SPRNG 1.0 provides the user the various SPRNG random number generators each in its own library. For most users this is acceptable, as one rarely uses more than one type of generator in a single program. However, if the user desires this added flexibility, SPRNG 2.0 provides it. In all other respects, SPRNG 1.0 and SPRNG 2.0 are identical. Both versions only uses the GNU Multi Precision (GMP) package for one of their generators. SPRNG 3.0 uses GMP for all generators. SPRNG 4.0 is a C++ version with the GMP package removed. It is not backwards compatible with any of the previous SPRNG versions, except for its default FORTRAN interface. SPRNG is Y2K compliant software.

References in zbMATH (referenced in 37 articles , 1 standard article )

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  1. Aljahdali, Asia; Mascagni, Michael: Feistel-inspired scrambling improves the quality of linear congruential generators (2017)
  2. Beebe, Nelson H. F.: The mathematical-function computation handbook. Programming using the MathCW portable software library (2017)
  3. Jones, Aquil D.; Simpson, Gideon; Wilson, William: Conservative integrators for a toy model of weak turbulence (2017)
  4. Ni, Eric C.; Ciocan, Dragos F.; Henderson, Shane G.; Hunter, Susan R.: Efficient ranking and selection in parallel computing environments (2017)
  5. Len^otre, Lionel: A strategy for parallel implementations of stochastic Lagrangian simulation (2016)
  6. Binder, Andrew; Lelièvre, Tony; Simpson, Gideon: A generalized parallel replica dynamics (2015)
  7. de Doncker, Elise; Kapenga, John; Assaf, Rida: Monte Carlo automatic integration with dynamic parallelism in CUDA (2014)
  8. Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
  9. Mascagni, Michael; Qiu, Yue; Hin, Lin-Yee: High performance computing in quantitative finance: a review from the pseudo-random number generator perspective (2014)
  10. Mascagni, Michael; Hin, Lin-Yee: Parallel pseudo-random number generators: a derivative pricing perspective with the Heston stochastic volatility model (2013)
  11. Balaž, Antun; Vidanović, Ivana; Stojiljković, Danica; Vudragović, Dušan; Belić, Aleksandar; Bogojević, Aleksandar: SPEEDUP code for calculation of transition amplitudes via the effective action approach (2012)
  12. Corsaro, S.; De Angelis, P.L.; Marino, Z.; Perla, F.: Participating life insurance policies: an accurate and efficient parallel software for COTS clusters (2011)
  13. Corsaro, S.; De Angelis, P.L.; Marino, Z.; Perla, F.; Zanetti, P.: On parallel asset-liability management in life insurance: a forward risk-neutral approach (2010)
  14. Hadjidoukas, P.; Bousis, C.; Emfietzoglou, D.: Parallelization of a Monte Carlo particle transport simulation code (2010)
  15. Hwang, Chi-Ok; Mascagni, Michael; Won, Taeyoung: Monte Carlo methods for computing the capacitance of the unit cube (2010)
  16. Lee, Junkyu; Peterson, Gregory D.; Harrison, Robert J.; Hinde, Robert J.: Implementation of hardware-accelerated scalable parallel random number generators (2010) ioport
  17. Ökten, Giray; Willyard, Matthew: Parameterization based on randomized quasi-Monte Carlo methods (2010)
  18. Lee, Junkyu; Bi, Yu; Peterson, Gregory D.; Hinde, Robert J.; Harrison, Robert J.: HASPRNG: hardware accelerated scalable parallel random number generators (2009)
  19. Mascagni, Michael; Yu, Haohai: Scrambled Soboĺ sequences via permutation (2009)
  20. Chi, Hongmei; Jones, Edward L.: Generating parallel quasirandom sequences via randomization (2007)

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