TestU01: A C library for empirical testing of random number generators

By Pierre L'Ecuyer, Richard J. Simard. TOMS, 2006

1,090 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ba61b9f0b400b6a375eca7f7ecdb18ad871fa9e8

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Citations in the corpus

PCG : A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation (2014, methodology): “Figure 3 shows the time performance of the generators we considered in previous sections running an application that makes heavy demands on its random number generator—SmallCrush from the TestU01 suite [29].”
Cascade Chaotic System With Applications (2015, methodology): “Here, two PRNG test standards, NIST SP800-22 [26] and TestU01 [27], are used to evaluate the randomness of TLPRNG.”
Dissecting the Graphcore IPU Architecture via Microbenchmarking (2019, methodology): “hn Salmon. We run the experiment on each GPU (V100 and T4) at the maximum clock frequency supported by each device. A comparison on the quality of randomness provided by PRNG algorithms (e.g., TestU01[18] performance) is beyond the scope of this paper. Output size. We study how bulk PRNG performance”
Parallel random numbers: As easy as 1, 2, 3 (2011, neutral): “In fact, the examples in the literature of counter-based PRNGs all use cryptographic block ciphers or cryptographic hashes [17, 24, 37, 45, 48].”
SIMD-Oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator (2008, background): “(Note: the referee pointed out that statistical tests based on the rank of F2-matrix is sensitive to the linear relations [9], so the above observation is not necessarily true.”
Introduction To Monte Carlo Simulation (2010, neutral): “There are many very good pseudo-RNGs available, and many tests have been developed to check how “random” they are (e.g. the Diehard tests [6] and TestU01 [7]).”