Using Dynamic Parallelism to Speed Up Clustering-Based Community Detection in Social Networks

Abstract

Social Network Analysis (SNA) has been gaining a lot of attention lately. One of the common steps in SNA is community detection. SNA literature has many interesting algorithms for community detection. One of the popular ones was proposed by Newman and it is mainly revolved around using a clustering algorithm. Three phases are iteratively applied in this algorithm in order to find the "best" community structure. These phases are: spectral mapping, clustering and modularity computation. Despite its effectiveness, this method suffers greatly in terms of running time when dealing with largescale networks. A parallel implementation using GPUs is one of the feasible solutions to address this problem. Moreover, due to the iterative nature of this algorithm, dynamic parallelism lends itself as a very appealing solution. Dynamic parallelism is a novel parallel programming technique that refers to the ability to launch new grids from the GPU. In this work, we present three implementation of the clustering-based community detection algorithm. In addition to the sequential implementation, we present two implementations: a Hybrid CPU-GPU (HCG) one and a Dynamic Parallel (DP) one. We test our parallel implementations on benchmark datasets to show the speed-up of each parallel implementation compared with the sequential one. The results show that the DP implementation achieves good speed-ups reaching up to 4.45X, however, the speed-ups achieved by HCG are almost twice as much.