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Distributed Algorithm of Data Allocation in the Fragmented Programming System LuNA

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9251)

Abstract

The paper presents distributed algorithm with local communications Rope-of-Beads for static and dynamic data allocation in the LuNA fragmented programming system. LuNA is intended for implementation of large-scale numerical models on multicomputers with large number of processors and various network topologies. The algorithm takes into account the structure of a numerical model, provides static and dynamic load balancing and can be used in various network topologies.

Keywords

  • Dynamic data allocation
  • Distributed algorithms with local interactions
  • Fragmented programming technology
  • Fragmented programming system LuNA

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References

  1. Malyshkin, V.E., Perepelkin, V.A.: LuNA fragmented programming system, main functions and peculiarities of run-time subsystem. In: Malyshkin, V. (ed.) PaCT 2011. LNCS, vol. 6873, pp. 53–61. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  2. Malyshkin, V.E., Perepelkin, V.A.: Optimization Methods of parallel execution of numerical programs in the LuNA fragmented programming system. J. Supercomputing 61(1), 235–248 (2012)

    CrossRef  Google Scholar 

  3. Malyshkin, V.E., Perepelkin, V.A.: The PIC implementation in LuNA system of fragmented programming. J. Supercomputing 69(1), 89–97 (2014)

    CrossRef  Google Scholar 

  4. Kraeva, M.A., Malyshkin, V.E.: Assembly technology for parallel realization of numerical models on MIMD-multicomputers. J. Future Gener. Comput. Syst. 17(6), 755–765 (2001)

    CrossRef  MATH  Google Scholar 

  5. Kireev, S.E., Malyshkin, V.E.: Fragmentation of numerical algorithms for parallel subroutines library. J. Supercomputing 57(2), 161–171 (2011)

    CrossRef  Google Scholar 

  6. Kraeva, M.A., Malyshkin, V.E.: Dynamic load balancing algorithms for implementation of pic method on MIMD multicomputers. J. Programmirovanie, no. 1, pp. 47–53 (1999) (In Russian)

    Google Scholar 

  7. Hu, Y.F., Blake, R.J.: An improved diffusion algorithm for dynamic load balancing. J. Parallel Comput. 25(4), 417–444 (1999)

    MathSciNet  CrossRef  MATH  Google Scholar 

  8. Corradi, A., Leonardi, L., Zambonelli, F.: Performance comparison of load balancing policies based on a diffusion scheme. In: Lengauer, C., Griebl, M., Gorlatch, S. (eds.) Euro-Par 1997. LNCS, vol. 1300, pp. 882–886. Springer, Heidelberg (1997)

    Google Scholar 

  9. Anderson, J.M., Lam, M.S.: Global optimizations for parallelism and locality on scalable parallel machines. In: ACM-SIGPLAN PLDI 1993, pp. 112–125. ACM, New York (1993)

    Google Scholar 

  10. Li, J., Chen, M.: The data alignment phase in compiling programs for distributed-memory machines. J. Parallel Distrib. Comput. 13(2), 213–221 (1991)

    CrossRef  Google Scholar 

  11. Lee, P.: Efficient algorithms for data distribution on distributed memory parallel computers. J. IEEE Trans. Parallel Distrib. Syst. 8(8), 825–839 (1997)

    CrossRef  Google Scholar 

  12. Kwok, Y.-K., Ahmad, I.: Design and evaluation of data allocation algorithms for distributed multimedia database systems. IEEE J. Sel. Areas Commun. 14(7), 1332–1348 (1997)

    CrossRef  Google Scholar 

  13. Iacob, N.M.: Fragmentation and data allocation in the distributed environments. Annals of the University of Craiova - Mathematics and Computer Science Series 38(3), 76–83 (2011)

    Google Scholar 

  14. Jagannatha, S., Geetha, D.E., Suresh Kumar, T.V., Rajani Kanth, K.: Load balancing in distributed database system using resource allocation approach. J. Adv. Res. Comput. Commun. Eng. 2(7), 2529–2535 (2013)

    Google Scholar 

  15. Honicky, R.J., Miller E.L.: Replication under scalable hashing: a family of algorithms for scalable decentralized data distribution. In: 18th International Parallel and Distributed Processing Symposium (2004)

    Google Scholar 

  16. Alicherry, M., Lakshman, T.V.: Network aware resource allocation in distributed clouds. In: INFOCOM 2012, pp. 963–971. IEEE (2012)

    Google Scholar 

  17. AuYoung, A., Chun, B.N., Snoeren, A.C., Vahdat, A.: Resource allocation in federated distributed computing infrastructures. In: First Workshop on Operating System and Architectural Support for the On-demand IT InfraStructure (2004)

    Google Scholar 

  18. Raman, R., Livny, M., Solomon, M.: Matchmaking: distributed resource management for high throughput computing. J. Cluster Comput. 2(1), 129–138 (1999)

    CrossRef  Google Scholar 

  19. Reddy, C., Bondfhugula, U.: Effective automatic computation placement and data allocation for parallelization of regular programs. In: 28th ACM International Conference on Supercomputing, pp. 13–22. ACM, New York (2014)

    Google Scholar 

  20. Baden, S.B., Shalit, D.: Performance tradeoffs in multi-tier formulation of a finite difference method. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS-ComputSci 2001. LNCS, vol. 2073, pp. 785–794. Springer, Heidelberg (2001)

    CrossRef  Google Scholar 

  21. Ishikawa, K.-I.: ASURA: Scalable and Uniform Data Distribution Algorithm for Storage Clusters. Computing Research Repository, abs/1309.7720 (2013)

    Google Scholar 

  22. Chawla, A., Reed B., Juhnke, K., Syed, G.: Semantics of Caching with SPOCA: A Stateless, Proportional, Optimally-Consistent Addressing Algorithm. In: USENIX Annual Technical Conference 2011, pp. 33–33. USENIX Association (2011)

    Google Scholar 

  23. Lawder, J.K., King, P.J.H.: Using space-filling curves for multi-dimensional indexing. In: Jeffery, K., Lings, B. (eds.) BNCOD 2000. LNCS, vol. 1832, pp. 20–35. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

  24. Moon, B., Jagadish, H.V., Faloutsos, C., Saltz, J.H.: Analysis of the Clustering Properties of the Hilbert Space-Filling Curve. J IEEE Trans. Knowl. Data Eng. 13(1), 124–141 (2001)

    CrossRef  Google Scholar 

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Acknowledgments

This work was supported by Russian Foundation for Basic Research (grants 14-07-00381 a and 14-01-31328 mol_a).

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Correspondence to Vladislav A. Perepelkin .

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Malyshkin, V.E., Perepelkin, V.A., Schukin, G.A. (2015). Distributed Algorithm of Data Allocation in the Fragmented Programming System LuNA. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2015. Lecture Notes in Computer Science(), vol 9251. Springer, Cham. https://doi.org/10.1007/978-3-319-21909-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-21909-7_8

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