with a disease progression for a certain individual. The number and type of components used in a model may vary depending on the research aims and data availability. The introduced high performance approaches are based on batch random number generation, distribution of simulation runs and the calculations on graphical processor units. The emphasis was made on the possibility to use the approaches for various model types without considerable code refactoring for every particular model. The speedup gained was measured on simulation programs written in C + + and MATLAB for the models of HIV and tuberculosis spread and the models of tumor screening for the prevention of colorectal cancer. The benefits and drawbacks of the described approaches along with the future directions of their development are discussed.