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Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions

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High Performance Computing (ISC High Performance 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10266))

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Abstract

Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical N-Body algorithms like Fast Multipole Method (FMM). In the present work, we propose three independent strategies to improve partitioning and reduce communication. First, we show that the conventional wisdom of using space-filling curve partitioning may not work well for boundary integral problems, which constitute a significant portion of FMM’s application user base. We propose an alternative method that modifies orthogonal recursive bisection to relieve the cell-partition misalignment that has kept it from scaling previously. Secondly, we optimize the granularity of communication to find the optimal balance between a bulk-synchronous collective communication of the local essential tree and an RDMA per task per cell. Finally, we take the dynamic sparse data exchange proposed by Hoefler et al. [1] and extend it to a hierarchical sparse data exchange, which is demonstrated at scale to be faster than the MPI library’s MPI_Alltoallv that is commonly used.

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Notes

  1. 1.

    ExaFMM is an open-source code base to utilize fast multipole algorithms, in parallel, and with GPU capability. Algorithms pertaining to partitioning and communication reduction are all available on the public repository https://github.com/exafmm/exafmm.

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Acknowledgment

This work was supported by JSPS KAKENHI Grant-in-Aid for Young Scientists A Grant Number 16H05859. This work is partially supported by “Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures” and “High Performance Computing Infrastructure” in Japan. The authors are grateful to the KAUST Supercomputing Laboratory for the use of the Shaheen XC40 system.

Author information

Authors and Affiliations

  1. Extreme Computing Research Center (ECRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Mustafa Abduljabbar, Huda Ibeid & David Keyes

  2. KAUST Supercomputing Laboratory (KSL), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    George S. Markomanolis

  3. Global Scientific Information and Computing Center (GSIC), Tokyo Institute of Technology (TITECH), Tokyo, Japan

    Rio Yokota

Authors
  1. Mustafa Abduljabbar
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  2. George S. Markomanolis
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  3. Huda Ibeid
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  4. Rio Yokota
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  5. David Keyes
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Corresponding author

Correspondence to Mustafa Abduljabbar .

Editor information

Editors and Affiliations

  1. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Germany

    Julian M. Kunkel

  2. Tokyo Institute of Technology, Tokyo, Japan

    Rio Yokota

  3. Argonne National Laboratory, Argonne, IL, USA

    Pavan Balaji

  4. KAUST, Thuwal, Saudi Arabia

    David Keyes

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Abduljabbar, M., Markomanolis, G.S., Ibeid, H., Yokota, R., Keyes, D. (2017). Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. In: Kunkel, J.M., Yokota, R., Balaji, P., Keyes, D. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10266. Springer, Cham. https://doi.org/10.1007/978-3-319-58667-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-58667-0_5

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  • Online ISBN: 978-3-319-58667-0

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