Initializing generalized feedback shift register pseudorandom number generators
Journal of the ACM (JACM)
How to construct random functions
Journal of the ACM (JACM)
Introduction to finite fields and their applications
Introduction to finite fields and their applications
A method for vectorized random number generators
Journal of Computational Physics
The use of Cebysev mixing to generate pseudo-random numbers
Journal of Computational Physics
Stochastic simulation
An exhaustive analysis of multiplicative congruential random number generators with modulus 231-1
SIAM Journal on Scientific and Statistical Computing
A simple unpredictable pseudo random number generator
SIAM Journal on Computing
A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Walsh-spectral test for GFSR pseudorandom numbers
Communications of the ACM
On the discrepancy of GFSR pseudorandom numbers
Journal of the ACM (JACM)
Maximum-length sequences, cellular automata, and random numbers
Journal of Computational Physics
A statistical analysis of generalized feedback shift register pseudorandom number generators
SIAM Journal on Scientific and Statistical Computing
Algorithmics: theory & practice
Algorithmics: theory & practice
Designing a uniform random number generator whose subsequences are k-distributed
SIAM Journal on Computing
RSA and Rabin functions: certain parts are as hard as the whole
SIAM Journal on Computing - Special issue on cryptography
Unbiased bits from sources of weak randomness and probabilistic communication complexity
SIAM Journal on Computing - Special issue on cryptography
Efficient parallel pseudorandom number generation
SIAM Journal on Computing - Special issue on cryptography
The lattice structure of pseudo-random vectors generated by matrix generators
Journal of Computational and Applied Mathematics
Uses and abuses of statistical simulation
Mathematical Programming: Series A and B - Mathematical Models and Their Solutions
Parallelization of random number generators and long-range correlations
Numerische Mathematik
Inferring sequences produced by pseudo-random number generators
Journal of the ACM (JACM)
Efficient and portable combined random number generators
Communications of the ACM
Random number generators: good ones are hard to find
Communications of the ACM
Random number generators for MIMD parallel processors
Journal of Parallel and Distributed Computing
ACORN—A new method for generating sequences of uniformly distributed Pseudo-random numbers
Journal of Computational Physics
Journal of Computational Physics
Parallel processing of random number generation for Monte Carlo turbulence simulation
Journal of Computational Physics
A fast uniform astronomical random number generator
ACM SIGSAC Review
Two fast implementations of the “minimal standard” random number generator
Communications of the ACM
Random number generation on parallel processors
WSC '89 Proceedings of the 21st conference on Winter simulation
Using linear congruential generators for parallel random number generation
WSC '89 Proceedings of the 21st conference on Winter simulation
About polynomial-time “unpredictable” generators
WSC '89 Proceedings of the 21st conference on Winter simulation
Statistical independence properties of pseudorandom vectors produced by matrix generators
Journal of Computational and Applied Mathematics - Random numbers and simulation
Implementing a random number package with splitting facilities
ACM Transactions on Mathematical Software (TOMS)
Recent trends in random number and random vector generation
Annals of Operations Research
The multiple prime random number generator
ACM Transactions on Mathematical Software (TOMS)
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Linear congruential generators of order K1
WSC '88 Proceedings of the 20th conference on Winter simulation
Generalized Feedback Shift Register Pseudorandom Number Algorithm
Journal of the ACM (JACM)
Partitioning the Period of a Class of m-Sequences and Application to Pseudorandom Number Generation
Journal of the ACM (JACM)
The k-distribution of generalized feedback shift register pseudorandom numbers
Communications of the ACM
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Efficient and portable combined Tausworthe random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
On the lattice structure of the add-with-carry and subtract-with-borrow random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
New methods for pseudorandom numbers and pseudorandom vector generation
WSC '92 Proceedings of the 24th conference on Winter simulation
Fast and reliable random-number generation
WSC '92 Proceedings of the 24th conference on Winter simulation
Analysis of add-with-carry and subtract-with-borrow generators
WSC '92 Proceedings of the 24th conference on Winter simulation
A portable random number generator well suited for the rejection method
ACM Transactions on Mathematical Software (TOMS)
A search for good multiple recursive random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Pseudorandom vector generation by the inversive method
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Nonparametric techniques in simulation analysis: a tutorial
WSC '94 Proceedings of the 26th conference on Winter simulation
Using nonparametric statistics in simulation analysis: a review
WSC '95 Proceedings of the 27th conference on Winter simulation
Uniform random number generators: a review
Proceedings of the 29th conference on Winter simulation
Techniques for empirical testing of parallel random number generators
ICS '98 Proceedings of the 12th international conference on Supercomputing
Uniform random number generators
Proceedings of the 30th conference on Winter simulation
Combining random number generators
WSC '91 Proceedings of the 23rd conference on Winter simulation
Computational Study of the Relationships Between Feasible and Efficient Sets and an Approximation
Computational Optimization and Applications
Algorithm 806: SPRNG: a scalable library for pseudorandom number generation
ACM Transactions on Mathematical Software (TOMS)
Generalized Lehmer-Tausworthe random number generators
ACM-SE 30 Proceedings of the 30th annual Southeast regional conference
Proceedings of the 32nd conference on Winter simulation
Software for uniform random number generation: distinguishing the good and the bad
Proceedings of the 33nd conference on Winter simulation
A system of high-dimensional, efficient, long-cycle and portable uniform random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
TestU01: A C library for empirical testing of random number generators
ACM Transactions on Mathematical Software (TOMS)
The additive congruential random number generator-A special case of a multiple recursive generator
Journal of Computational and Applied Mathematics
A plug-in-based architecture for random number generation in simulation systems
Proceedings of the 40th Conference on Winter Simulation
E = MC3: managing uncertain enterprise data in a cluster-computing environment
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Random number generators with period divisible by a Mersenne prime
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Design considerations for M&S software
Winter Simulation Conference
Fast and reliable random number generators for scientific computing
PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
On the combination of self-organized systems to generate pseudo-random numbers
Information Sciences: an International Journal
Hi-index | 0.00 |
In the mind of the average computer user, the problem of generating uniform variates by computer has been solved long ago. After all, every computer :system offers one or more function(s) to do so. Many software products, like compilers, spreadsheets, statistical or numerical packages, etc. also offer their own. These functions supposedly return numbers that could be used, for all practical purposes, as if they were the values taken by independent random variables, with a uniform distribution between 0 and 1. Many people use them with faith and feel happy with the results. So, why bother?Other (less naive) people do not feel happy with the results and with good reasons. Despite renewed crusades, blatantly bad generators still abound, especially on microcomputers [55, 69, 85, 90, 100]. Other generators widely used on medium-sized computers are perhaps not so spectacularly bad, but still fail some theoretical and/or empirical statistical tests, and/or generate easily detectable regular patterns [56, 65].Fortunately, many applications appear quite robust to these defects. But with the rapid increase in desktop computing power, increasingly sophisticated simulation studies are being performed that require more and more “random” numbers and whose results are more sensitive to the quality of the underlying generator [28, 40, 65, 90]. Sometimes, using a not-so-good generator can give totally misleading results. Perhaps this happens rarely, but can be disastrous in some cases. For that reason, researchers are still actively investigating ways of building generators. The main goal is to design more robust generators without having to pay too much in terms of portability, flexibility, and efficiency. In the following sections, we give a quick overview of the ongoing research. We focus mainly on efficient and recently proposed techniques for generating uniform pseudorandom numbers. Stochastic simulations typically transform such numbers to generate variates according to more complex distributions [13, 25]. Here, “uniform pseudorandom” means that the numbers behave from the outside as if they were the values of i.i.d. random variables, uniformly distributed over some finite set of symbols. This set of symbols is often a set of integers of the form {0, . . . , m - 1} and the symbols are usually transformed by some function into values between 0 and 1, to approximate the U(0, 1) distribution. Other tutorial-like references on uniform variate generation include [13, 23, 52, 54, 65, 84, 89].