Engines (Basic Random Number Generators)

Intel® oneMKL RNG provides pseudorandom, quasi-random, and non-deterministic random number generators for Data Parallel C++:

Routine

Description

oneapi::mkl::rng::mrg32k3a

The combined multiple recursive pseudorandom number generator MRG32k3a[L’Ecuyer99a]

oneapi::mkl::rng::philox4x32x10

Philox4x32-10 counter-based pseudorandom number generator with a period of 2128PHILOX4X32X10[Salmon11]

oneapi::mkl::rng::mcg31m1

The 31-bit multiplicative congruential pseudorandom number generator MCG(1132489760, 231 -1) [L’Ecuyer99]

oneapi::mkl::rng::r250

The 32-bit generalized feedback shift register pseudorandom number generator GFSR(250,103)[Kirkpatrick81]

oneapi::mkl::rng::mcg59

The 59-bit multiplicative congruential pseudorandom number generator MCG(1313, 259) from NAG Numerical Libraries [NAG]

oneapi::mkl::rng::wichmann_hill

Wichmann-Hill pseudorandom number generator (a set of 273 basic generators) from NAG Numerical Libraries [NAG]

oneapi::mkl::rng::mt19937

Mersenne Twister pseudorandom number generator MT19937[Matsumoto98] with period length 219937-1 of the produced sequence

oneapi::mkl::rng::mt2203

Set of 6024 Mersenne Twister pseudorandom number generators MT2203[Matsumoto98], [Matsumoto00]. Each of them generates a sequence of period length equal to 22203-1. Parameters of the generators provide mutual independence of the corresponding sequences.

oneapi::mkl::rng::sfmt19937

SIMD-oriented Fast Mersenne Twister pseudorandom number generator SFMT19937[Saito08] with a period length equal to 219937-1 of the produced sequence.

oneapi::mkl::rng::sobol

Sobol quasi-random number generator [Sobol76], [Bratley88], which works in arbitrary dimension.

oneapi::mkl::rng::niederreiter

Niederreiter quasi-random number generator [Bratley92], which works in arbitrary dimension.

oneapi::mkl::rng::ars5

ARS-5 counter-based pseudorandom number generator with a period of 2128, which uses instructions from the AES-NI set ARS5[Salmon11].

oneapi::mkl::rng::nondeterministic

Non-deterministic random number generator (RDRAND-based) [AVX][IntelSWMan]

For some basic generators, Intel® oneMKL RNG provides two methods of creating independent states in multiprocessor computations, which are the leapfrog method and the block-splitting method. These sequence splitting methods are also useful in sequential Monte Carlo. The description of these functions can be found in the Service Routines section.

In addition, MT2203 pseudorandom number generator is a set of 6024 generators designed to create up to 6024 independent random sequences, which might be used in parallel Monte Carlo simulations. Another generator that has the same feature is Wichmann-Hill. It allows creating up to 273 independent random streams. The properties of the generators designed for parallel computations are discussed in detail in [Coddington94].

See VS Notes for the detailed description.