The Mersenne Twister (MT) is a pseudo-random number generator (PRNG) widely used in High Performance Computing for parallel stochastic simulations. We aim to assess the quality of common parallelization techniques used to generate large streams of MT pseudo-random numbers. We compare three techniques: sequence splitting, random spacing and MT indexed sequence. The TestU01 Big Crush battery is used to evaluate the quality of 4096 streams for each technique on three different hardware configurations. Surprisingly, all techniques exhibited almost 30% of defects with no technique showing better quality than the others. While all 106 Big Crush tests showed failures, the failure rate was limited to a small number of tests (maximum of 6 tests failed per stream, resulting in over 94% success rate). Thanks to 33 CPU years, high-quality streams identified are given. They can be used for sensitive parallel simulations such as nuclear medicine and precise high-energy physics applications.

翻译：梅森旋转算法（MT）是一种广泛应用于高性能计算并行随机模拟的伪随机数生成器（PRNG）。本研究旨在评估用于生成大规模MT伪随机数流的常见并行化技术的质量。我们比较了三种技术：序列分割、随机间隔和MT索引序列。采用TestU01 Big Crush测试套件，在三种不同硬件配置下对每种技术的4096个流进行质量评估。令人惊讶的是，所有技术均表现出近30%的缺陷，且无任何技术质量明显优于其他。尽管所有106项Big Crush测试均出现失效，但失效比例仅限于少量测试（每个流最多6项测试失败，成功率超过94%）。基于33 CPU年的计算，我们识别出了高质量流，这些流可应用于核医学、精密高能物理等敏感并行模拟场景。