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Parallel I/O in Bulk-Synchronous Parallel ML

  • Frédéric Gava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3038)

Abstract

Bulk Synchronous Parallel ML or BSML is a functional data-parallel language for programming bulk synchronous parallel (BSP) algorithms. The execution time can be estimated and dead-locks and indeterminism are avoided. For large scale applications where parallel processing is helpful and where the total amount of data often exceeds the total main memory available, parallel disk I/O becomes a necessity. We present here a library of I/O features for BSML and its cost model.

Keywords

Cost Model Local Memory Disk Drive External Memory Parallel Vector 
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.
    Dehne, F., Dittrich, W., Hutchinson, D., Maheshwari, A.: Parallel virtual memory. In: 10th Annual ACM-SIAM Symposium on Discrete Algorithms, Baltimore, MD, pp. 889– 890 (1999)Google Scholar
  2. 2.
    Dehne, F., Dittrich, W., Hutchinson, D., Maheshwari, A.: Bulk synchronous parallel algorithms for the external memory model. Theory of Computing Systems 35, 567–598 (2003)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Gava, F., Loulergue, F.: A Polymorphic Type System for Bulk Synchronous Parallel ML. In: Malyshkin, V.E. (ed.) PaCT 2003. LNCS, vol. 2763, pp. 215–229. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Grelck, C., Scholz, S.-B.: Classes and objects as basis for I/O in SAC. In: Proceedings of IFL 1995, Gothenburg, Sweden, pp. 30–44 (1995)Google Scholar
  5. 5.
    Trinder, P.W., Hammond, K., all: Comparing parallel functional languages: Programming and performance. Higher-order and Symbolic Computation 15(3) (2003)Google Scholar
  6. 6.
    Klusik, U., Ortega, Y., Pena, R.: Implementing EDEN: Dreams becomes reality. In: Hammond, K., Davie, T., Clack, C. (eds.) IFL 1998. LNCS, vol. 1595, pp. 103–119. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  7. 7.
    Leroy, X., Mauny, M.: Dynamics in ML. Journal of Functional Programming 3(4), 431–463 (1994)CrossRefGoogle Scholar
  8. 8.
    McColl, W.F.: Scalability, portability and predictability: The BSP approach to parallel programming. Future Generation Computer Systems 12, 265–272 (1996)CrossRefGoogle Scholar
  9. 9.
    Pottier, F., Simonet, V.: Information flow inference of ML. ACM Transactions on Programming Languages and Systems 25(1), 117–158 (2003)CrossRefGoogle Scholar
  10. 10.
    Sibeyn, J.F., Kaufmann, M.: BSP-Like External-Memory Computation. In: Bongiovanni, G., Bovet, D.P., Di Battista, G. (eds.) CIAC 1997. LNCS, vol. 1203, Springer, Heidelberg (1997)Google Scholar
  11. 11.
    Valiant, L.G.: A bridging model for parallel computation. Communications of the ACM 33(8), 103 (1990)CrossRefGoogle Scholar
  12. 12.
    Vitter, J.S., Shriver, E.A.M.: Algorithms for parallel memory, two -level memories. Algorithmica 12(2), 110–147 (1994)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Frédéric Gava
    • 1
  1. 1.LACLUniversity Paris XIICréteilFrance

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