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Reconfigurable Object Consistency Model for Distributed Shared Memory

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 3758)

Abstract

The consistency models are responsible for managing the state of shared data for the applications of a distributed shared memory (DSM) systems. The already proposed consistency models are inflexible and cannot adapt to the workload and environments characteristics. So, they cannot achieve the best performance for the workloads and environments in all the cases. In this work, we propose, present and analyze a reconfigurable consistency model (ROCoM –Reconfigurable Object Consistency Model) for object based DSMs. ROCoM behavior was represented using a reconfigurable algorithm (RA) and its analysis was made using a simulation tool. Our results show that ROCoM, on average, had 34% (upper bound) better performance than other ones.

Keywords

Consistency Model Sequential Consistency Distribute Shared Memory High Performance Computational System Replication Protocol 
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.

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References

  1. 1.
    Adve, V.S., Garachorloo, K.: Shared Memory Consistency Models: A Tutorial., Technical Report 95/7, DEC Western Research Laboratory, University Avenue (1995)Google Scholar
  2. 2.
    Mosherger, D.: Memory Consistency Models., Technical Report TR 92/11, University of Arizona, pp. 18-26 (1992)Google Scholar
  3. 3.
    Melo, A.C.M.A.: Defining Uniform and Hybrid Memory Consistency Models on a Unified Framework. In: Proc. of the 32th HICSS, Software Technology, vol. VIII, pp. 270–279 (1999)Google Scholar
  4. 4.
    Ahuja, S., Carriero, N., Gerlernter, D.: Linda and Friends. IEEE Computer 19, 8 (1986)Google Scholar
  5. 5.
    Shi, W., Hu, W., Tang, Z.: An Interaction of Coherence Protocols and Memory Consistency Models in DSM Systems. ACM Operating Systems, 41–54 (1997)Google Scholar
  6. 6.
    Raynal, M.: Sequential Consistency as Lazy Linearizability. In: Fourth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 151–152 (2002)Google Scholar
  7. 7.
    Pousa, C.V., Góes, L.F.W., Martins, C.A.P.S.: Reconfigurable Object Consistency Model. In: 7th Advances in Parallel and Distributed Computational Models (2005) (in Press)Google Scholar
  8. 8.
    Jiménez, E., Fernández, A., Cholvi, V.: A Parametrized Algorithm that Implements Sequential, Causal, and Cache Memory Consistency. In: Workshop on Parallel, Distributed and Network-based Processing, pp. 437–444 (2002)Google Scholar
  9. 9.
    Raynal, M., Vidyasankar, K.: A Distributed Implementation of Sequential Consistency with Multi-Object. In: 24th International Conference on Distributed Computing Systems, pp. 544–551 (2004)Google Scholar
  10. 10.
    Singh, G.: Invariant Consistency: A Mechanism for Inter-Process Ordering in Distributed Shared Memory Systems. In: 22th International Conference on Distributed Computing Systems, pp. 447–450 (2002)Google Scholar
  11. 11.
    Góes, L.F.W., Martins, C.A.P.S.: Reconfigurable Gang Scheduling Algorithm. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 81–101. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Ramos, L.E.S., Martins, C.A.P.S.: Reconfigurable Collective Communication MPI Functions. High Performance Computational Systems (2004) (in Portuguese)Google Scholar
  13. 13.
    Góes, L.F.: Proposal and Development of a Reconfigurable Parallel Job Scheduling., M.Sc. Thesis Graduation Program in Electrical Engineering, Pontifical Catholic University of Minas Gerais (2004) (in Portuguese)Google Scholar
  14. 14.
    Pousa, C.V., Góes, L.F.W., Penha, D.O., Martins, C.A.P.S.: Reconfigurable Sequential Consistency Algorithm. In: 12th Reconfigurable Architecture Workshop (2005) (in Press)Google Scholar
  15. 15.
    Monnerat, L.R., Bianchini, R.: Efficiently Adapting to Sharing Patterns in Software DSMs. In: Proceedings of the 4th IEEE International Symposium on High-Performance Computer Architecture (1998)Google Scholar
  16. 16.
    Shah, S.K., Fleisch, B.D.: A Comparison of DSM Coherence Protocols using Program Driven Simulations. In: Proc. Int’l Conf. Parallel and Distributed Processing Techniques and Applications (PDPTA), vol. 3, pp. 1546–1553. CSREA Press (1998)Google Scholar
  17. 17.
    Wang, D., Chen, I., Chu, C.: Analyzing reconfigurable algorithms for managing replicated data with strict consistency requirements: a case study. In: 24th Annual International Computer Software and Applications Conference, pp. 608–613 (2000)Google Scholar
  18. 18.
    Lamport, L.: How to make a multiprocessor computer that correctly executes multiprocess programs. IEEE Trans. Comput. 28, 690–691 (1979)zbMATHCrossRefGoogle Scholar
  19. 19.
    Pousa, C.V., Ramos, L.E.S., Goes, L.F.W., Martins, C.A.P.S.: Extending ClusterSim with MP and DSM Modules. In: International Symposium on High Performance Computational Science and Engineering (2004)Google Scholar
  20. 20.
    Góes, L.F.W., Ramos, L.E.S., Martins, C.A.P.S.: ClusterSim: A Java Parallel Discrete Event Simulation Tool for Cluster Computing. In: IEEE International Conference on Cluster Computing (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Graduation Program in Electrical Engineering, Computational and Digital Systems GroupPontifical Catholic University of Minas GeraisMinas GeraisBrazil

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