A fast vectorised implementation of Wallace's normal random number generator

Wallace has proposed a new class of pseudo-random generators for normal variates. These generators do not require a stream of uniform pseudo-random numbers, except for initialisation. The inner loops are essentially matrix-vector multiplications and are very suitable for implementation on vector processors or vector/parallel processors such as the Fujitsu VPP300. In this report we outline Wallace's idea, consider some variations on it, and describe a vectorised implementation RANN4 which is more than three times faster than its best competitors (the Polar and Box-Muller methods) on the Fujitsu VP2200 and VPP300

Some comments on C. S. Wallace's random number generators

We outline some of Chris Wallace's contributions to pseudo-random number generation. In particular, we consider his idea for generating normally distributed variates without relying on a source of uniform random numbers, and compare it with more conventional methods for generating normal random numbers. Implementations of Wallace's idea can be very fast (approximately as fast as good uniform generators). We discuss the statistical quality of the output, and mention how certain pitfalls can be avoided.Comment: 13 pages. For further information, see http://wwwmaths.anu.edu.au/~brent/pub/pub213.htm

Algorithms for randomness in the behavioral sciences: A tutorial

Simulations and experiments frequently demand the generation of random numbera that have specific distributions. This article describes which distributions should be used for the most cammon problems and gives algorithms to generate the numbers.It is also shown that a commonly used permutation algorithm (Nilsson, 1978) is deficient