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Random Number Generators


Peter Hellekalek, U Salzburg, PLab, Random Number Generators, here. Nice site. Matsumoto’s Mersenne Twister still seems to be top dog?

We present results and links for this fundamental tool in stochastic simulation and in applied cryptography, some of them due to our own research in this field. Enjoy the data and allow for necessary incompleteness and subjectivity.

An unmoderated collection of network-resouces on random number generators is located on this server as part of the WWW Virtual Library.

A short tour of the information we offer:

  • About UsOn this page, we provide information on our team.

  • GeneratorsWe discuss different types of random number generators and some of their properties.

  • LinksWe provide rather extensive links to people, software and related sites.

  • LiteratureOn this page, you will find references to articles and books that we consider helpful and worth reading.

  • SoftwareIf you need code for random number generation, even for nonuniform distributions: This is your page!

  • TestsAll random number generators have their weak points. We discuss the issue of empirical (statistical) testing of RNGs and provide results as well as links.

Pierre L’Ecuyer, An Object-Oriented Random-Number Package With Many Long Streams and Substreams, here. I was curious if  there was a literature of parallel random  number generators. You are kind of drawn in to thinking about it when you have to put a large MC computation on a grid. For performance reasons it is unpleasant to contemplate having to generate all your random deviates at one time on one proecessor and then have to store them and then send them to a particular grid element for use in the simulation, rather than simply generate them in parallel on the fly.

Multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random number generation. It provides for multiple generators (streams) running simultaneously, and each generator(stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. The basic underlying generator for this implementation is a combined multiple-recursive generator with period length of approximately 2191, proposed by L’Ecuyer(1999a). A C++interface is described here. Portable implementations are available in C, C++, and Java via the online companion to this paper on the Operations Research Web site. 􏰈􏰉.

Pierre L’Ecuyer’s publications, here.


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