Global graphs: A middleware for large scale graph processing

Modern graphs are large and often display the well known power-law property. Graphs with millions of vertices and edges are becoming commonplace. All these facts pose significant challenges in processing real graphs. Space efficient representation, scalable distributed processing and ease of programming are some of the most critical capabilities sought after by researchers for dealing with such large graphs. In this paper we present Global Graphs, a distributed memory middleware for easy and efficient processing of large graphs. Global Graphs provides the ease of “shared memory” programming while maintaining the scalability of “distributed memory” programming. Global Graphs comes with parallel implementations of numerous important algorithms including Regularized Markov Clustering (RMCL), a popular algorithm for clustering large graphs. Our experiments on real graphs and large systems show good performance of Global Graphs.