SIMD-oriented Fast Mersenne Twister (SFMT): twice faster than Mersenne Twister. SFMT is a new variant of Mersenne Twister (MT) introduced by Mutsuo Saito and Makoto Matsumoto in 2006. The algorithm was reported at MCQMC 2006. The article published in the proceedings of MCQMC2006. (see Prof. Matsumoto’s Papers on random number generation.) SFMT is a Linear Feedbacked Shift Register (LFSR) generator that generates a 128-bit pseudorandom integer at one step. SFMT is designed with recent parallelism of modern CPUs, such as multi-stage pipelining and SIMD (e.g. 128-bit integer) instructions. It supports 32-bit and 64-bit integers, as well as double precision floating point as output. SFMT is much faster than MT, in most platforms. Not only the speed, but also the dimensions of equidistributions at v-bit precision are improved. In addition, recovery from 0-excess initial state is much faster. See Master’s Thesis of Mutsuo Saito for detail

References in zbMATH (referenced in 22 articles )

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  1. Dimov, Ivan; Todorov, Venelin; Sabelfeld, Karl: A study of highly efficient stochastic sequences for multidimensional sensitivity analysis (2022)
  2. Anacleto, Eduardo A. J.; Meneses, Cláudio N.; Liang, Ricardo N.: Fast r-flip move evaluations via closed-form formulae for Boolean quadratic programming problems with generalized upper bound constraints (2021)
  3. Bhattacharjee, Kamalika; Das, Sukanta: Random number generation using decimal cellular automata (2019)
  4. Dimov, I. T.; Maire, S.: A new unbiased stochastic algorithm for solving linear Fredholm equations of the second kind (2019)
  5. Harase, Shin: Conversion of mersenne twister to double-precision floating-point numbers (2019)
  6. Bakiri, Mohammed; Guyeux, Christophe; Couchot, Jean-François; Oudjida, Abdelkrim Kamel: Survey on hardware implementation of random number generators on FPGA: theory and experimental analyses (2018)
  7. Yagasaki, Kazuyuki: Melnikov processes and chaos in randomly perturbed dynamical systems (2018)
  8. L’Ecuyer, Pierre; Munger, David; Oreshkin, Boris; Simard, Richard: Random numbers for parallel computers: requirements and methods, with emphasis on gpus (2017)
  9. Takahiro Misawa, Satoshi Morita, Kazuyoshi Yoshimi, Mitsuaki Kawamura, Yuichi Motoyama, Kota Ido, Takahiro Ohgoe, Masatoshi Imada, Takeo Kato: mVMC - Open-source software for many-variable variational Monte Carlo method (2017) arXiv
  10. Li, Jie; Zheng, Jianliang; Whitlock, Paula: MaD0: an ultrafast nonlinear pseudorandom number generator (2016)
  11. McFarland, Christopher D.: A modified ziggurat algorithm for generating exponentially and normally distributed pseudorandom numbers (2016)
  12. Vigna, Sebastiano: An experimental exploration of Marsaglia’s \textttxorshiftgenerators, scrambled (2016)
  13. Cheng, Ching-Wei; Hung, Ying-Chao; Balakrishnan, Narayanaswamy: Generating beta random numbers and Dirichlet random vectors in R: the package rBeta2009 (2014)
  14. Cominetti, Roberto; Mascarenhas, Walter F.; Silva, Paulo J. S.: A Newton’s method for the continuous quadratic knapsack problem (2014)
  15. Diaz-Emparanza, Ignacio: Numerical distribution functions for seasonal unit root tests (2014)
  16. Dimov, Ivan; Georgieva, Rayna: Multidimensional sensitivity analysis of large-scale mathematical models (2013)
  17. A. Talha Yalta, Sven Schreiber: Random Number Generation in gretl (2012) not zbMATH
  18. Evans, Z. W. E.; Stephens, A. M.: Optimal correction of concatenated fault-tolerant quantum codes (2012)
  19. Dimov, Ivan; Georgieva, Rayna: Monte Carlo method for numerical integration based on Sobol’s sequences (2011)
  20. Harase, Shin: An efficient lattice reduction method for (\mathbfF_2)-linear pseudorandom number generators using Mulders and Storjohann algorithm (2011)

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