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A Decomposition Algorithm for the All-Pairs Shortest Path Problem on Massively Parallel Computer Architectures

Author

Listed:
  • Mayiz B. Habbal

    (GTE Laboratories, Inc., Waltham, Massachusetts)

  • Haris N. Koutsopoulos

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

  • Steven R. Lerman

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Network optimization on parallel computer architectures has attracted significant interest in recent years. In this paper, we examine the solution of the shortest path problem on massively parallel architectures. We propose a network decomposition strategy that is amenable to parallel implementation and suggest efficient data structures and mappings of the data to the processors that facilitate the solution of the problem. We discuss computational results for the solution of networks of various sizes on the Connection Machine CM-2 (a representative massively parallel architecture) and compare the performance of the algorithm to the performance of serial algorithms implemented on the CRAY X-MP supercomputer and VAXstation 3100. We also present an “idealized” analysis of the algorithm and draw conclusions on properties of decomposition strategies that optimize its performance.

Suggested Citation

  • Mayiz B. Habbal & Haris N. Koutsopoulos & Steven R. Lerman, 1994. "A Decomposition Algorithm for the All-Pairs Shortest Path Problem on Massively Parallel Computer Architectures," Transportation Science, INFORMS, vol. 28(4), pages 292-308, November.
    • Handle: RePEc:inm:ortrsc:v:28:y:1994:i:4:p:292-308
    DOI: 10.1287/trsc.28.4.292
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    Cited by:

    1. Yu-Li Chou & H. Edwin Romeijn & Robert L. Smith, 1998. "Approximating Shortest Paths in Large-Scale Networks with an Application to Intelligent Transportation Systems," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 163-179, May.
    2. Ziliaskopoulos, Athanasios K. & Mandanas, Fotios D. & Mahmassani, Hani S., 2009. "An extension of labeling techniques for finding shortest path trees," European Journal of Operational Research, Elsevier, vol. 198(1), pages 63-72, October.
    3. Fengqin Tang & Chunning Wang & Jinxia Su & Yuanyuan Wang, 2020. "Spectral clustering-based community detection using graph distance and node attributes," Computational Statistics, Springer, vol. 35(1), pages 69-94, March.

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