Big Data applications have gained importance over the last few years. Such applications focus on the analysis of huge amounts of unstructured information and present a series of differences with traditional High Performance Computing (HPC) applications. For illustrating such dissimilarities, this paper analyzes the behavior of the most scalable version of the Graph500 benchmark when run on a state-of-the-art commodity cluster facility. Our work shows that this new computation paradigm stresses the interconnection subsystem. In this work, we provide both analytical and empirical characterizations of the Graph500 benchmark, showing that its communication needs bound the achieved performance on a cluster facility. Up to our knowledge, our evaluation is the first to consider the impact of message aggregation on the communication overhead and explore a tradeoff that diminishes benchmark execution time, increasing system performance.

This work has been supported by the Spanish Science and Technology Commission (CICYT) under contract TIN2010-21291-C02-02, the European Unions FP7 under Agreements ERC-321253 (RoMoL) and ICT-288777 (Mont-Blanc) and by the European HiPEAC Network of Excellence. This project has also been partially funded by the JSA no. 2013 119 as part of the IBM/BSC Technology Center for Supercomputing agreement.

Peer Reviewed