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Visible to Intel only — GUID: GUID-93E6E9A1-A25A-41E5-B6F6-81B1267B764D
Visible to Intel only — GUID: GUID-93E6E9A1-A25A-41E5-B6F6-81B1267B764D
Engines (Basic Random Number Generators)
oneMKL RNG provides pseudorandom, quasi-random, and non-deterministic random number generators for Data Parallel C++:
Routine |
Description |
|---|---|
The combined multiple recursive pseudorandom number generator MRG32k3a [L’Ecuyer99a] |
|
Philox4x32-10 counter-based pseudorandom number generator with a period of |
|
The 31-bit multiplicative congruential pseudorandom number generator MCG( |
|
The 32-bit generalized feedback shift register pseudorandom number generator GFSR(250,103) [Kirkpatrick81] |
|
The 59-bit multiplicative congruential pseudorandom number generator MCG ( |
|
Wichmann-Hill pseudorandom number generator (a set of 273 basic generators) from NAG Numerical Libraries [NAG] |
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Mersenne Twister pseudorandom number generator MT19937 [Matsumoto98] with period length |
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Set of 6024 Mersenne Twister pseudorandom number generators MT2203 [Matsumoto98], [Matsumoto00]. Each of them generates a sequence of period length equal to |
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SIMD-oriented Fast Mersenne Twister pseudorandom number generator SFMT19937 [Saito08] with a period length equal to |
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Sobol quasi-random number generator [Sobol76], [Bratley88], which works in arbitrary dimension. |
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Niederreiter quasi-random number generator [Bratley92], which works in arbitrary dimension. |
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ARS-5 counter-based pseudorandom number generator with a period of |
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Non-deterministic random number generator (RDRAND-based) [AVX], [IntelSWMan]. |
PHILOX4X32X10 
) 
) from NAG Numerical Libraries 
of the produced sequence
. Parameters of the generators provide mutual independence of the corresponding sequences.For some basic generators, 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.