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17. Random Number Generation

The library provides a large collection of random number generators which can be accessed through a uniform interface. Environment variables allow you to select different generators and seeds at runtime, so that you can easily switch between generators without needing to recompile your program. Each instance of a generator keeps track of its own state, allowing the generators to be used in multi-threaded programs. Additional functions are available for transforming uniform random numbers into samples from continuous or discrete probability distributions such as the Gaussian, log-normal or Poisson distributions.

These functions are declared in the header file ‘gsl_rng.h’.

17.1 General comments on random numbers  
17.2 The Random Number Generator Interface  
17.3 Random number generator initialization  
17.4 Sampling from a random number generator  
17.5 Auxiliary random number generator functions  
17.6 Random number environment variables  
17.7 Copying random number generator state  
17.8 Reading and writing random number generator state  
17.9 Random number generator algorithms  
17.10 Unix random number generators  
17.11 Other random number generators  
17.12 Performance  
17.13 Examples  
17.14 References and Further Reading  
17.15 Acknowledgements  
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17.1 General comments on random numbers

In 1988, Park and Miller wrote a paper entitled “Random number generators: good ones are hard to find.” [Commun. ACM, 31, 1192–1201]. Fortunately, some excellent random number generators are available, though poor ones are still in common use. You may be happy with the system-supplied random number generator on your computer, but you should be aware that as computers get faster, requirements on random number generators increase. Nowadays, a simulation that calls a random number generator millions of times can often finish before you can make it down the hall to the coffee machine and back.

A very nice review of random number generators was written by Pierre L'Ecuyer, as Chapter 4 of the book: Handbook on Simulation, Jerry Banks, ed. (Wiley, 1997). The chapter is available in postscript from L'Ecuyer's ftp site (see references). Knuth's volume on Seminumerical Algorithms (originally published in 1968) devotes 170 pages to random number generators, and has recently been updated in its 3rd edition (1997). It is brilliant, a classic. If you don't own it, you should stop reading right now, run to the nearest bookstore, and buy it.

A good random number generator will satisfy both theoretical and statistical properties. Theoretical properties are often hard to obtain (they require real math!), but one prefers a random number generator with a long period, low serial correlation, and a tendency not to “fall mainly on the planes.” Statistical tests are performed with numerical simulations. Generally, a random number generator is used to estimate some quantity for which the theory of probability provides an exact answer. Comparison to this exact answer provides a measure of “randomness”.

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17.2 The Random Number Generator Interface

It is important to remember that a random number generator is not a “real” function like sine or cosine. Unlike real functions, successive calls to a random number generator yield different return values. Of course that is just what you want for a random number generator, but to achieve this effect, the generator must keep track of some kind of “state” variable. Sometimes this state is just an integer (sometimes just the value of the previously generated random number), but often it is more complicated than that and may involve a whole array of numbers, possibly with some indices thrown in. To use the random number generators, you do not need to know the details of what comprises the state, and besides that varies from algorithm to algorithm.

The random number generator library uses two special structs, gsl_rng_type which holds static information about each type of generator and gsl_rng which describes an instance of a generator created from a given gsl_rng_type.

The functions described in this section are declared in the header file ‘gsl_rng.h’.

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17.3 Random number generator initialization

Function: gsl_rng * gsl_rng_alloc (const gsl_rng_type * T)

This function returns a pointer to a newly-created instance of a random number generator of type T. For example, the following code creates an instance of the Tausworthe generator,

 
gsl_rng * r = gsl_rng_alloc (gsl_rng_taus);

If there is insufficient memory to create the generator then the function returns a null pointer and the error handler is invoked with an error code of GSL_ENOMEM.

The generator is automatically initialized with the default seed, gsl_rng_default_seed. This is zero by default but can be changed either directly or by using the environment variable GSL_RNG_SEED (see section Random number environment variables).

The details of the available generator types are described later in this chapter.

Function: void gsl_rng_set (const gsl_rng * r, unsigned long int s)

This function initializes (or `seeds') the random number generator. If the generator is seeded with the same value of s on two different runs, the same stream of random numbers will be generated by successive calls to the routines below. If different values of s are supplied, then the generated streams of random numbers should be completely different. If the seed s is zero then the standard seed from the original implementation is used instead. For example, the original Fortran source code for the ranlux generator used a seed of 314159265, and so choosing s equal to zero reproduces this when using gsl_rng_ranlux.

Function: void gsl_rng_free (gsl_rng * r)

This function frees all the memory associated with the generator r.

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17.4 Sampling from a random number generator

The following functions return uniformly distributed random numbers, either as integers or double precision floating point numbers. To obtain non-uniform distributions see section Random Number Distributions.

Function: unsigned long int gsl_rng_get (const gsl_rng * r)

This function returns a random integer from the generator r. The minimum and maximum values depend on the algorithm used, but all integers in the range [min,max] are equally likely. The values of min and max can determined using the auxiliary functions gsl_rng_max (r) and gsl_rng_min (r).

Function: double gsl_rng_uniform (const gsl_rng * r)

This function returns a double precision floating point number uniformly distributed in the range [0,1). The range includes 0.0 but excludes 1.0. The value is typically obtained by dividing the result of gsl_rng_get(r) by gsl_rng_max(r) + 1.0 in double precision. Some generators compute this ratio internally so that they can provide floating point numbers with more than 32 bits of randomness (the maximum number of bits that can be portably represented in a single unsigned long int).

Function: double gsl_rng_uniform_pos (const gsl_rng * r)

This function returns a positive double precision floating point number uniformly distributed in the range (0,1), excluding both 0.0 and 1.0. The number is obtained by sampling the generator with the algorithm of gsl_rng_uniform until a non-zero value is obtained. You can use this function if you need to avoid a singularity at 0.0.

Function: unsigned long int gsl_rng_uniform_int (const gsl_rng * r, unsigned long int n)

This function returns a random integer from 0 to $n-1$ inclusive by scaling down and/or discarding samples from the generator r. All integers in the range $[0,n-1]$ are produced with equal probability. For generators with a non-zero minimum value an offset is applied so that zero is returned with the correct probability.

Note that this function is designed for sampling from ranges smaller than the range of the underlying generator. The parameter n must be less than or equal to the range of the generator r. If n is larger than the range of the generator then the function calls the error handler with an error code of GSL_EINVAL and returns zero.

In particular, this function is not intended for generating the full range of unsigned integer values $[0,2^32-1]$. Instead choose a generator with the maximal integer range and zero mimimum value, such as gsl_rng_ranlxd1, gsl_rng_mt19937 or gsl_rng_taus, and sample it directly using gsl_rng_get. The range of each generator can be found using the auxiliary functions described in the next section.

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17.5 Auxiliary random number generator functions

The following functions provide information about an existing generator. You should use them in preference to hard-coding the generator parameters into your own code.

Function: const char * gsl_rng_name (const gsl_rng * r)

This function returns a pointer to the name of the generator. For example,

 
printf ("r is a '%s' generator\n", 
        gsl_rng_name (r));

would print something like r is a 'taus' generator.

Function: unsigned long int gsl_rng_max (const gsl_rng * r)

gsl_rng_max returns the largest value that gsl_rng_get can return.

Function: unsigned long int gsl_rng_min (const gsl_rng * r)

gsl_rng_min returns the smallest value that gsl_rng_get can return. Usually this value is zero. There are some generators with algorithms that cannot return zero, and for these generators the minimum value is 1.

Function: void * gsl_rng_state (const gsl_rng * r)
Function: size_t gsl_rng_size (const gsl_rng * r)

These functions return a pointer to the state of generator r and its size. You can use this information to access the state directly. For example, the following code will write the state of a generator to a stream,

 
void * state = gsl_rng_state (r);
size_t n = gsl_rng_size (r);
fwrite (state, n, 1, stream);
Function: const gsl_rng_type ** gsl_rng_types_setup (void)

This function returns a pointer to an array of all the available generator types, terminated by a null pointer. The function should be called once at the start of the program, if needed. The following code fragment shows how to iterate over the array of generator types to print the names of the available algorithms,

 
const gsl_rng_type **t, **t0;

t0 = gsl_rng_types_setup ();

printf ("Available generators:\n");

for (t = t0; *t != 0; t++)
  {
    printf ("%s\n", (*t)->name);
  }
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17.6 Random number environment variables

The library allows you to choose a default generator and seed from the environment variables GSL_RNG_TYPE and GSL_RNG_SEED and the function gsl_rng_env_setup. This makes it easy try out different generators and seeds without having to recompile your program.

Function: const gsl_rng_type * gsl_rng_env_setup (void)

This function reads the environment variables GSL_RNG_TYPE and GSL_RNG_SEED and uses their values to set the corresponding library variables gsl_rng_default and gsl_rng_default_seed. These global variables are defined as follows,

 
extern const gsl_rng_type *gsl_rng_default
extern unsigned long int gsl_rng_default_seed

The environment variable GSL_RNG_TYPE should be the name of a generator, such as taus or mt19937. The environment variable GSL_RNG_SEED should contain the desired seed value. It is converted to an unsigned long int using the C library function strtoul.

If you don't specify a generator for GSL_RNG_TYPE then gsl_rng_mt19937 is used as the default. The initial value of gsl_rng_default_seed is zero.

Here is a short program which shows how to create a global generator using the environment variables GSL_RNG_TYPE and GSL_RNG_SEED,

 
#include <stdio.h>
#include <gsl/gsl_rng.h>

gsl_rng * r;  /* global generator */

int
main (void)
{
  const gsl_rng_type * T;

  gsl_rng_env_setup();

  T = gsl_rng_default;
  r = gsl_rng_alloc (T);
  
  printf ("generator type: %s\n", gsl_rng_name (r));
  printf ("seed = %lu\n", gsl_rng_default_seed);
  printf ("first value = %lu\n", gsl_rng_get (r));

  gsl_rng_free (r);
  return 0;
}

Running the program without any environment variables uses the initial defaults, an mt19937 generator with a seed of 0,

 
$ ./a.out 

generator type: mt19937
seed = 0
first value = 4293858116

By setting the two variables on the command line we can change the default generator and the seed,

 
$ GSL_RNG_TYPE="taus" GSL_RNG_SEED=123 ./a.out 
GSL_RNG_TYPE=taus
GSL_RNG_SEED=123
generator type: taus
seed = 123
first value = 2720986350
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17.7 Copying random number generator state

The above methods do not expose the random number `state' which changes from call to call. It is often useful to be able to save and restore the state. To permit these practices, a few somewhat more advanced functions are supplied. These include:

Function: int gsl_rng_memcpy (gsl_rng * dest, const gsl_rng * src)

This function copies the random number generator src into the pre-existing generator dest, making dest into an exact copy of src. The two generators must be of the same type.

Function: gsl_rng * gsl_rng_clone (const gsl_rng * r)

This function returns a pointer to a newly created generator which is an exact copy of the generator r.

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17.8 Reading and writing random number generator state

The library provides functions for reading and writing the random number state to a file as binary data or formatted text.

Function: int gsl_rng_fwrite (FILE * stream, const gsl_rng * r)

This function writes the random number state of the random number generator r to the stream stream in binary format. The return value is 0 for success and GSL_EFAILED if there was a problem writing to the file. Since the data is written in the native binary format it may not be portable between different architectures.

Function: int gsl_rng_fread (FILE * stream, gsl_rng * r)

This function reads the random number state into the random number generator r from the open stream stream in binary format. The random number generator r must be preinitialized with the correct random number generator type since type information is not saved. The return value is 0 for success and GSL_EFAILED if there was a problem reading from the file. The data is assumed to have been written in the native binary format on the same architecture.

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17.9 Random number generator algorithms

The functions described above make no reference to the actual algorithm used. This is deliberate so that you can switch algorithms without having to change any of your application source code. The library provides a large number of generators of different types, including simulation quality generators, generators provided for compatibility with other libraries and historical generators from the past.

The following generators are recommended for use in simulation. They have extremely long periods, low correlation and pass most statistical tests. For the most reliable source of uncorrelated numbers, the second-generation RANLUX generators have the strongest proof of randomness.

Generator: gsl_rng_mt19937

The MT19937 generator of Makoto Matsumoto and Takuji Nishimura is a variant of the twisted generalized feedback shift-register algorithm, and is known as the “Mersenne Twister” generator. It has a Mersenne prime period of $2^19937 - 1$ (about $10^6000$) and is equi-distributed in 623 dimensions. It has passed the DIEHARD statistical tests. It uses 624 words of state per generator and is comparable in speed to the other generators. The original generator used a default seed of 4357 and choosing s equal to zero in gsl_rng_set reproduces this. Later versions switched to 5489 as the default seed, you can choose this explicitly via gsl_rng_set instead if you require it.

For more information see,

The generator gsl_rng_mt19937 uses the second revision of the seeding procedure published by the two authors above in 2002. The original seeding procedures could cause spurious artifacts for some seed values. They are still available through the alternative generators gsl_rng_mt19937_1999 and gsl_rng_mt19937_1998.

Generator: gsl_rng_ranlxs0
Generator: gsl_rng_ranlxs1
Generator: gsl_rng_ranlxs2

The generator ranlxs0 is a second-generation version of the RANLUX algorithm of Lüscher, which produces “luxury random numbers”. This generator provides single precision output (24 bits) at three luxury levels ranlxs0, ranlxs1 and ranlxs2, in increasing order of strength. It uses double-precision floating point arithmetic internally and can be significantly faster than the integer version of ranlux, particularly on 64-bit architectures. The period of the generator is about $10^171$. The algorithm has mathematically proven properties and can provide truly decorrelated numbers at a known level of randomness. The higher luxury levels provide increased decorrelation between samples as an additional safety margin.

Generator: gsl_rng_ranlxd1
Generator: gsl_rng_ranlxd2

These generators produce double precision output (48 bits) from the RANLXS generator. The library provides two luxury levels ranlxd1 and ranlxd2, in increasing order of strength.

Generator: gsl_rng_ranlux
Generator: gsl_rng_ranlux389

The ranlux generator is an implementation of the original algorithm developed by Lüscher. It uses a lagged-fibonacci-with-skipping algorithm to produce “luxury random numbers”. It is a 24-bit generator, originally designed for single-precision IEEE floating point numbers. This implementation is based on integer arithmetic, while the second-generation versions RANLXS and RANLXD described above provide floating-point implementations which will be faster on many platforms. The period of the generator is about $10^171$. The algorithm has mathematically proven properties and it can provide truly decorrelated numbers at a known level of randomness. The default level of decorrelation recommended by Lüscher is provided by gsl_rng_ranlux, while gsl_rng_ranlux389 gives the highest level of randomness, with all 24 bits decorrelated. Both types of generator use 24 words of state per generator.

For more information see,

Generator: gsl_rng_cmrg

This is a combined multiple recursive generator by L'Ecuyer. Its sequence is, where the two underlying generators $x_n$ and $y_n$ are, with coefficients $a_1 = 0$, $a_2 = 63308$, $a_3 = -183326$, $b_1 = 86098$, $b_2 = 0$, $b_3 = -539608$, and moduli $m_1 = 2^31 - 1 = 2147483647$ and $m_2 = 2145483479$.

The period of this generator is $lcm(m_1^3-1, m_2^3-1)$, which is approximately $2^185$ (about $10^56$). It uses 6 words of state per generator. For more information see,

Generator: gsl_rng_mrg

This is a fifth-order multiple recursive generator by L'Ecuyer, Blouin and Coutre. Its sequence is, with $a_1 = 107374182$, $a_2 = a_3 = a_4 = 0$, $a_5 = 104480$ and $m = 2^31 - 1$.

The period of this generator is about $10^46$. It uses 5 words of state per generator. More information can be found in the following paper,

Generator: gsl_rng_taus
Generator: gsl_rng_taus2

This is a maximally equidistributed combined Tausworthe generator by L'Ecuyer. The sequence is, where, computed modulo $2^32$. In the formulas above $^^$ denotes “exclusive-or”. Note that the algorithm relies on the properties of 32-bit unsigned integers and has been implemented using a bitmask of 0xFFFFFFFF to make it work on 64 bit machines.

The period of this generator is $2^88$ (about $10^26$). It uses 3 words of state per generator. For more information see,

The generator gsl_rng_taus2 uses the same algorithm as gsl_rng_taus but with an improved seeding procedure described in the paper,

The generator gsl_rng_taus2 should now be used in preference to gsl_rng_taus.

Generator: gsl_rng_gfsr4

The gfsr4 generator is like a lagged-fibonacci generator, and produces each number as an xor'd sum of four previous values.

Ziff (ref below) notes that “it is now widely known” that two-tap registers (such as R250, which is described below) have serious flaws, the most obvious one being the three-point correlation that comes from the definition of the generator. Nice mathematical properties can be derived for GFSR's, and numerics bears out the claim that 4-tap GFSR's with appropriately chosen offsets are as random as can be measured, using the author's test.

This implementation uses the values suggested the example on p392 of Ziff's article: $A=471$, $B=1586$, $C=6988$, $D=9689$.

If the offsets are appropriately chosen (such as the one ones in this implementation), then the sequence is said to be maximal; that means that the period is $2^D - 1$, where $D$ is the longest lag. (It is one less than $2^D$ because it is not permitted to have all zeros in the ra[] array.) For this implementation with $D=9689$ that works out to about $10^2917$.

Note that the implementation of this generator using a 32-bit integer amounts to 32 parallel implementations of one-bit generators. One consequence of this is that the period of this 32-bit generator is the same as for the one-bit generator. Moreover, this independence means that all 32-bit patterns are equally likely, and in particular that 0 is an allowed random value. (We are grateful to Heiko Bauke for clarifying for us these properties of GFSR random number generators.)

For more information see,

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17.10 Unix random number generators

The standard Unix random number generators rand, random and rand48 are provided as part of GSL. Although these generators are widely available individually often they aren't all available on the same platform. This makes it difficult to write portable code using them and so we have included the complete set of Unix generators in GSL for convenience. Note that these generators don't produce high-quality randomness and aren't suitable for work requiring accurate statistics. However, if you won't be measuring statistical quantities and just want to introduce some variation into your program then these generators are quite acceptable.

Generator: gsl_rng_rand

This is the BSD rand generator. Its sequence is with $a = 1103515245$, $c = 12345$ and $m = 2^31$. The seed specifies the initial value, $x_1$. The period of this generator is $2^31$, and it uses 1 word of storage per generator.

Generator: gsl_rng_random_bsd
Generator: gsl_rng_random_libc5
Generator: gsl_rng_random_glibc2

These generators implement the random family of functions, a set of linear feedback shift register generators originally used in BSD Unix. There are several versions of random in use today: the original BSD version (e.g. on SunOS4), a libc5 version (found on older GNU/Linux systems) and a glibc2 version. Each version uses a different seeding procedure, and thus produces different sequences.

The original BSD routines accepted a variable length buffer for the generator state, with longer buffers providing higher-quality randomness. The random function implemented algorithms for buffer lengths of 8, 32, 64, 128 and 256 bytes, and the algorithm with the largest length that would fit into the user-supplied buffer was used. To support these algorithms additional generators are available with the following names,

 
gsl_rng_random8_bsd
gsl_rng_random32_bsd
gsl_rng_random64_bsd
gsl_rng_random128_bsd
gsl_rng_random256_bsd

where the numeric suffix indicates the buffer length. The original BSD random function used a 128-byte default buffer and so gsl_rng_random_bsd has been made equivalent to gsl_rng_random128_bsd. Corresponding versions of the libc5 and glibc2 generators are also available, with the names gsl_rng_random8_libc5, gsl_rng_random8_glibc2, etc.

Generator: gsl_rng_rand48

This is the Unix rand48 generator. Its sequence is defined on 48-bit unsigned integers with $a = 25214903917$, $c = 11$ and $m = 2^48$. The seed specifies the upper 32 bits of the initial value, $x_1$, with the lower 16 bits set to 0x330E. The function gsl_rng_get returns the upper 32 bits from each term of the sequence. This does not have a direct parallel in the original rand48 functions, but forcing the result to type long int reproduces the output of mrand48. The function gsl_rng_uniform uses the full 48 bits of internal state to return the double precision number $x_n/m$, which is equivalent to the function drand48. Note that some versions of the GNU C Library contained a bug in mrand48 function which caused it to produce different results (only the lower 16-bits of the return value were set).

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17.11 Other random number generators

The generators in this section are provided for compatibility with existing libraries. If you are converting an existing program to use GSL then you can select these generators to check your new implementation against the original one, using the same random number generator. After verifying that your new program reproduces the original results you can then switch to a higher-quality generator.

Note that most of the generators in this section are based on single linear congruence relations, which are the least sophisticated type of generator. In particular, linear congruences have poor properties when used with a non-prime modulus, as several of these routines do (e.g. with a power of two modulus, $2^31$ or $2^32$). This leads to periodicity in the least significant bits of each number, with only the higher bits having any randomness. Thus if you want to produce a random bitstream it is best to avoid using the least significant bits.

Generator: gsl_rng_ranf

This is the CRAY random number generator RANF. Its sequence is defined on 48-bit unsigned integers with $a = 44485709377909$ and $m = 2^48$. The seed specifies the lower 32 bits of the initial value, $x_1$, with the lowest bit set to prevent the seed taking an even value. The upper 16 bits of $x_1$ are set to 0. A consequence of this procedure is that the pairs of seeds 2 and 3, 4 and 5, etc produce the same sequences.

The generator compatible with the CRAY MATHLIB routine RANF. It produces double precision floating point numbers which should be identical to those from the original RANF.

There is a subtlety in the implementation of the seeding. The initial state is reversed through one step, by multiplying by the modular inverse of $a$ mod $m$. This is done for compatibility with the original CRAY implementation.

Note that you can only seed the generator with integers up to $2^32$, while the original CRAY implementation uses non-portable wide integers which can cover all $2^48$ states of the generator.

The function gsl_rng_get returns the upper 32 bits from each term of the sequence. The function gsl_rng_uniform uses the full 48 bits to return the double precision number $x_n/m$.

The period of this generator is $2^46$.

Generator: gsl_rng_ranmar

This is the RANMAR lagged-fibonacci generator of Marsaglia, Zaman and Tsang. It is a 24-bit generator, originally designed for single-precision IEEE floating point numbers. It was included in the CERNLIB high-energy physics library.

Generator: gsl_rng_r250

This is the shift-register generator of Kirkpatrick and Stoll. The sequence is based on the recurrence where $^^$ denotes “exclusive-or”, defined on 32-bit words. The period of this generator is about $2^250$ and it uses 250 words of state per generator.

For more information see,

Generator: gsl_rng_tt800

This is an earlier version of the twisted generalized feedback shift-register generator, and has been superseded by the development of MT19937. However, it is still an acceptable generator in its own right. It has a period of $2^800$ and uses 33 words of storage per generator.

For more information see,

Generator: gsl_rng_vax

This is the VAX generator MTH$RANDOM. Its sequence is, with $a = 69069$, $c = 1$ and $m = 2^32$. The seed specifies the initial value, $x_1$. The period of this generator is $2^32$ and it uses 1 word of storage per generator.

Generator: gsl_rng_transputer

This is the random number generator from the INMOS Transputer Development system. Its sequence is, with $a = 1664525$ and $m = 2^32$. The seed specifies the initial value, $x_1$.

Generator: gsl_rng_randu

This is the IBM RANDU generator. Its sequence is with $a = 65539$ and $m = 2^31$. The seed specifies the initial value, $x_1$. The period of this generator was only $2^29$. It has become a textbook example of a poor generator.

Generator: gsl_rng_minstd

This is Park and Miller's “minimal standard” MINSTD generator, a simple linear congruence which takes care to avoid the major pitfalls of such algorithms. Its sequence is, with $a = 16807$ and $m = 2^31 - 1 = 2147483647$. The seed specifies the initial value, $x_1$. The period of this generator is about $2^31$.

This generator is used in the IMSL Library (subroutine RNUN) and in MATLAB (the RAND function). It is also sometimes known by the acronym “GGL” (I'm not sure what that stands for).

For more information see,

Generator: gsl_rng_uni
Generator: gsl_rng_uni32

This is a reimplementation of the 16-bit SLATEC random number generator RUNIF. A generalization of the generator to 32 bits is provided by gsl_rng_uni32. The original source code is available from NETLIB.

Generator: gsl_rng_slatec

This is the SLATEC random number generator RAND. It is ancient. The original source code is available from NETLIB.

Generator: gsl_rng_zuf

This is the ZUFALL lagged Fibonacci series generator of Peterson. Its sequence is,

The original source code is available from NETLIB. For more information see,

Generator: gsl_rng_knuthran2

This is a second-order multiple recursive generator described by Knuth in Seminumerical Algorithms, 3rd Ed., page 108. Its sequence is, with $a_1 = 271828183$, $a_2 = 314159269$, and $m = 2^31 - 1$.

Generator: gsl_rng_knuthran2002
Generator: gsl_rng_knuthran

This is a second-order multiple recursive generator described by Knuth in Seminumerical Algorithms, 3rd Ed., Section 3.6. Knuth provides its C code. The updated routine gsl_rng_knuthran2002 is from the revised 9th printing and corrects some weaknesses in the earlier version, which is implemented as gsl_rng_knuthran.

Generator: gsl_rng_borosh13
Generator: gsl_rng_fishman18
Generator: gsl_rng_fishman20
Generator: gsl_rng_lecuyer21
Generator: gsl_rng_waterman14

These multiplicative generators are taken from Knuth's Seminumerical Algorithms, 3rd Ed., pages 106–108. Their sequence is, where the seed specifies the initial value, $x_1$. The parameters $a$ and $m$ are as follows, Borosh-Niederreiter: $a = 1812433253$, $m = 2^32$, Fishman18: $a = 62089911$, $m = 2^31 - 1$, Fishman20: $a = 48271$, $m = 2^31 - 1$, L'Ecuyer: $a = 40692$, $m = 2^31 - 249$, Waterman: $a = 1566083941$, $m = 2^32$.

Generator: gsl_rng_fishman2x

This is the L'Ecuyer–Fishman random number generator. It is taken from Knuth's Seminumerical Algorithms, 3rd Ed., page 108. Its sequence is, with $m = 2^31 - 1$. $x_n$ and $y_n$ are given by the fishman20 and lecuyer21 algorithms. The seed specifies the initial value, $x_1$.

Generator: gsl_rng_coveyou

This is the Coveyou random number generator. It is taken from Knuth's Seminumerical Algorithms, 3rd Ed., Section 3.2.2. Its sequence is, with $m = 2^32$. The seed specifies the initial value, $x_1$.

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17.12 Performance

The following table shows the relative performance of a selection the available random number generators. The fastest simulation quality generators are taus, gfsr4 and mt19937. The generators which offer the best mathematically-proven quality are those based on the RANLUX algorithm.

 
1754 k ints/sec,    870 k doubles/sec, taus
1613 k ints/sec,    855 k doubles/sec, gfsr4
1370 k ints/sec,    769 k doubles/sec, mt19937
 565 k ints/sec,    571 k doubles/sec, ranlxs0
 400 k ints/sec,    405 k doubles/sec, ranlxs1
 490 k ints/sec,    389 k doubles/sec, mrg
 407 k ints/sec,    297 k doubles/sec, ranlux
 243 k ints/sec,    254 k doubles/sec, ranlxd1
 251 k ints/sec,    253 k doubles/sec, ranlxs2
 238 k ints/sec,    215 k doubles/sec, cmrg
 247 k ints/sec,    198 k doubles/sec, ranlux389
 141 k ints/sec,    140 k doubles/sec, ranlxd2

1852 k ints/sec,    935 k doubles/sec, ran3
 813 k ints/sec,    575 k doubles/sec, ran0
 787 k ints/sec,    476 k doubles/sec, ran1
 379 k ints/sec,    292 k doubles/sec, ran2
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17.13 Examples

The following program demonstrates the use of a random number generator to produce uniform random numbers in the range [0.0, 1.0),

 
#include <stdio.h>
#include <gsl/gsl_rng.h>

int
main (void)
{
  const gsl_rng_type * T;
  gsl_rng * r;

  int i, n = 10;

  gsl_rng_env_setup();

  T = gsl_rng_default;
  r = gsl_rng_alloc (T);

  for (i = 0; i < n; i++) 
    {
      double u = gsl_rng_uniform (r);
      printf ("%.5f\n", u);
    }

  gsl_rng_free (r);

  return 0;
}

Here is the output of the program,

 
$ ./a.out 

0.99974
0.16291
0.28262
0.94720
0.23166
0.48497
0.95748
0.74431
0.54004
0.73995

The numbers depend on the seed used by the generator. The default seed can be changed with the GSL_RNG_SEED environment variable to produce a different stream of numbers. The generator itself can be changed using the environment variable GSL_RNG_TYPE. Here is the output of the program using a seed value of 123 and the multiple-recursive generator mrg,

 
$ GSL_RNG_SEED=123 GSL_RNG_TYPE=mrg ./a.out 

GSL_RNG_TYPE=mrg
GSL_RNG_SEED=123
0.33050
0.86631
0.32982
0.67620
0.53391
0.06457
0.16847
0.70229
0.04371
0.86374
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17.14 References and Further Reading

The subject of random number generation and testing is reviewed extensively in Knuth's Seminumerical Algorithms.

Further information is available in the review paper written by Pierre L'Ecuyer,

The source code for the DIEHARD random number generator tests is also available online,

A comprehensive set of random number generator tests is available from NIST,

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17.15 Acknowledgements

Thanks to Makoto Matsumoto, Takuji Nishimura and Yoshiharu Kurita for making the source code to their generators (MT19937, MM&TN; TT800, MM&YK) available under the GNU General Public License. Thanks to Martin Lüscher for providing notes and source code for the RANLXS and RANLXD generators.

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