Massive Parallelism with Gpus for Centrality Ranking in Complex Networks

Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex networks, which can be considered equivalent to undirected graphs, provides an useful metric of the relative importance of each node inside the evaluated network. The knowledge on which the most central nodes are, has various applications, such as improving information spreading in diffusion networks. In this case, most central nodes can be considered to have higher influence rates over other nodes in the network. The main purpose in this work is developing a GPU based and massively parallel application so as to evaluate the node centrality in complex networks using the Nvidia CUDA programming model. The main contribution of this work is the strategies for the development of an algorithm to evaluate the node centrality in complex networks using Nvidia CUDA parallel programming model. We show that the strategies improves algorithm's speed-up in two orders of magnitude on one NVIDIA Tesla k20 GPU cluster node, when compared to the hybrid OpenMP/MPI algorithm version, running in the same cluster, with 4 nodes 2 Intel(R) Xeon(R) CPU E5-2660 each, for radius zero.