scholarly journals Quality of random number generators significantly affects results of Monte Carlo simulations for organic and biological systems

2010 ◽  
Vol 32 (3) ◽  
pp. 513-524 ◽  
Author(s):  
Timothy H. Click ◽  
Aibing Liu ◽  
George A. Kaminski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Biological Systems ◽  
Random Number Generators

scholarly journals TESTS OF RANDOM NUMBER GENERATORS USING ISING MODEL SIMULATIONS

1996 ◽  
Vol 07 (03) ◽  
pp. 295-303 ◽  
Author(s):  
P. D. CODDINGTON
Keyword(s):  
Monte Carlo ◽  
Ising Model ◽  
Monte Carlo Simulations ◽  
High Performance ◽  
Large Scale ◽  
Random Number ◽  
Random Number Generators ◽  
Lattice Monte Carlo ◽  
High Performance Computers

Large-scale Monte Carlo simulations require high-quality random number generators to ensure correct results. The contrapositive of this statement is also true — the quality of random number generators can be tested by using them in large-scale Monte Carlo simulations. We have tested many commonly-used random number generators with high precision Monte Carlo simulations of the 2-d Ising model using the Metropolis, Swendsen-Wang, and Wolff algorithms. This work is being extended to the testing of random number generators for parallel computers. The results of these tests are presented, along with recommendations for random number generators for high-performance computers, particularly for lattice Monte Carlo simulations.

scholarly journals On parallel random number generation for accelerating simulations of communication systems

2014 ◽  
Vol 12 ◽  
pp. 75-81 ◽  
Author(s):  
C. Brugger ◽  
S. Weithoffer ◽  
C. de Schryver ◽  
U. Wasenmüller ◽  
N. Wehn
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Communication Systems ◽  
Random Number ◽  
Random Number Generation ◽  
Random Number Generators ◽  
Distributed Simulations ◽  
Parallel Stream ◽  
Very High

Abstract. Powerful compute clusters and multi-core systems have become widely available in research and industry nowadays. This boost in utilizable computational power tempts people to run compute-intensive tasks on those clusters, either for speed or accuracy reasons. Especially Monte Carlo simulations with their inherent parallelism promise very high speedups. Nevertheless, the quality of Monte Carlo simulations strongly depends on the quality of the employed random numbers. In this work we present a comprehensive analysis of state-of-the-art pseudo random number generators like the MT19937 or the WELL generator used for parallel stream generation in different settings. These random number generators can be realized in hardware as well as in software and help to accelerate the analysis (or simulation) of communications systems. We show that it is possible to generate high-quality parallel random number streams with both generators, as long as some configuration constraints are met. We furthermore depict that distributed simulations with those generator types are viable even to very high degrees of parallelism.

Improving Random Number Generators in the Monte Carlo simulations via twisting and combining

2008 ◽  
Vol 178 (6) ◽  
pp. 401-408 ◽  
Author(s):  
Lih-Yuan Deng ◽  
Rui Guo ◽  
Dennis K.J. Lin ◽  
Fengshan Bai
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generators

Using random number generators in Monte Carlo simulations

1998 ◽  
Vol 58 (4) ◽  
pp. 5183-5184 ◽  
Author(s):  
F. J. Resende ◽  
B. V. Costa
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generators

scholarly journals ERRORS IN MONTE CARLO SIMULATIONS USING SHIFT REGISTER RANDOM NUMBER GENERATORS

1996 ◽  
Vol 06 (06) ◽  
pp. 781-787 ◽  
Author(s):  
F. SCHMID ◽  
N. B. WILDING
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Magnetization Distribution ◽  
Number Generator ◽  
Random Number Generators ◽  
Single Spin ◽  
System Volume

We report large systematic errors in Monte Carlo simulations of the tricritical Blume–Capel model using single spin Metropolis updating. The error, manifest as a 20% asymmetry in the magnetization distribution, is traced to the interplay between strong triplet correlations in the shift register random number generator and the large tricritical clusters. The effect of these correlations is visible only when the system volume is a multiple of the random number generator lag parameter. No such effects are observed in related models.

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