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Framework for testing random numbers in parallel calculations.

I Vattulainen1

  • 1Department of Chemistry, Building 207, Technical University of Denmark, DK-2800 Lyngby, Denmark.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|April 24, 2002
PubMed
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This study introduces a novel framework for assessing random number quality in parallel computing. By analyzing correlations between random walkers, it effectively detects flaws in common pseudorandom number generators.

Area of Science:

  • Computational science
  • Statistical physics
  • Numerical analysis

Background:

  • High-quality random numbers are crucial for accurate parallel computations.
  • Existing methods for testing pseudorandom number generators may not be sufficient for parallel environments.
  • Correlations within random number sequences can lead to significant errors in simulations.

Purpose of the Study:

  • To develop a robust framework for evaluating the quality of random numbers used in parallel calculations.
  • To introduce a novel method for detecting subtle correlations in pseudorandom number sequences.
  • To provide a tool for improving the reliability of scientific simulations.

Main Methods:

  • Proposing a framework based on studying cross-correlations between distinct random number sequences.

Related Experiment Videos

  • Utilizing diffusing random walkers, each governed by a unique random number sequence.
  • Analyzing the asymptotic power-law behavior of correlation functions and their exponents.
  • Employing complementary methods for investigating pre-asymptotic correlation regimes.
  • Main Results:

    • Demonstrating the framework's effectiveness through three efficient tests.
    • Identifying correlations in several widely used pseudorandom number generators.
    • Establishing a method for comparing empirical exponents with exact theoretical results.

    Conclusions:

    • The proposed framework offers a powerful approach for testing random number quality in parallel settings.
    • The random walker correlation method successfully reveals deficiencies in common pseudorandom number generators.
    • The framework has potential applications in various scientific and computational contexts.