RANLUX: a Fortran implementation of the high-quality pseudorandom number generator of Luscher. Following some remarks on the quality of pseudorandom number generators commonly used in Monte Carlo calculations in computational physics, we offer a portable Fortran 77 implementation of a high-quality generator called RANLUX (for LUXury RANdom numbers), using the algorithm of M. Lüscher [ibid. 79, No. 1, 100-110 (1994; reviewed above)]. The implementation allows the user to select different quality or luxury levels, where higher quality requires somewhat longer computing time for the generation. There is a convenient way of initialization (appropriate also for massively parallel Monte Carlo computations) as well as two different methods of restarting from a break point (Source: http://cpc.cs.qub.ac.uk/summaries/)

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  1. Lista, Luca: Statistical methods for data analysis in particle physics (2016)
  2. Krömer, Pavel; Zelinka, Ivan; Snášel, Václav: Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods (2014) ioport
  3. Bach, Matthias; Lindenstruth, Volker; Philipsen, Owe; Pinke, Christopher: Lattice QCD based on OpenCL (2013)
  4. Kalczynski, Pawel J.: A discrete model for optimal operation of fossil-fuel generators of electricity (2012)
  5. Weigel, Martin: Performance potential for simulating spin models on GPU (2012)
  6. Buividovich, P.V.: A method for resummation of perturbative series based on the stochastic solution of Schwinger-Dyson equations (2011)
  7. Czakon, M.: Double-real radiation in hadronic top quark pair production as a proof of a certain concept (2011)
  8. Czyż, Henryk; Ivashyn, Sergiy: EKHARA: A Monte Carlo generator for $e^+e^- \to e^+e^-\pi^0$ and $e^+e^-\to e^+e^-\pi^+\pi^-$ processes (2011)
  9. Demchik, Vadim: Pseudo-random number generators for Monte Carlo simulations on ATI graphics processing units (2011)
  10. Jean-Baptiste, Nelly; Malaterre, Pierre-Olivier; Dorée, Christophe; Sau, Jacques: Data assimilation for real-time estimation of hydraulic states and unmeasured perturbations in a 1D hydrodynamic model (2011)
  11. Nankervis, John C.; Savin, N.E.: Testing for serial correlation: generalized Andrews-Ploberger tests (2010)
  12. Sau, Jacques; Malaterre, Pierre-Olivier; Baume, Jean-Pierre: Sequential Monte Carlo hydraulic state estimation of an irrigation canal (2010)
  13. Jansen, Karl; Urbach, Carsten: tmLQCD: a program suite to simulate Wilson twisted mass lattice QCD (2009)
  14. Kalczynski, Pawel J.; Kamburowski, Jerzy: An empirical analysis of the optimality rate of flow shop heuristics (2009)
  15. Deng, Lih-Yuan; Guo, Rui; Lin, Dennis K.J.; Bai, Fengshan: Improving random number generators in the Monte Carlo simulations via twisting and combining (2008)
  16. Badal, Andreu; Sempau, Josep: A package of Linux scripts for the parallelization of Monte Carlo simulations (2006) ioport
  17. Caselle, Michele; Hasenbusch, Martin; Panero, Marco: High precision Monte Carlo simulations of interfaces in the three-dimensional Ising model: a comparison with the Nambu-Goto effective string model (2006)
  18. Kalczynski, Pawel Jan; Kamburowski, Jerzy: A heuristic for minimizing the expected makespan in two-machine flow shops with consistent coefficients of variation (2006)
  19. Kleiss, Ronald; Lazopoulos, Achilleas: Error in Monte Carlo, quasi-error in quasi-Monte Carlo (2006)
  20. Mannella, R.: Numerical stochastic integration for quasi-symplectic flows (2006)

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