Scalable performance of ScaleGraph for large scale graph analysis

Abstract

Scalable analysis of massive graphs has become a challenging issue in high performance computing environments. ScaleGraph is an X10 library aimed for large scale graph analysis scenarios. This paper evaluates scalability of ScaleGraph library for degree distribution calculation, betweeness centrality, and spectral clustering algorithms. We make scalability evaluation by analyzing a synthetic Kronecker graph with 40.3 million edges (for all the three algorithms), and a real social network with 69 million edges (for degree distribution calculation) on Tsubame 2.0 distributed memory environment.