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A modification of Marsaglia's Xorshift generators, one of the fastest generators on modern 64-bit CPUs. Related generators include xoroshiro128**, xoshiro256+ and xoshiro256**. 64-bit MELG (MELG-64) 2018 S. Harase, T. Kimoto [38] An implementation of 64-bit maximally equidistributed F 2-linear generators with Mersenne prime period. Squares RNG ...
A USB-pluggable hardware true random number generator. In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), [1] or physical random number generator [2] [3] is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a ...
Fortuna is a cryptographically secure pseudorandom number generator (CS-PRNG) devised by Bruce Schneier and Niels Ferguson and published in 2003. It is named after Fortuna, the Roman goddess of chance. FreeBSD uses Fortuna for /dev/random and /dev/urandom is symbolically linked to it since FreeBSD 11. [1] Apple OSes have switched to Fortuna ...
In addition to Threefry and ARS, Salmon et al. described a third counter-based PRNG, Philox, [1] based on wide multiplies; e.g. multiplying two 32-bit numbers and producing a 64-bit number, or multiplying two 64-bit numbers and producing a 128-bit number. As of 2020, Philox is popular on CPUs and GPUs.
The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto (松本 眞) and Takuji Nishimura (西村 拓士). [1] [2] Its name derives from the choice of a Mersenne prime as its period length. The Mersenne Twister was designed specifically to rectify most of the flaws found in older PRNGs.
Dice are an example of a mechanical hardware random number generator. When a cubical die is rolled, a random number from 1 to 6 is obtained. Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols is generated that cannot be reasonably predicted better than by random chance.
The first has one 32-bit word of state, and period 2 32 −1. The second has one 64-bit word of state and period 2 64 −1. The last one has four 32-bit words of state, and period 2 128 −1. The 128-bit algorithm passes the diehard tests. However, it fails the MatrixRank and LinearComp tests of the BigCrush test suite from the TestU01 framework.
That is, given the first k bits of a random sequence, there is no polynomial-time algorithm that can predict the (k+1)th bit with probability of success non-negligibly better than 50%. [1] Andrew Yao proved in 1982 that a generator passing the next-bit test will pass all other polynomial-time statistical tests for randomness. [2]