dieharder
Dieharder: A Random Number Test Suite. Dieharder is a random number generator (rng) testing suite. It is intended to test generators, not files of possibly random numbers as the latter is a fallacious view of what it means to be random. Is the number 7 random? If it is generated by a random process, it might be. If it is made up to serve the purpose of some argument (like this one) it is not. Perfect random number generators produce ”unlikely” sequences of random numbers -- at exactly the right average rate. Testing a rng is therefore quite subtle. dieharder is a tool designed to permit one to push a weak generator to unambiguous failure (at the e.g. 0.0001% level), not leave one in the ”limbo” of 1% or 5% maybe-failure. It also contains many tests and is extensible so that eventually it will contain many more tests than it already does. If you are using dieharder for testing rngs either in one of its prebuilt versions (rpm or apt) or built from source (which gives you the ability to e.g. add more tests or integrate your rng directly with dieharder for ease of use) you may want to join either or both of the dieharder-announce or the dieharder-devel mailing lists here. The former should be very low traffic -- basically announcing when a snapshot makes it through development to where I’m proud of it. The latter will be a bit more active, and is a good place to post bug reports, patches, suggestions, fixes, complaints and generally participate in the development process.
Keywords for this software
References in zbMATH (referenced in 16 articles )
Showing results 1 to 16 of 16.
Sorted by year (- Gevorkyan, M. N.; Demidova, A. V.; Korol’kova, A. V.; Kulyabov, D. S.: A practical approach to testing random number generators in computer algebra systems (2020)
- Lorek, Paweł; Łoś, Grzegorz; Gotfryd, Karol; Zagórski, Filip: On testing pseudorandom generators via statistical tests based on the arcsine law (2020)
- Fan, Chunlei; Wang, Chuanfu; Ding, Qun: A novel algorithm for detection and localization of periodic phenomena of chaotic binary sequences (2019)
- Yosefnezhad Irani, Behzad; Ayubi, Peyman; Amani Jabalkandi, Fardin; Yousefi Valandar, Milad; Jafari Barani, Milad: Digital image scrambling based on a new one-dimensional coupled sine map (2019)
- Johnston, David: Random number generators. Principles and practices. A guide for engineers and programmers (2018)
- Kneusel, Ronald T.: Random numbers and computers (2018)
- Kocak, Onur; Sulak, Fatih; Doganaksoy, Ali; Uguz, Muhiddin: Modifications of Knuth randomness tests for integer and binary sequences (2018)
- M. N. Gevorkyan, A. V. Demidova, A. V. Korolkova, D. S. Kulyabov, L. A. Sevastianov: The Stochastic Processes Generation in OpenModelica (2017) arXiv
- Sýs, Marek; Říha, Zdeněk; Matyáš, Vashek: Algorithm 970: Optimizing the NIST statistical test suite and the Berlekamp-Massey algorithm (2017)
- Sýs, Marek; Matyáš, Vashek: Randomness testing: result interpretation and speed (2016)
- Vigna, Sebastiano: An experimental exploration of Marsaglia’s \textttxorshiftgenerators, scrambled (2016)
- de Doncker, Elise; Kapenga, John; Assaf, Rida: Monte Carlo automatic integration with dynamic parallelism in CUDA (2014)
- Demchik, Vadim: Pseudorandom numbers generation for Monte Carlo simulations on GPUs: OpenCL approach (2014)
- Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
- Mascagni, Michael; Hin, Lin-Yee: Parallel pseudo-random number generators: a derivative pricing perspective with the Heston stochastic volatility model (2013)
- Ladd, Anthony J. C.: A fast random number generator for stochastic simulations (2009)