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Analyzing Communication Features and Community Structure of HPC Applications

  • Manfred Calvo
  • Diego JiménezEmail author
  • Esteban Meneses
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 979)

Abstract

A few exascale machines are scheduled to become operational in the next couple of years. Reaching such achievement required the HPC community to overcome obstacles in programmability, power management, memory hierarchy, and reliability. Similar challenges are to be faced in the pursuit of greater performance gains. In particular, design of interconnects stands out as a major hurdle. Computer networks for extreme-scale system will need a deeper understanding of the communication characteristics of applications that will run on those systems. We analyzed a set of nine representative HPC applications and created a catalog of well-defined communication patterns that constitute building blocks for modern scientific codes. Furthermore, we found little difference between popular community-detection algorithms, which tend to form few but relatively big communities.

Keywords

Communication patterns High performance computing Graph partitioning Community structure detection Application characterization Message Passing Interface (MPI) 

Notes

Acknowledgments

This research was partially supported by a machine allocation on Kabré supercomputer at the Costa Rica National High Technology Center.

References

  1. 1.
    igraph: The network analysis package (2015). http://igraph.org/
  2. 2.
    Almeida, H., Guedes, D., Meira, W., Zaki, M.J.: Is there a best quality metric for graph clusters? In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6911, pp. 44–59. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23780-5_13CrossRefGoogle Scholar
  3. 3.
    Barrett, R., et al.: On the role of co-design in high performance computing, vol. 24, pp. 141–155 (2013)Google Scholar
  4. 4.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Brightwell, R., Barrett, B.W., Hemmert, K.S., Underwood, K.D.: Challenges for high-performance networking for exascale computing. In: 2010 Proceedings of 19th International Conference on Computer Communications and Networks, pp. 1–6, August 2010Google Scholar
  6. 6.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  7. 7.
    CORAL: Collaboration of Oak Ridge, Argonne and Livermore benchmark codes. https://asc.llnl.gov/CORAL-benchmarks
  8. 8.
    Dongarra, J., et al.: The international exascale software project roadmap (2011)Google Scholar
  9. 9.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Heroux, M.A., et al.: Improving performance via mini-applications. Technical report SAND2009-5574, Sandia National Laboratories (2009)Google Scholar
  11. 11.
    Hoefler, T., Snir, M.: Generic topology mapping strategies for large-scale parallel architectures. In: Proceedings of the 2011 ACM International Conference on Supercomputing (ICS 2011), pp. 75–85. ACM, June 2011Google Scholar
  12. 12.
    Hoefler, T., Jeannot, E., Mercier, G.: An overview of process mapping techniques and algorithms in high-performance computing (2014)Google Scholar
  13. 13.
    Kogge, P., et al.: Exascale computing study: technology challenges in achieving exascale systems (2008)Google Scholar
  14. 14.
    Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, pp. 631–640 (2010)Google Scholar
  15. 15.
  16. 16.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)CrossRefGoogle Scholar
  17. 17.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  19. 19.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. U.S.A. 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  20. 20.
    Raponi, P.G., Petrini, F., Walkup, R., Checconi, F.: Characterization of the communication patterns of scientific applications on blue gene/p. In: 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp. 1017–1024 (2011)Google Scholar
  21. 21.
    Riesen, R.: Communication patterns [message-passing patterns]. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006, 8 pp. IEEE (2006)Google Scholar
  22. 22.
    Ropars, T., Guermouche, A., Uçar, B., Meneses, E., Kalé, L.V., Cappello, F.: On the use of cluster-based partial message logging to improve fault tolerance for MPI HPC applications. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 567–578. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23400-2_53CrossRefGoogle Scholar
  23. 23.
    Roth, P.C., Meredith, J.S., Vetter, J.S.: Automated characterization of parallel application communication patterns. In: Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing, pp. 73–84. ACM (2015)Google Scholar
  24. 24.
    Vetter, J.S., et al.: Quantifying architectural requirements of contemporary extreme-scale scientific applications. In: Jarvis, S.A., Wright, S.A., Hammond, S.D. (eds.) PMBS 2013. LNCS, vol. 8551, pp. 3–24. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10214-6_1CrossRefGoogle Scholar
  25. 25.
    Vetter, J.S., Mueller, F.: Communication characteristics of large-scale scientific applications for contemporary cluster architectures. J. Parallel Distrib. Comput. 63(9), 853–865 (2003)CrossRefGoogle Scholar
  26. 26.
    Vetter, J.S., Yoo, A.: An empirical performance evaluation of scalable scientific applications. In: ACM/IEEE 2002 Conference on Supercomputing, p. 16. IEEE (2002)Google Scholar
  27. 27.
    Vetter, J., Chambreau, C.: mpIP: lightweight, scalable MPI profling (2014). http://mpip.sourceforge.net/
  28. 28.
    Xue, R., et al.: MPIWiz: subgroup reproducible replay of MPI applications. In: Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2009, pp. 251–260. ACM, New York (2009)Google Scholar
  29. 29.
    Yang, Z., Algesheimer, R., Tessone, C.J.: A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, 30750 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manfred Calvo
    • 1
  • Diego Jiménez
    • 2
    Email author
  • Esteban Meneses
    • 1
    • 2
  1. 1.School of ComputingCosta Rica Institute of TechnologyCartagoCosta Rica
  2. 2.Advanced Computing LaboratoryCosta Rica National High Technology CenterSan JoséCosta Rica

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