TestU01 is a software library, implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators. The library implements several types of random number generators in generic form, as well as many specific generators proposed in the literature or found in widely-used software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to user-defined generators. Specific tests suites for either sequences of uniform random numbers in [0,1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of random number generators. That is, for a given kind of test and a given class of random number generators, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically.

References in zbMATH (referenced in 49 articles )

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  1. Aljahdali, Asia; Mascagni, Michael: Feistel-inspired scrambling improves the quality of linear congruential generators (2017)
  2. M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
  3. Alvarez, Nicolás; Becher, Verónica; Ferrari, Pablo A.; Yuhjtman, Sergio A.: Perfect necklaces (2016)
  4. Balková, Ľubomíra; Bucci, Michelangelo; De Luca, Alessandro; Hladký, Jiří; Puzynina, Svetlana: Aperiodic pseudorandom number generators based on infinite words (2016)
  5. Faure, Emil V.; Shcherba, Anatoly I.; Rudnytskyi, V.M.: The method and criterion for quality assessment of random number sequences (2016)
  6. Len^otre, Lionel: A strategy for parallel implementations of stochastic Lagrangian simulation (2016)
  7. Self, Julian; Mackey, Michael C.: Random numbers from a delay equation (2016)
  8. Balint, Adrian; Belov, Anton; Järvisalo, Matti; Sinz, Carsten: Overview and analysis of the SAT challenge 2012 solver competition (2015) ioport
  9. Demchik, Vadim: Pseudorandom numbers generation for Monte Carlo simulations on GPUs: OpenCL approach (2014)
  10. Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
  11. Mélard, Guy: On the accuracy of statistical procedures in Microsoft Excel 2010 (2014)
  12. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  13. Beliakov, Gleb; Johnstone, Michael; Creighton, Doug; Wilkin, Tim: An efficient implementation of Bailey and Borwein’s algorithm for parallel random number generation on graphics processing units (2013)
  14. Graham, Carl; Talay, Denis: Stochastic simulation and Monte Carlo methods. Mathematical foundations of stochastic simulation (2013)
  15. Li, Jie; Zheng, Jianliang: MaD2: an ultra-performance stream cipher for pervasive data encryption (2013)
  16. Mascagni, Michael; Hin, Lin-Yee: Parallel pseudo-random number generators: a derivative pricing perspective with the Heston stochastic volatility model (2013)
  17. Saito, Mutsuo; Matsumoto, Makoto: Variants of Mersenne Twister suitable for graphic processors (2013)
  18. Sezgin, Fatin; Sezgin, Tevfik Metin: Finding the best portable congruential random number generators (2013)
  19. Barash, L.Yu.: Geometric and statistical properties of pseudorandom number generators based on multiple recursive transformations (2012)
  20. Deng, Lih-Yuan; Shiau, Jyh-Jen Horng; Lu, Henry Horng-Shing: Efficient computer search of large-order multiple recursive pseudo-random number generators (2012)

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