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Fine Tuning Algorithmic Skeletons

  • Denis Caromel
  • Mario Leyton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)

Abstract

Algorithmic skeletons correspond to a high-level programming model that takes advantage of nestable programming patterns to hide the complexity of parallel/distributed applications. Programmers have to: define the nested skeleton structure, and provide the muscle (sequential) portions of code which are specific to a problem.

An inadequate structure definition, or inefficient muscle code can lead to performance degradation of the application. Related research has focused on the problem of performing optimization to the skeleton structure. Nevertheless, to our knowledge, no focus has been done on how to aide the programmer to write performance efficient muscle code.

We present the Calcium skeleton framework as the environment in which to perform fine tuning of algorithmic skeletons. Calcium provides structured parallelism in Java using ProActive. ProAcitve is a grid middleware implementing the active object programming model, and providing a deployment framework.

Finally, using a skeleton solution of the NQueens counting problems in Calcium, we validate the fine tuning approach on a grid environment.

Keywords

Grid Environment Single Instruction Multiple Data Performance Diagnosis Algorithmic Skeleton Idle Resource 
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.
    Cole, M.: Algorithmic skeletons: structured management of parallel computation. MIT Press, Cambridge (1991)Google Scholar
  2. 2.
    Aldinucci, M., Danelutto, M.: Stream parallel skeleton optimization. In: Proc. of PDCS: Intl. Conference on Parallel and Distributed Computing and Systems, Cambridge, Massachusetts, USA, November 1999, pp. 955–962. IASTED, ACTA press (1999)Google Scholar
  3. 3.
    Aldinucci, M., Danelutto, M., Dünnweber, J.: Optimization techniques for implementing parallel skeletons in grid environments. In: Gorlatch, S. (ed.) Proc. of CMPP: Intl. Workshop on Constructive Methods for Parallel Programming, Stirling, Scotland, UK, July 2004, pp. 35–47. Universität Münster, Germany (2004)Google Scholar
  4. 4.
    Danelutto, M.: Qos in parallel programming through application managers. In: PDP 2005. Proceedings of the 13th Euromicro Conference on Parallel, Distributed and Network-Based Processing, Washington, DC, USA, pp. 282–289. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  5. 5.
  6. 6.
    Caromel, D.: Toward a method of object-oriented concurrent programming. Communications of the ACM 36(9), 90–102 (1993)CrossRefGoogle Scholar
  7. 7.
    Baude, F., Caromel, D., Mestre, L., Huet, F., Vayssière, J.: Interactive and descriptor-based deployment of object-oriented grid applications. In: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, Edinburgh, Scotland, July 2002, pp. 93–102. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  8. 8.
    Aldinucci, M., Danelutto, M., Teti, P.: An advanced environment supporting structured parallel programming in Java. Future Generation Computer Systems 19(5), 611–626 (2003)CrossRefGoogle Scholar
  9. 9.
    Danelutto, M., Teti, P.: Lithium: A structured parallel programming enviroment in Java. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J.J., Hoekstra, A.G. (eds.) ICCS 2002. LNCS, vol. 2330, pp. 844–853. Springer, Heidelberg (2002)Google Scholar
  10. 10.
    Danelutto, M., Dazzi, P.: Joint structured/unstructured parallelism exploitation in Muskel. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2006. LNCS, vol. 3991, Springer, Heidelberg, 2006 (to appear)Google Scholar
  11. 11.
    Microsystems, S.: Java, http://java.sun.com
  12. 12.
    Caromel, D., Delbe, C., Costanzo, A., Leyton, M.: Proactive: an integrated platform for programming and running applications on grids and p2p systems. Computational Methods in Science and Technology 12 (2006)Google Scholar
  13. 13.
    Priebe, S.: Dynamic task generation and transformation within a nestable workpool skeleton. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 615–624. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Grid5000: Official web site, http://www.grid5000.fr
  15. 15.
  16. 16.
    Cesar, E., Mesa, J.G., Sorribes, J., Luque, E.: Modeling master-worker applications in poetries. hips 00, 22–30 (2004)Google Scholar
  17. 17.
    Li, L., Malony, A.: Model-based performance diagnosis of master-worker parallel computations. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 35–46. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Benoit, A., Cole, M., Gilmore, S., Hillston, J.: Evaluating the performance of skeleton-based high level parallel programs. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2004. LNCS, vol. 3036, pp. 299–306. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Denis Caromel
    • 1
  • Mario Leyton
    • 1
  1. 1.INRIA Sophia-Antipolis, CNRS, I3S, UNSA. 2004, Route des Lucioles, BP 93, F-06902 Sophia-Antipolis CedexFrance

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