MAHEVE: An Efficient Reliable Mapping of Asynchronous Iterative Applications on Volatile and Heterogeneous Environments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6586)


The asynchronous iteration model, called AIAC, has been proven to be an efficient solution for heterogeneous and distributed architectures. An efficient mapping of application tasks is essential to reduce their execution time. In this paper we present a new mapping algorithm, called MAHEVE (Mapping Algorithm for HEterogeneous and Volatile Environments) which is efficient on such architectures and integrates a fault tolerance mechanism to resist computing node failures. Our experiments show gains on a typical AIAC application execution time up to 65%, executed on distributed clusters architectures containing more than 400 computing cores with the JaceP2P-V2 environment.


Fault Tolerance Mapping Algorithm Computing Node Node Failure Reliable Mapping 
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.


  1. 1.
  2. 2.
    Bahi, J., Contassot-Vivier, S., Couturier, R.: Performance comparison of parallel programming environments for implementing AIAC algorithms. Journal of Supercomputing 35(3), 227–244 (2006)CrossRefGoogle Scholar
  3. 3.
    Bahi, J., Contassot-Vivier, S., Couturier, R.: Asynchronous Iterations. In: Parallel Iterative Algorithms: from Sequential to Grid Computing. Numerical Analysis & Scientific Computating, vol. 1. Chapman & Hall/CRC, Boca Raton (2007)Google Scholar
  4. 4.
    Bailey, D., et al.: The NAS Parallel Benchmarks. Tech. Rep. RNR-94-007, NASA Advanced Supercomputing (NAS) Division (March 1994)Google Scholar
  5. 5.
    Charr, J.C., Couturier, R., Laiymani, D.: JACEP2P-V2: A fully decentralized and fault tolerant environment for executing parallel iterative asynchronous applications on volatile distributed architectures. In: Abdennadher, N., Petcu, D. (eds.) GPC 2009. LNCS, vol. 5529, pp. 446–458. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Couturier, R., Laiymani, D., Miquée, S.: Mapping asynchronous iterative applications on heterogeneous distributed architectures. In: PDSEC 2010 (2010)Google Scholar
  7. 7.
    Elnozahy, E.N., Alvisi, L., Wang, Y., Johnson, D.: A survey of rollback-recovery protocols in message-passing systems. ACM Comput. Surv. 34(3), 375–408 (2002)CrossRefGoogle Scholar
  8. 8.
    Hendrickson, B., Leland, R.W.: The Chaco User’s Guide (1995)Google Scholar
  9. 9.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partioning irregular graphs. SIAM Journal on Scientific Computing 20(1), 359–392 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Kumar, S., Das, S.K., Biswas, R.: Graph partitioning for parallel applications in heterogeneous grid environments. In: IPDPS (2002)Google Scholar
  11. 11.
    Long, D.L., Clarke, L.A.: Task interaction graphs for concurrency analysis. In: ICSE, pp. 44–52 (1989)Google Scholar
  12. 12.
    Phinjaroenphan, P.: An Efficient, Pratical, Portable Mapping Technique on Computational Grids. Ph.D. thesis, RMIT University (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.LIFC laboratoryUniversity of Franche-ComtéFrance

Personalised recommendations