GP-GPU Computing for Analysis of Large Scale networks

Abstract

Over last few years the interest in large scale network processing has gained momentum due to the increased importance of efficient processing of such networks for the analysis and problem solving in Social networks, Internet of Things(IoT), Data mining, Biological networks etc.. The steep increase in volume of data being produced, necessitates the development of parallel and distributed graph processing algorithms that demand extensive computation. Now a days, because of high-computing-power of GP-GPUs (General Purpose Graphic Processor Unit) and its cost-effectiveness, GP-GPUs are being widely used in almost all areas of computing. In this paper, we address the problem of designing efficient parallel graph processing algorithms for GP-GPU platform which is the upcoming trend in high end machines. Presently, the problem of community detection and its influence on misinformation containment in online social networks have been investigated thoroughly, and parallel CUDA algorithms have been proposed. Simulation studies show a significant speed-up compared to the existing sequential/parallel techniques.