Advertisement

Applying Process Migration on a BSP-Based LU Decomposition Application

  • Rodrigo da Rosa Righi
  • Laércio Lima Pilla
  • Alexandre Carissimi
  • Philippe Olivier Alexandre Navaux
  • Hans-Ulrich Heiss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6449)

Abstract

Process migration is an useful mechanism to offer load balancing. In this context, we developed a model called MigBSP that controls processes rescheduling on BSP applications. MigBSP is especially pertinent to obtain performance on this type of applications, since they are composed by supersteps which always wait for the slowest process. In this paper, we focus on the BSP-based modeling of the widely used LU Decomposition algorithm as well as its execution with MigBSP. The use of multiple metrics to decide migrations and adaptations on rescheduling frequency turn possible gains up to 19% over our cluster-of-clusters architecture. Finally, our final idea is to show the possibility to get performance in LU effortlessly by using novel migration algorithms.

Keywords

Load Balance High Performance Computing Process Migration Migration Cost Application Execution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, G., Motwani, R., Zhu, A.: The load rebalancing problem. In: SPAA 2003: Proceedings of the Fifteenth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 258–265. ACM Press, New York (2003)CrossRefGoogle Scholar
  2. 2.
    Bhandarkar, M.A., Brunner, R., Kale, L.V.: Run-time support for adaptive load balancing. In: IPDPS 2000: Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, pp. 1152–1159. Springer, London (2000)Google Scholar
  3. 3.
    Bisseling, R.H.: Parallel Scientific Computation: A Structured Approach Using BSP and MPI. Oxford University Press, Oxford (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bonorden, O.: Load balancing in the bulk-synchronous-parallel setting using process migrations. In: 21th International Parallel and Distributed Processing Symposium (IPDPS 2007), pp. 1–9. IEEE, Los Alamitos (2007)CrossRefGoogle Scholar
  5. 5.
    Bonorden, O., Gehweiler, J., auf der Heide, F.M.: Load balancing strategies in a web computing environment. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds.) PPAM 2005. LNCS, vol. 3911, pp. 839–846. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Casanova, H., Legrand, A., Quinson, M.: Simgrid: A generic framework for large-scale distributed experiments. In: Tenth International Conference on Computer Modeling and Simulation (uksim), pp. 126–131. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar
  7. 7.
    Chen, L., Wang, C.-L., Lau, F.: Process reassignment with reduced migration cost in grid load rebalancing. In: IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008, pp. 1–13 (April 2008)Google Scholar
  8. 8.
    da Rosa Righi, R., Pilla, L., Carissimi, A., Navaux, P.O.A.: Controlling processes reassignment in bsp applications. In: 20th International Symposium on Computer Architecture and high Performance Computing (SBAC-PAD 2008), pp. 37–44. IEEE Computer Society, Los Alamitos (2008)CrossRefGoogle Scholar
  9. 9.
    da Rosa Righi, R., Pilla, L.L., Carissimi, A., Navaux, P., Heiss, H.-U.: Migbsp: A novel migration model for bulk-synchronous parallel processes rescheduling. In: 10th IEEE International Conference on High Performance Computing and Communications, pp. 585–590 (2009)Google Scholar
  10. 10.
    Frachtenberg, E., Schwiegelshohn, U.: New Challenges of Parallel Job Scheduling. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2007. LNCS, vol. 4942, pp. 1–23. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Galindo, I., Almeida, F., Badía-Contelles, J.M.: Dynamic load balancing on dedicated heterogeneous systems. In: Lastovetsky, A., Kechadi, T., Dongarra, J. (eds.) EuroPVM/MPI 2008. LNCS, vol. 5205, pp. 64–74. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Gustavson, F.G.: High-performance linear algebra algorithms using new generalized data structures for matrices. IBM J. Res. Dev. 47(1), 31–55 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huang, C., Zheng, G., Kalé, L., Kumar, S.: Performance evaluation of adaptive mpi. In: PPoPP 2006: Proceedings of the Eleventh ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 12–21. ACM Press, New York (2006)Google Scholar
  14. 14.
    Jiang, Y., Tong, W., Zhao, W.: Resource load balancing based on multi-agent in servicebsp model. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 42–49. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Low, M.Y.-H., Liu, W., Schmidt, B.: A parallel bsp algorithm for irregular dynamic programming. In: Xu, M., Zhan, Y.-W., Cao, J., Liu, Y. (eds.) APPT 2007. LNCS, vol. 4847, pp. 151–160. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Maassen, J., van Nieuwpoort, R.V., Kielmann, T., Verstoep, K., den Burger, M.: Middleware adaptation with the delphoi service. Concurrency and Computation: Practice & Experience (2006)Google Scholar
  17. 17.
    Pontelli, E., Le, H.V., Son, T.C.: An investigation in parallel execution of answer set programs on distributed memory platforms: Task sharing and dynamic scheduling. Comput. Lang. Syst. Struct. 36(2), 158–202 (2010)Google Scholar
  18. 18.
    Qin, X., Jiang, H., Manzanares, A., Ruan, X., Yin, S.: Communication-aware load balancing for parallel applications on clusters. IEEE Trans. Comput. 59(1), 42–52 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Silva, R.E., Pezzi, G., Maillard, N., Diverio, T.: Automatic data-flow graph generation of mpi programs. In: SBAC-PAD 2005: Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing, Washington, DC, USA, pp. 93–100. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  20. 20.
    Valiant, L.G.: A bridging model for parallel computation. Commun. ACM 33(8), 103–111 (1990)CrossRefGoogle Scholar
  21. 21.
    Wieczorek, M., Podlipnig, S., Prodan, R., Fahringer, T.: Bi-criteria scheduling of scientific workflows for the grid. ccgrid, 9–16 (2008)Google Scholar
  22. 22.
    Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Gener. Comput. Syst. 26(4), 608–621 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rodrigo da Rosa Righi
    • 1
  • Laércio Lima Pilla
    • 1
  • Alexandre Carissimi
    • 1
  • Philippe Olivier Alexandre Navaux
    • 1
  • Hans-Ulrich Heiss
    • 2
  1. 1.Institute of InformaticsFederal University of Rio Grande do SulBrazil
  2. 2.Kommunikations- und BetriebssystemeTechnical University BerlinGermany

Personalised recommendations