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Using RngStreams for parallel random number generation in C++ and R

Author

Listed:
  • Andrew Karl
  • Randy Eubank
  • Jelena Milovanovic
  • Mark Reiser
  • Dennis Young

Abstract

The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using RngStreams with C++ programs that are parallelized via OpenMP or MPI. Ways to access the backbone generator from RngStreams in R through the parallel and rstream packages are also described. The ideas in the paper are illustrated with both a simple running example and a Monte Carlo integration application. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Andrew Karl & Randy Eubank & Jelena Milovanovic & Mark Reiser & Dennis Young, 2014. "Using RngStreams for parallel random number generation in C++ and R," Computational Statistics, Springer, vol. 29(5), pages 1301-1320, October.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1301-1320
    DOI: 10.1007/s00180-014-0492-3
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    References listed on IDEAS

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    1. Schmidberger, Markus & Morgan, Martin & Eddelbuettel, Dirk & Yu, Hao & Tierney, Luke & Mansmann, Ulrich, 2009. "State of the Art in Parallel Computing with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i01).
    2. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    3. Manuel Eugster & Jochen Knaus & Christine Porzelius & Markus Schmidberger & Esmeralda Vicedo, 2011. "Hands-on tutorial for parallel computing with R," Computational Statistics, Springer, vol. 26(2), pages 219-239, June.
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    Cited by:

    1. Eric C. Ni & Dragos F. Ciocan & Shane G. Henderson & Susan R. Hunter, 2017. "Efficient Ranking and Selection in Parallel Computing Environments," Operations Research, INFORMS, vol. 65(3), pages 821-836, June.
    2. L’Ecuyer, Pierre & Munger, David & Oreshkin, Boris & Simard, Richard, 2017. "Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 135(C), pages 3-17.
    3. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).

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    Keywords

    OpenMP; MPI; Multicore; Rstream;
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