Techniques for Empirical Testing of Parallel Random Number Generators

Parallel computers are now commonly used for computational science and engineering, and many applications in these areas use random number generators. For some applications, such as large-scale Monte Carlo simulations, it is crucial that the random number generator have good randomness properties. Many programs are available for testing the quality of sequential random number generators, but very little work has been done on testing parallel random number generators. We present some techniques for empirical testing of random number generators on parallel computers, using tests based on computational science applications as examples. In particular, we focus on tests based on parallel algorithms developed for Monte Carlo simulations of the two dimensional Ising model, for which exact results are known. Preliminary results of these tests are presented for several parallel random number generators.