A Uniform Random Number Generator (RNG)

UNIFORM is a C library which returns a sequence of uniformly distributed pseudorandom numbers.

The fundamental underlying random number generator is based on a simple, old, and limited linear congruential random number generator originally used in the IBM System 360. If you want state of the art random number generation, look elsewhere!

The C math library already has random number functions, which can return pseudorandom numbers rapidly, in bulk, and generally with less correlation than UNIFORM provides.

However, this library makes it possible to compare certain computations that use uniform random numbers, written in C, C++, FORTRAN77, FORTRAN90, Mathematica, MATLAB or Python.

Various types of random data can be computed. The routine names are chosen to indicate the corresponding type:

In some cases, a one dimensional vector or two dimensional array of values is to be generated, and part of the name will therefore include:

The underlying random numbers are generally defined over some unit interval or region. Routines are available which return these "unit" values, while other routines allow the user to specify limits between which the unit values are rescaled. The name of a routine will usually include a tag suggestig which is the case:

The random number generator embodied here is not very sophisticated. It will not have the best properties of distribution, noncorrelation and long period. It is not the purpose of this library to achieve such worthy goals. This is simply a reasonably portable library that can be implemented in various languages, on various machines, and for which it is possible, for instance, to regard the output as a function of the seed, and moreover, to work directly with the sequence of seeds, if necessary.


The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.


UNIFORM is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version and a Python version.

Related Data and Programs:

ASA183, a C library which implements the Wichman-Hill pseudorandom number generator.

C_RANDOM, C programs which illustrate the use of C's random number generator routines.

HAMMERSLEY, a C library which computes elements of a Hammersley Quasi Monte Carlo (QMC) sequence, using a simple interface.

LATIN_RANDOM, a C library which computes a Latin Hypercube in M dimensions of N points, randomly placed within their subsquares.

NORMAL, a C library which computes elements of a sequence of pseudorandom normally distributed values.

RANDLC, a C library which generates a sequence of pseudorandom numbers, used by the NAS Benchmark programs.

RANDOM_SORTED, a C library which generates vectors of random values which are already sorted.

RANLIB, a C library which produces random samples from Probability Density Functions (PDF's), including Beta, Chi-square Exponential, F, Gamma, Multivariate normal, Noncentral chi-square, Noncentral F, Univariate normal, random permutations, Real uniform, Binomial, Negative Binomial, Multinomial, Poisson and Integer uniform, by Barry Brown and James Lovato.

RBOX, a C program which generates a set of points in a region, selected at random according to a given distribution.

RNGLIB, a C library which implements a random number generator (RNG) with splitting facilities, allowing multiple independent streams to be computed, by L'Ecuyer and Cote.

VAN_DER_CORPUT, a C library which computes elements of a 1D van der Corput Quasi Monte Carlo (QMC) sequence using a simple interface.

ZIGGURAT, a C library which computes elements of uniform, normal or exponential pseudorandom sequence using the ziggurat method.


  1. Paul Bratley, Bennett Fox, Linus Schrage,
    A Guide to Simulation,
    Second Edition,
    Springer, 1987,
    ISBN: 0387964673,
    LC: QA76.9.C65.B73.
  2. Bennett Fox,
    Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators,
    ACM Transactions on Mathematical Software,
    Volume 12, Number 4, December 1986, pages 362-376.
  3. Donald Knuth,
    The Art of Computer Programming,
    Volume 2, Seminumerical Algorithms,
    Third Edition,
    Addison Wesley, 1997,
    ISBN: 0201896842,
    LC: QA76.6.K64.
  4. Pierre LEcuyer,
    Random Number Generation,
    in Handbook of Simulation,
    edited by Jerry Banks,
    Wiley, 1998,
    ISBN: 0471134031,
    LC: T57.62.H37.
  5. Peter Lewis, Allen Goodman, James Miller,
    A Pseudo-Random Number Generator for the System/360,
    IBM Systems Journal,
    Volume 8, Number 2, 1969, pages 136-143.
  6. Stephen Park, Keith Miller,
    Random Number Generators: Good Ones are Hard to Find,
    Communications of the ACM,
    Volume 31, Number 10, October 1988, pages 1192-1201.
  7. Eric Weisstein,
    CRC Concise Encyclopedia of Mathematics,
    CRC Press, 2002,
    Second edition,
    ISBN: 1584883472,
    LC: QA5.W45.
  8. Barry Wilkinson, Michael Allen,
    Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers,
    Prentice Hall,
    ISBN: 0-13-140563-2,
    LC: QA76.642.W54.

Source Code:

Examples and Tests:

List of Routines:

You can go up one level to the C source codes.

Last revised on 15 January 2012.