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Monte-Carlo simulations are common and inherently well suited to parallel processing, thus requiring random numbers that are also generated in parallel. We describe here a splitting approach for parallel random number generation. Various definitions of the Monte-Carlo method have been given. As one example [1]: “The Monte-Carlo method is defined as representing the solution of a problem as a parameter of a hypothetical population, and using a random sequence of numbers to construct a sample of the population from which statistical estimates of the parameters can be obtained. ” The method has been defined more broadly [2] as “any technique making use of random numbers to solve a problem. ” A more encompassing description [6] is “a numerical method based on random sampling. ” In this article we take the definition in its most general sense, and it is the random aspect of Monte Carlo that is our focus. Monte-Carlo simulation consists of repeating the same basic calculation a large number of times with different input data and then performing some statistical analysis on the set of results. Input data for the different “trials’ ’ are selected using values in prescribed distributions, using a pseudo-random number generator. The basic computation typically involves a significant amount of calculation, so that the pseudo-random number generation itself represents a small fraction of the total computational effort. In theory, the accuracy of the method improves with the number of trials. In practice, this improvement is dependent on the quality of the pseudo-random number generator used. The obviou