article

Gaussian random number generators

Authors:

David B. Thomas,

Philip H.W. Leong, and

John D. VillasenorAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 39, Issue 4

Pages 11 - es

https://doi.org/10.1145/1287620.1287622

Published: 02 November 2007 Publication History

Abstract

Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.

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Index Terms

Gaussian random number generators
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms
      1. Random number generation

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 39, Issue 4

2007

134 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1287620

Issue’s Table of Contents

Copyright © 2007 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2007

Published in CSUR Volume 39, Issue 4

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