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Overcoming MPI Communication Overhead for Distributed Community Detection

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Software Challenges to Exascale Computing (SCEC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 964))

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Abstract

Community detection is an important graph (network) analysis kernel used for discovering functional units and organization of a graph. Louvain method is an efficient algorithm for discovering communities. However, sequential Louvain method does not scale to the emerging large-scale network data. Parallel algorithms designed for modern high performance computing platforms are necessary to process such network big data. Although there are several shared memory based parallel algorithms for Louvain method, those do not scale to a large number of cores and to large networks. One existing Message Passing Interface (MPI) based distributed memory parallel implementation of Louvain algorithm has shown scalability to only 16 processors. In this work, first, we design a shared memory based algorithm using Open MultiProcessing (OpenMP), which shows a 4-fold speedup but is only limited to the physical cores available to our system. Our second algorithm is an MPI-based distributed memory parallel algorithm that scales to a moderate number of processors. We then implement a hybrid algorithm combining the merits from both shared and distributed memory-based approaches. Finally, we incorporate a parallel load balancing scheme, which leads to our final algorithm DPLAL (Distributed Parallel Louvain Algorithm with Load-balancing). DPLAL overcomes the performance bottleneck of the previous algorithms with improved load balancing. We present a comparative analysis of these parallel implementations of Louvain methods using several large real-world networks. DPLAL shows around 12-fold speedup and scales to a larger number of processors.

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Acknowledgements

This work has been partially supported by Louisiana Board of Regents RCS Grant LEQSF(2017-20)-RDA- 25 and University of New Orleans ORSP Award CON000000002410. We also thank the anonymous reviewers for the helpful comments and suggestions to improve this paper.

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Authors and Affiliations

  1. Department of Computer Science, University of New Orleans, New Orleans, LA, 70148, USA

    Naw Safrin Sattar & Shaikh Arifuzzaman

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  1. Naw Safrin Sattar
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  2. Shaikh Arifuzzaman
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Correspondence to Naw Safrin Sattar or Shaikh Arifuzzaman .

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Editors and Affiliations

  1. San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, USA

    Amit Majumdar

  2. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA

    Ritu Arora

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Sattar, N.S., Arifuzzaman, S. (2019). Overcoming MPI Communication Overhead for Distributed Community Detection. In: Majumdar, A., Arora, R. (eds) Software Challenges to Exascale Computing. SCEC 2018. Communications in Computer and Information Science, vol 964. Springer, Singapore. https://doi.org/10.1007/978-981-13-7729-7_6

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  • DOI: https://doi.org/10.1007/978-981-13-7729-7_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7728-0

  • Online ISBN: 978-981-13-7729-7

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