On the quality of random number generators with taps

On the quality of random number generators with taps

The large-scale simulations in statistical physics use some deterministic procedures to generate a sequence of uniformly distributed (pseudo)random numbers. It is possible to generate 10 numbers per second and 10 numbers in 100 days on the best processors. The widely known linear congruential methods cannot be used in a such simulations on 32-bit computers because of the short period T < 2 ≈ 10...

Random-number Generators Physical Random-number Generators

R andom numbers have applications in many areas: simulation, game-playing, cryptography, statistical sampling, evaluation of multiple integrals, particletransport calculations, and computations in statistical physics, to name a few. Since each application involves slightly different criteria for judging the “worthiness” of the random numbers generated, a variety of generators have been develope...

Performance and Quality of Random Number Generators

Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for generating pseudo random numbers. Graphical Processing Units (GPUs) are a powerful platform for accelerating...

On Uniform Random Number Generators

On the Reliability of Random Number Generators

In this paper we discuss major problems related to reliability of random number generators used for simulation studies. We propose the decimals of π as the most reliable random number generator as compared to certain real normal numbers as well as all families of pseudorandom number generators. A new property that a random number generator must have is introduced. Two applications are discussed.