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Automatic Tuning of Data Distribution Using Factoring in Master/Worker Applications

  • Anna Morajko
  • Paola Caymes
  • Tomàs Margalef
  • Emilio Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)

Abstract

Parallel/Distributed programming is a complex task that requires a high degree of expertise to fulfill the expectations of high performance computation. On the one hand, application developers must tackle new programming paradigms, languages, libraries. On the other hand they must consider all the issues concerning application performance. On this context the Master/Worker paradigm appears as one of the most commonly used because it is quite easy to understand and there is a wide range of applications that match this paradigm. However, to reach high performance indeces it is necessary to tune the data distribution or the number of Workers considering the particular features of each run or even the actual behavior that can change dynamically during the execution. Dynamic tuning becomes a necessary and promising approach to reach the desired indeces. In this paper, we show the usage of a dynamic tuning environment that allows for adapting the data distribution applying Factoring algorithm on Master/Worker applications. The results show that such approach improves the execution time significantly when the application modifies its behavior during its execution.

Keywords

Execution Time Data Distribution Work Unit Load Imbalance Master Process 
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.

References

  1. 1.
    César, E., Mesa, J.G., Sorribes, J., Luque, E.: Modeling Master-Worker Applications in POETRIES. In: IEEE 9th International Workshop HIPS 2004, IPDPS, April 2004, pp. 22–30 (2004)Google Scholar
  2. 2.
    Hummel, S.F., Schonberg, E., Flynn, L.E.: Factoring: A method for Scheduling Parallel Loops. Communications of the ACM 35(8) (August 1992)Google Scholar
  3. 3.
    Morajko, A., Morajko, O., Jorba, J., Margalef, T., Luque, E.: Dynamic Performance Tuning of Distributed Programming Libraries. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2660, pp. 191–200. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Morajko, A., Morajko, O., Margalef, T., Luque, E.: MATE: Dynamic Performance Tuning Environment. In: Danelutto, M., Vanneschi, M., Laforenza, D. (eds.) Euro-Par 2004. LNCS, vol. 3149, pp. 98–107. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Jorba, J., Margalef, T., Luque, E., Andre, J., Viegas, D.X.: Application of Parallel Computing to the Simulation of Forest Fire Propagation. In: Proc. 3rd International Conference in Forest Fire Propagation, Portugal, November 1998, vol. 1, pp. 891–900 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Anna Morajko
    • 1
  • Paola Caymes
    • 1
  • Tomàs Margalef
    • 1
  • Emilio Luque
    • 1
  1. 1.Computer Science DepartmentUniversitat Autònoma de BarcelonaBellaterraSpain

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