ASSESSING THE STOCHASTIC PROPERTIES OF MODERN PSEUDO-RANDOM GENERATORS FOR PARALLEL COMPUTING

Pseudo-random number generators (PRNGs) are widely used in modern computing and are expected to exhibit excellent statistical performance and repeatability. This study evaluates and compares modern PRNGs used in high performance computing and artificial intelligence. Our selections comes from different families, including Xoshiro, Philox, PCG, and MRG32k3a. We systematically assess the quality of these generators; instead of testing a single stream for each generator, we test more than 10 3 streams with the BigCrush battery form the TestU01 library. The results, involving more than 4.5 years of cumulative computing time, are analyzed against the claims made by the generators' creators. The highest success rate is 72%, and all tests have been failed by almost every generator, the failed tests are documented. To ensure fairness, all tests are conducted under consistent conditions and are designed to closely simulate real-world usage. The results of each test are available, usable and reproducible with a git repository.