Performance Comparison of Graph BFS Implemented in MapReduce and PGAS Programming Models

  • Magdalena RyczkowskaEmail author
  • Marek Nowicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)


Computations based on graphs are very common problems but complexity, increasing size of analyzed graphs and a huge amount of communication make this analysis a challenging task. In this paper, we present a comparison of two parallel BFS (Breath-First Search) implementations: MapReduce run on Hadoop infrastructure and in PGAS (Partitioned Global Address Space) model. The latter implementation has been developed with the help of the PCJ (Parallel Computations in Java) - a library for parallel and distributed computations in Java. Both implementations realize the level synchronous strategy - Hadoop algorithm assumes iterative MapReduce jobs, whereas PCJ uses explicit synchronization after each level. The scalability of both solutions is similar. However, the PCJ implementation is much faster (about 100 times) than the MapReduce Hadoop solution.


High performance computing Hadoop MapReduce PGAS Parallel and distributed computation Performance evaluation Parallel graph algorithms Java 



This work has been performed using the PL-Grid infrastructure. The authors would like to thank CHIST-ERA consortium for financial support under HPDCJ project (Polish part funded by NCN grant 2014/14/Z/ST6/00007).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Interdisciplinary Centre for Mathematical and Computational ModelingUniversity of WarsawWarsawPoland
  2. 2.Faculty of Mathematics and Computer ScienceNicolaus Copernicus UniversityTorunPoland

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