The RNG library provides a family of linear congruential generators of the form
x[n] = a x[n - 1] + b (modm) , | (2) |
The prime modulus generators provided in the RNG library have the useful feature that the value zero does not occur as a valid random number state. By carefully zeroing a random number state when it is copied or retired, the random number generator routines will trap on the invalid value should the retired random number state be recycled. This has proven to be very useful when debugging applications that implement per-particle random number state.
The interface routines for the random number generators in the RNG library take the general form:
Rng_Type RngXSeed( Rng_UInt32 i, Rng_UInt32 j );
Rng_Type RngXSpawn( Rng_Type *x );
int iRngX( Rng_Type *x );
double dRngX( Rng_Type *x );
float fRngX( Rng_Type *x );
where X is replaced with a token that indicates the type of linear congruential recurrence relation being used for the random number generator.
The RngXSeed() function creates a randomly selected state by passing the two integer arguments through the cryptographic hash function. The resulting random number state is returned. Rng_Type, Rng_UInt32 and the function declarations are defined in the header file rng.h. The routine RngXSpawn() takes an existing RNG state, called the parent, and advances it. A copy of the advanced parent state is then hashed to produce an uncorrelated child RNG state that is returned. As an example of the use of RngXSpawn(), consider the case where a ``parent particle'', parent, spawns three ``child particles'', child[3]. The particles are represented by a struct that has a Rng_Type member named rng. Then, the following lines of code demonstrate the seeding of the children's RNG states using the parent's RNG state.
for( i = 0; i < 3; i++ ) {
child[i].rng = RngXSpawn( &(parent.rng) );
}
Note that parent.rng is advanced three times in this example. Finally, note that both RngXSeed() and RngXSpawn() take 64-bits values, RngXSeed() in the form of two 32-bit unsigned integers, and hash them through the same hashing algorithm described in Section 4.
The last three functions step the random number state and return uniformly distributed numbers in integer and floating point formats. The function iRngX() returns a 31-bit integer in the range [0, 2^{31}). The function dRngX() returns a double precision floating point value in the range [0,1). The function fRngX() returns a single precision floating point value in the range [0,1). If one casts the value returned by dRngX() to a float, it is possible for the value 1.0 to occur due to rounding.