Tables of 64-bit Mersenne twisters

Nishimura, Takuji

doi:10.1145/369534.369540

Cited by 63 publications

(45 citation statements)

References 17 publications

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“…In these publications, the concept of uniform random numbers in PRNG actively uses the operations of bit logic. Great success has been found in directions such as linear congruential generators (Niederreiter, 1995;Entacher, 1999) and in twisting algorithm generators where Mersenne numbers are usually used (Matsumoto and Kurita, 1992;1994;Matsumoto and Nishimura, 1998;Nishimura, 2000). Important results were received in the use of approaches such as Fibonacci numbers (Makino, 1994;Aluru, 1997), Blum-Blum-Shub algorithm (Blum et al, 1986) and others.…”

Section: Related Workmentioning

confidence: 99%

“…Partially this approach was used in the classic research articles published by Japanese researches (Matsumoto and Nishimura, 1998;Nishimura, 2000). They have built several generators, including the well-known MT19937 (or MT19937-64 for the implementation that uses a 64-bit word length), which can reach a big value of repeatability as 2 19937 -1 and that is excellent for some special cases.…”

Section: Twisting Generatorsmentioning

confidence: 99%

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Parametrical Tuning of Twisting Generators

Deon¹,

Menyaev²

2016

Journal of Computer Science

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Generators of uniformly distributed random numbers are broadly applied in simulations of stochastic processes that rely on normal and other distributions. In a point of fact, the uniform random numbers are actively used for applications that range from, modeling different phenomena such as theoretical mathematics and technical designing, to evidence-based medicine. This paper proposes a novel approach which consists of a combination of global twister with circular technique and initial congruential generation with complete stochastic sequences. It has been experimentally confirmed that for complete sequences this type of generation provides uniformity in distribution of random numbers. The offered program codes include the tuning methods for the generation technique where random numbers may take any bit length. Moreover, the automatic switching of generator parameters such as initial congruential constants depending on intervals for generated numbers is considered as well. Demonstrated results of testing confirm the uniformity of distribution without any repeated or skipped generated elements.

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Section: Related Workmentioning

confidence: 99%

Section: Twisting Generatorsmentioning

confidence: 99%

Parametrical Tuning of Twisting Generators

Deon¹,

Menyaev²

2016

Journal of Computer Science

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“…The operations implemented by this matrix are called tempering and their purpose is to improve the uniformity of the points produced by the RNG. The Mersenne twister (Matsumoto and Nishimura, 1998;Nishimura, 2000) is a variant of the TGFSR where k is slightly less than pq and can be a prime number. A specific instance proposed by Matsumoto and Nishimura (1998) is fast, robust, has the huge period length of 2 19937 − 1, and has become quite popular.…”

Section: The Gfsr and Twisted Gfsrmentioning

confidence: 99%

Random Number Generation

L’Ecuyer

2011

Handbook of Computational Statistics

129

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“…Variations on generalized feedback shift register (GFSR) algorithms include the Ziff (6) RNG gfsr, a four-tap GFSR algorithm, the Mersenne Twister (7,8) 32-bit version mt and 64-bit version mt64 (tested as mtx and mtx32), and the well-equidistributed long-period linear (9) RNG well1024a.…”

Section: Random Number Generatorsmentioning

confidence: 99%

Testing, Selection, and Implementation of Random Number Generators

Collins¹

2008

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Tables of 64-bit Mersenne twisters

Abstract: We give new parameters for a Mersenne Twister pseudorandom number generator for 64-bit word machines.

Cited by 63 publications

References 17 publications

Parametrical Tuning of Twisting Generators

Parametrical Tuning of Twisting Generators

Random Number Generation

Testing, Selection, and Implementation of Random Number Generators

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