HPC Simulations of Information Propagation Over Social Networks

imulations provide a flexible and valuable method to study the behaviors of information propagation over complex social networks. High Performance Computing (HPC) is a technology that allows the implementation of efficient algorithms on powerful new hardware resources. With the increased computing resource usage in large-scale network based simulations, it is therefore attractive to apply the emerging HPC techniques to improve the simulation performance. This paper describes optimized simulation strategies based on algorithmic adaptation at runtime, which can facilitate the performance improvement of execution. In addition, the proposed optimization method is demonstrated on HPC architectures such as Multicore CPU and General Purpose GPU (GPGPU). Such a high performance simulation approach is evaluated by a range of experiments on different network structures. The experimental results show that we can obtain significant performance gain by an optimized algorithmic adaptation strategy in a serial CPU execution environment. Furthermore, the proposed optimization method is implemented on AMD Quad-core CPU and Nvidia Fermi C2050 GPGPU for performance acceleration