Implementation of the Parallel Superposition in Bulk-Synchronous Parallel ML

  • Frédéric Gava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4487)


Bulk-Synchronous Parallel ML (BSML) is a functional data-parallel language to code Bulk-Synchronous Parallel (BSP) algorithms. It allows an estimation of execution time, avoids deadlocks and nondeterminism. This paper presents the implementation of a new primitive for BSML which can express divide-and-conquer algorithms.


BSP Functional Programming divide-and-conquer 


  1. 1.
    Aumor, M., Arguello, F., Lopez, J., Plata, O., Zapata, L.: A data-parallel formulation for divide-and-conquer algorithms. The Computer Journal 44(4), 303–320 (2001)CrossRefGoogle Scholar
  2. 2.
    Bisseling, R.H.: Parallel Scientific Computation. A structured approach using BSP and MPI. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  3. 3.
    Bonorden, O., Juurlink, B., Von Otte, I., Rieping, O.: The Paderborn University BSP (PUB) library. Parallel Computing 29(2), 187–207 (2003)CrossRefGoogle Scholar
  4. 4.
    Gava, F.: Approches fonctionnelles de la programmation paralléle et des méta-ordinateurs; Sémantiques, implantation et certification. PhD thesis, University of Paris XII (2005)Google Scholar
  5. 5.
    Herrmann, C.A.: Functional meta-programming in the construction of parallel programs. Parallel Processing Letters, to appear (2006)Google Scholar
  6. 6.
    Loulergue, F.: Parallel Juxtaposition for Bulk Synchronous Parallel ML. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 781–788. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Loulergue, F.: Parallel Superposition for Bulk Synchronous Parallel ML. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2659, pp. 223–232. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Loulergue, F., Gava, F., Billiet, D.: Bulk Synchronous Parallel ML: Modular Implementation and Performance Prediction. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 1046–1054. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Skillicorn, D.B., Hill, J.M.D., McColl, W.F.: Questions and Answers about BSP. Scientific Programming 6(3), 249–274 (1997)Google Scholar
  10. 10.
    Tiskin, A.: A New Way to Divide and Conquer. Parallel Processing Letters 11(4), 409–422 (2001)MathSciNetGoogle Scholar
  11. 11.
    Vasconcelos, P.B., Hammond, K.: Inferring Cost Equations for Recursive, Polymorphic and Higher-Order Functional Programs. In: Trinder, P., Michaelson, G.J., Peña, R. (eds.) IFL 2003. LNCS, vol. 3145, pp. 86–101. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Zavanella, A.: Skeletons and BSP: Performance Portability for Parallel Programming. PhD thesis, Universita degli studi di Pisa (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Frédéric Gava
    • 1
  1. 1.Laboratory of Algorithms, Complexity and Logic, University of Paris XII 

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