CUDA implementation of the algorithm for simulating the epidemic spreading over large networks

Preventing and controlling of epidemics in human or computer networks is a top problem. This paper presents an implementation of the algorithm that simulates the epidemic spreading in networks, using CUDA (Compute Unified Device Architecture) technology. Spreading of the epidemics over the network is modeled by a discrete SIR (Susceptible - Infected - Recovered) model. This implementation enables selection of starting node and monitoring the epidemic spread in each cycle. In comparison to a common CPU implementation, the CUDA implementation achieves about 10× faster execution time, which is of high significance to running tests on larger networks. The implementation was tested on real social networks consisting of more than 5 million nodes. Hence, we belive this implementation can have a practical value in analysis of the epidemic spreading over large networks.