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Random numbers for large-scale distributed Monte Carlo simulations.

Heiko Bauke1, Stephan Mertens

  • 1Institut für Theoretische Physik, Universität Magdeburg, Universitätsplatz 2, 39016 Magdeburg, Germany. heiko.bauke@physics.ox.ac.uk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 7, 2007
PubMed
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Generating high-quality pseudorandom numbers for parallel computing is crucial. This study shows multiple linear recurrences in finite fields, with delinearization, effectively produce these numbers for distributed simulations.

Area of Science:

  • Statistical physics
  • Complex system science
  • Computational science

Background:

  • Monte Carlo simulations are essential tools in various scientific fields.
  • Running these simulations on distributed systems necessitates efficient parallel random number generation.
  • Existing methods face challenges in distributed and high-dimensional environments.

Purpose of the Study:

  • To demonstrate the efficacy of multiple linear recurrences in finite fields for pseudorandom number generation.
  • To address the limitations of linear recurrences in high-dimensional sampling.
  • To provide a robust method for generating high-quality random numbers in parallel algorithms.

Main Methods:

  • Utilizing multiple linear recurrences over finite fields.

Related Experiment Videos

  • Implementing a delinearization technique to overcome sampling weaknesses.
  • Applying the method to sequential and parallel simulation algorithms.

    • Main Results:

    • Multiple linear recurrences in finite fields provide high-quality pseudorandom numbers.
    • The proposed delinearization method successfully addresses the issue of sampling points in high dimensions.
    • The method is suitable for both sequential and parallel computational environments.

    Conclusions:

    • Multiple linear recurrences with delinearization offer an ideal solution for pseudorandom number generation in distributed systems.
    • This approach enhances the reliability and efficiency of Monte Carlo simulations.
    • The method preserves desirable properties of linear sequences for advanced scientific computing.