URNG: A portable optimization technique for software applications requiring pseudo-random numbers

Research software involving stochastic behaviour often requires millions of random numbers. In addition to the quality of the pseudo-random number generator (PRNG), the speed of the algorithm and the ease of its implementation are common practical aspects. In this work we will discuss how to optimize the access speed to random numbers independently from the generation algorithm. We propose an additional implementation technique in order to speed up any kind of PRNG taking into account the capacities of current computers and microcomputers. The speed of our solution stems from the classical unrolling optimization technique, it is named the URNG technique (unrolled random number generator). Random numbers are first generated in source code, then precompiled and stored inside the RAM of inexpensive computers at the executable loading time. With this technique random numbers need to be computed only once. The URNG technique is compliant with parallel computing. Limits and effects on speed and sensitivity are explored over four computer generations with a simple Monte Carlo simulation. Every research field using stochastic computation can be concerned by this software optimization technique which is currently limited to applications requiring not more than a few hundred millions of pseudo-random numbers.