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Workshop on OpenSHMEM and Related Technologies

OpenSHMEM 2017: OpenSHMEM and Related Technologies. Big Compute and Big Data Convergence pp 65–81Cite as

Parallelizing Single Source Shortest Path with OpenSHMEM

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10679))

Abstract

Single Source Shortest Path (SSSP) is one of the widely occurring graph problems where the paths are discovered from an origin vertex to all other vertices in the graph. In this paper, we discuss our experience parallelizing SSSP using OpenSHMEM. We start with the serial Dijkstra and Bellman-Ford algorithms, parallelize these algorithms, and adapt them to the Partitioned Global Address Space (PGAS) programming model. We implement the parallel algorithms using OpenSHMEM and introduce a series of optimizations to achieve higher scaling and performance characteristics. The implementation is evaluated on Titan with various graphs including synthetic Recursive Matrix (R-MAT) and small-world network graphs as well as real-world graphs from Facebook, Twitter, LiveJournal, and the road maps of California and Texas.

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References

  1. Lumsdaine, A., Gregor, D., Hendrickson, B., Berry, J.: Challenges in parallel graph processing. Parallel Process. Lett. 17, 5–20 (2007)

    Article  MathSciNet  Google Scholar 

  2. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bellman, R.: On a routing problem. Q. Appl. Math. 16, 87–90 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ford, L.A.: Network flow theory. Technical report P-923, The Rand Corporation (1956)

    Google Scholar 

  5. Edmonds, N., Breuer, A., Gregor, D.P., Lumsdaine, A.: Single-source shortest paths with the parallel boost graph library. In: The Shortest Path Problem, Proceedings of a DIMACS Workshop, Piscataway, New Jersey, USA, 13–14 November 2006, pp. 219–248 (2006)

    Google Scholar 

  6. Crauser, A., Mehlhorn, K., Meyer, U., Sanders, P.: A parallelization of Dijkstra’s shortest path algorithm. In: Brim, L., Gruska, J., Zlatuška, J. (eds.) MFCS 1998. LNCS, vol. 1450, pp. 722–731. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0055823

    Chapter  Google Scholar 

  7. Crauser, A., Mehlhorn, K., Meyer, U., Sanders, P.: Parallelizing Dijkstra’s shortest path algorithm. Technical report, MPI-Informatik (1998)

    Google Scholar 

  8. Plimpton, S.J., Devine, K.D.: Mapreduce in MPI for large-scale graph algorithms. Parallel Comput. 37, 610–632 (2011)

    Article  Google Scholar 

  9. Chakaravarthy, V.T., Checconi, F., Murali, P., Petrini, F., Sabharwal, Y.: Scalable single source shortest path algorithms for massively parallel systems. IEEE Trans. Parallel Distrib. Syst. 28, 2031–2045 (2017)

    Article  Google Scholar 

  10. Meyer, U., Sanders, P.: Delta-stepping: a parallelizable shortest path algorithm. J. Algorithms 49, 114–152 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  11. Maleki, S., Nguyen, D., Lenharth, A., Garzarán, M., Padua, D., Pingali, K.: DSMR: a shared and distributed memory algorithm for single-source shortest path problem. SIGPLAN Not. 51, 39:1–39:2 (2016)

    Google Scholar 

  12. Martín, P.J., Torres, R., Gavilanes, A.: CUDA solutions for the SSSP problem. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009. LNCS, vol. 5544, pp. 904–913. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01970-8_91

    Chapter  Google Scholar 

  13. Davidson, A., Baxter, S., Garland, M., Owens, J.D.: Work-efficient parallel GPU methods for single-source shortest paths. In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp. 349–359 (2014)

    Google Scholar 

  14. Busato, F., Bombieri, N.: An efficient implementation of the Bellman-Ford algorithm for Kepler GPU architectures. IEEE Trans. Parallel Distrib. Syst. 27, 2222–2233 (2016)

    Article  Google Scholar 

  15. Leskovec, J., Krevl, A.: SNAP datasets: Stanford large network dataset collection (2014). http://snap.stanford.edu/data

  16. Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: a recursive model for graph mining, pp. 442–446 (2004)

    Google Scholar 

  17. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  MATH  Google Scholar 

  18. Bader, D.A., Berry, J., Kahan, S., Murphy, R., Riedy, E.J., Willcock, J.: Graph 500 Benchmark Specification 1.2 (2017)

    Google Scholar 

Download references

Acknowledgements

This research was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. Additionally, this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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Authors and Affiliations

  1. Computer Science and Mathematics Division, Oak Ridge National Laboratory (ORNL), Oak Ridge, USA

    Ferrol Aderholdt & Manjunath Gorentla Venkata

  2. Tennessee Tech University (TTU), Cookeville, USA

    Jeffrey A. Graves

Authors
  1. Ferrol Aderholdt
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  2. Jeffrey A. Graves
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  3. Manjunath Gorentla Venkata
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Correspondence to Ferrol Aderholdt .

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Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Manjunath Gorentla Venkata

  2. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Neena Imam

  3. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Swaroop Pophale

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Aderholdt, F., Graves, J.A., Venkata, M.G. (2018). Parallelizing Single Source Shortest Path with OpenSHMEM. In: Gorentla Venkata, M., Imam, N., Pophale, S. (eds) OpenSHMEM and Related Technologies. Big Compute and Big Data Convergence. OpenSHMEM 2017. Lecture Notes in Computer Science(), vol 10679. Springer, Cham. https://doi.org/10.1007/978-3-319-73814-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-73814-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73813-0

  • Online ISBN: 978-3-319-73814-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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