Many Random Number Generators (RNG) are available nowadays; they are divided in two categories, hardware RNG, that provide “true” random numbers, and algorithmic RNG, that generate pseudo random numbers (PRNG). Both types usually generate random numbers (Xn)n as independent uniform samples in a range 0, . . . 2 − 1, with b = 8, 16, 32 or b = 64. In applications, it is instead sometimes desirabl...

The generation of uniform pseudo-random numbers between 0 and 1 is important in many numerical simulations. The purpose of this report is to explore the best generator(s) of such random numbers in terms of statistical properties and speed. While attempting to find the best generator in general, the specific goal of this report is to find the best generator for Latin hypercube sampling [Iman and...

The venerable Linux /dev/random served users of cryptographic mechanisms well for a long time. Its behavior is well understood to deliver entropic data. In the last years, however, the Linux /dev/random showed signs of age where it has challenges to cope with modern computing environments ranging from tiny embedded systems, over new hardware resources such as SSDs, up to massive parallel system...

A random number generator (often abbreviated as RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. Computer-based systems for random number generation are widely used, but often fall short of this goal, though they may meet some statistical tests for randomness intended to ensure that they do not have any e...