Developer Reference

Contents

Distribution Generators

oneMKL
VS
routines are used to generate random numbers with different types of distribution. Each function group is introduced below by the type of underlying distribution and contains a short description of its functionality, as well as specifications of the call sequence and the explanation of input and output parameters. Table
"Continuous Distribution Generators"
and Table
"Discrete Distribution Generators"
list the random number generator routines with data types and output distributions, and sets correspondence between data types of the generator routines and the basic random number generators.
Continuous Distribution Generators
Type of Distribution
Data Types
BRNG Data Type
Description
s
,
d
s
,
d
Uniform continuous distribution on the interval [
a,b
)
s
,
d
s
,
d
Normal (Gaussian) distribution
s
,
d
s
,
d
Multivariate normal (Gaussian) distribution
s
,
d
s
,
d
Exponential distribution
s
,
d
s
,
d
Laplace distribution (double exponential distribution)
s
,
d
s
,
d
Weibull distribution
s
,
d
s
,
d
Cauchy distribution
s
,
d
s
,
d
Rayleigh distribution
s
,
d
s
,
d
Lognormal distribution
s
,
d
s
,
d
Gumbel (extreme value) distribution
s
,
d
s
,
d
Gamma distribution
s
,
d
s
,
d
Beta distribution
s
,
d
s
,
d
Chi-Square distribution
 
Discrete Distribution Generators
Type of Distribution
Data Types
BRNG Data Type
Description
i
d
Uniform discrete distribution on the interval [
a,b
)
i
i
Underlying BRNG integer recurrence
i
i
Uniformly distributed bits in 32-bit chunks
i
i
Uniformly distributed bits in 64-bit chunks
i
s
Bernoulli distribution
i
s
Geometric distribution
i
d
Binomial distribution
i
d
Hypergeometric distribution
i
s
(for
VSL_RNG_METHOD_POISSON_POISNORM
)
s
(for distribution parameter
λ
27) and
d
(for
λ
< 27) (for
VSL_RNG_METHOD_POISSON_PTPE
)
Poisson distribution
i
s
Poisson distribution with varying mean
i
d
Negative binomial distribution, or Pascal distribution
i
d
Multinomial distribution

Modes of random number generation

The library provides two modes of random number generation, accurate and fast. Accurate generation mode is intended for the applications that are highly demanding to accuracy of calculations. When used in this mode, the generators produce random numbers lying completely within definitional domain for all values of the distribution parameters. For example, random numbers obtained from the generator of continuous distribution that is uniform on interval [
a
,
b
] belong to this interval irrespective of what
a
and
b
values may be. Fast mode provides high performance of generation and also guarantees that generated random numbers belong to the definitional domain except for some specific values of distribution parameters. The generation mode is set by specifying relevant value of the method parameter in generator routines. List of distributions that support accurate mode of generation is given in the table below.
Distribution Generators Supporting Accurate Mode
Type of Distribution
Data Types
s
,
d
s
,
d
s
,
d
s
,
d
s
,
d
s
,
d
s
,
d
See additional details about accurate and fast mode of random number generation in VS Notes .

New method names

The current version of
oneMKL
has a modified structure of VS RNG method names. (See RNG Naming Conventions for details.) The old names are kept for backward compatibility. The
tables below
set correspondence between the new and legacy method names for VS random number generators.
Method Names for Continuous Distribution Generators
RNG
Legacy Method Name
New Method Name
VSL_METHOD_SUNIFORM_STD
,
VSL_METHOD_DUNIFORM_STD
,
VSL_METHOD_SUNIFORM_STD_ACCURATE
,
VSL_METHOD_DUNIFORM_STD_ACCURATE
VSL_RNG_METHOD_UNIFORM_STD
,
VSL_RNG_METHOD_UNIFORM_STD_ACCURATE
VSL_METHOD_SGAUSSIAN_BOXMULLER
,
VSL_METHOD_SGAUSSIAN_BOXMULLER2
,
VSL_METHOD_SGAUSSIAN_ICDF
,
VSL_METHOD_DGAUSSIAN_BOXMULLER
,
VSL_METHOD_DGAUSSIAN_BOXMULLER2
,
VSL_METHOD_DGAUSSIAN_ICDF
VSL_RNG_METHOD_GAUSSIAN_BOXMULLER
,
VSL_RNG_METHOD_GAUSSIAN_BOXMULLER2
,
VSL_RNG_METHOD_GAUSSIAN_ICDF
VSL_METHOD_SGAUSSIANMV_BOXMULLER
,
VSL_METHOD_SGAUSSIANMV_BOXMULLER2
,
VSL_METHOD_SGAUSSIANMV_ICDF
,
VSL_METHOD_DGAUSSIANMV_BOXMULLER
,
VSL_METHOD_DGAUSSIANMV_BOXMULLER2
,
VSL_METHOD_DGAUSSIANMV_ICDF
VSL_RNG_METHOD_GAUSSIANMV_BOXMULLER
,
VSL_RNG_METHOD_GAUSSIANMV_BOXMULLER2
,
VSL_RNG_METHOD_GAUSSIANMV_ICDF
VSL_METHOD_SEXPONENTIAL_ICDF
,
VSL_METHOD_DEXPONENTIAL_ICDF
,
VSL_METHOD_SEXPONENTIAL_ICDF_ACCURATE
,
VSL_METHOD_DEXPONENTIAL_ICDF_ACCURATE
VSL_RNG_METHOD_EXPONENTIAL_ICDF
,
VSL_RNG_METHOD_EXPONENTIAL_ICDF_ACCURATE
VSL_METHOD_SLAPLACE_ICDF
,
VSL_METHOD_DLAPLACEL_ICDF
VSL_RNG_METHOD_LAPLACE_ICDF
VSL_METHOD_SWEIBULL_ICDF
,
VSL_METHOD_DWEIBULL_ICDF
,
VSL_METHOD_SWEIBULL_ICDF_ACCURATE
,
VSL_METHOD_DWEIBULL_ICDF_ACCURATE
VSL_RNG_METHOD_WEIBULL_ICDF
,
VSL_RNG_METHOD_WEIBULL_ICDF_ACCURATE
VSL_METHOD_SCAUCHY_ICDF
,
VSL_METHOD_DCAUCHY_ICDF
VSL_RNG_METHOD_CAUCHY_ICDF
VSL_METHOD_SRAYLEIGH_ICDF
,
VSL_METHOD_DRAYLEIGH_ICDF
,
VSL_METHOD_SRAYLEIGH_ICDF_ACCURATE
,
VSL_METHOD_DRAYLEIGH_ICDF_ACCURATE
VSL_RNG_METHOD_RAYLEIGH_ICDF
,
VSL_RNG_METHOD_RAYLEIGH_ICDF_ACCURATE
VSL_METHOD_SLOGNORMAL_BOXMULLER2
,
VSL_METHOD_DLOGNORMAL_BOXMULLER2
,
VSL_METHOD_SLOGNORMAL_BOXMULLER2_ACCURATE
,
VSL_METHOD_DLOGNORMAL_BOXMULLER2_ACCURATE
VSL_RNG_METHOD_LOGNORMAL_BOXMULLER2
,
VSL_RNG_METHOD_LOGNORMAL_BOXMULLER2_ACCURATE
VSL_METHOD_SLOGNORMAL_ICDF
,
VSL_METHOD_DLOGNORMAL_ICDF
,
VSL_METHOD_SLOGNORMAL_ICDF_ACCURATE
,
VSL_METHOD_DLOGNORMAL_ICDF_ACCURATE
VSL_RNG_METHOD_LOGNORMAL_ICDF
,
VSL_RNG_METHOD_LOGNORMAL_ICDF_ACCURATE
VSL_METHOD_SGUMBEL_ICDF
,
VSL_METHOD_DGUMBEL_ICDF
VSL_RNG_METHOD_GUMBEL_ICDF
VSL_METHOD_SGAMMA_GNORM
,
VSL_METHOD_DGAMMA_GNORM
,
VSL_METHOD_SGAMMA_GNORM_ACCURATE
,
VSL_METHOD_DGAMMA_GNORM_ACCURATE
VSL_RNG_METHOD_GAMMA_GNORM
,
VSL_RNG_METHOD_GAMMA_GNORM_ACCURATE
VSL_METHOD_SBETA_CJA
,
VSL_METHOD_DBETA_CJA
,
VSL_METHOD_SBETA_CJA_ACCURATE
,
VSL_METHOD_DBETA_CJA_ACCURATE
VSL_RNG_METHOD_BETA_CJA
,
VSL_RNG_METHOD_BETA_CJA_ACCURATE
 
Method Names for Discrete Distribution Generators
RNG
Legacy Method Name
New Method Name
VSL_METHOD_IUNIFORM_STD
VSL_RNG_METHOD_UNIFORM_STD
VSL_METHOD_IUNIFORMBITS_STD
VSL_RNG_METHOD_UNIFORMBITS_STD
VSL_METHOD_IBERNOULLI_ICDF
VSL_RNG_METHOD_BERNOULLI_ICDF
VSL_METHOD_IGEOMETRIC_ICDF
VSL_RNG_METHOD_GEOMETRIC_ICDF
VSL_METHOD_IBINOMIAL_BTPE
VSL_RNG_METHOD_BINOMIAL_BTPE
VSL_METHOD_IHYPERGEOMETRIC_H2PE
VSL_RNG_METHOD_HYPERGEOMETRIC_H2PE
VSL_METHOD_IPOISSON_PTPE
,
VSL_METHOD_IPOISSON_POISNORM
VSL_RNG_METHOD_POISSON_PTPE
,
VSL_RNG_METHOD_POISSON_POISNORM
VSL_METHOD_IPOISSONV_POISNORM
VSL_RNG_METHOD_POISSONV_POISNORM
VSL_METHOD_INEGBINOMIAL_NBAR
VSL_RNG_METHOD_NEGBINOMIAL_NBAR

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804