Evaluating large graph processing in MapReduce based on message passing

Abstract

Since analyzing large-scale graph is usually difficult to be implemented on a single machine, how to design efficient parallel large-scale graph algorithms is receiving more and more attention. Constrained by embarrassingly parallel assumption, parallel graph algorithms are not easy to express in MapReduce. Inspired by Bulk Synchronous Parallel model, we propose a message-enhanced version of Hadoop MapReduce that breaks its key assumption. Enhanced implementation is compatible with original Hadoop MapReduce, existing Hadoop MapReduce programs can run directly on this platform without modification, and uses message passing mechanisms to facilitate interactive data communication between supersteps of tasks. It also provides a highly flexible self-defined message passing interface and two adaptive message passing mechanisms to support efficient implementation of graph algorithms with data transition and iterative computation. The experimental results on the real Stanford large network dataset collection demonstrate the superiority of enhanced version over original Hadoop MapReduce on PageRank algorithm.