Development and testing of high performance pseudo random number generator for Monte Carlo simulation

Random number plays an important role in any Monte Carlo simulation. The accuracy of the results depends on the quality of the sequence of random numbers employed in the simulation. These include randomness of the random numbers, uniformity of their distribution, absence of correlation and long period. In a typical Monte Carlo simulation of particle transport in a nuclear reactor core, the history of a particle from its birth in a fission event until its death by an absorption or leakage event is tracked. The geometry of the core and the surrounding materials are exactly modeled in the simulation. To track a neutron history one needs random numbers for determining inter collision distance, nature of the collision, the direction of the scattered neutron etc. Neutrons are tracked in batches. In one batch approximately 2000-5000 neutrons are tracked. The statistical accuracy of the results of the simulation depends on the total number of particles (number of particles in one batch multiplied by the number of batches) tracked. The number of histories to be generated is usually large for a typical radiation transport problem. To track a very large number of histories one needs to generate a long sequence of independent random numbers. In other words the cycle length of the random number generator (RNG) should be more than the total number of random numbers required for simulating the given transport problem. The number of bits of the machine generally limits the cycle length. For a binary machine of p bits the maximum cycle length is 2p. To achieve higher cycle length in the same machine one has to use either register arithmetic or bit manipulation technique