Developer Reference

Contents

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 2
128
PHILOX4X32X10
[Salmon11]
The 31-bit multiplicative congruential pseudorandom number generator MCG(1132489760, 2
32
-1) [L’Ecuyer99a]
The 32-bit generalized feedback shift register pseudorandom number generator
GFSR(250,103)
[Kirkpatrick81]
The 59-bit multiplicative congruential pseudorandom number generator
MCG(13
13
, 2
59
) from NAG Numerical Libraries [NAG]
Wichmann-Hill pseudorandom number generator (a set of 273 basic generators) from NAG Numerical Libraries [NAG]
Mersenne Twister pseudorandom number generator
MT19937
[Matsumoto98] with period length 2
19937
-1 of the produced sequence
Set of 6024 Mersenne Twister pseudorandom number generators
MT2203
[Matsumoto98], [Matsumoto00]. Each of them generates a sequence of period length equal to 2
2203
-1. Parameters of the generators provide mutual independence of the corresponding sequences.
SIMD-oriented Fast Mersenne Twister pseudorandom number generator
SFMT19937
[Saito08] with a period length equal to 2
19937
-1 of the produced sequence.
Sobol quasi-random number generator [Sobol76], [Bratley88], which works in arbitrary dimension.
Niederreiter quasi-random number generator [Bratley92], which works in arbitrary dimension.
ARS-5 counter-based pseudorandom number generator with a period of 2
128
, which uses instructions from the AES-NI set
ARS5
[Salmon11].
Non-deterministic random number generator (RDRAND-based) [AVX], [IntelSWMan]
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.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.