VSL.jl

This package provides bindings to the Intel Vector Statistics Library.

Installation

]add VSL

Julia v1.7+ is required to install VSL.jl. and MKL_jll.jl will be downloaded automatically.

Usage

VSL.jl provides several basic random number generators (BRNGs) and distributions, and each distribution has at least one method to generate random number. After VSL.jl loaded, you can use the distributions such like the followings:

julia> using VSL, Random

julia> brng = BasicRandomNumberGenerator(VSL_BRNG_MT19937, 12345);
# A BRNG created, in which 12345 is the random seed.

julia> u = Uniform(brng, 0.0, 1.0); # Create a uniform distribution between 0.0 and 1.0.

julia> rand(u) # Generate one random number.
0.41661986871622503

julia> rand(u, 2, 3) # Generate an random 2*3 array.
2×3 Array{Float64,2}:
 0.732685   0.820175  0.802848
 0.0101692  0.825207  0.29864

julia> A = Array{Float64}(undef, 3, 4);

julia> rand!(u, A) # Fill an array with random numbers.
3×4 Array{Float64,2}:
 0.855138  0.193661  0.436228  0.124267
 0.368412  0.270245  0.161688  0.874174
 0.931785  0.566008  0.373064  0.432936

Basic random number generators

Use the Enum BRNGType to set the type of BRNG.

BRNGType Enum
VSL_BRNG_MCG31
VSL_BRNG_R250
VSL_BRNG_MRG32K3A
VSL_BRNG_MCG59
VSL_BRNG_WH
VSL_BRNG_SOBOL
VSL_BRNG_NIEDERR
VSL_BRNG_MT19937
VSL_BRNG_MT2203
VSL_BRNG_SFMT19937
VSL_BRNG_NONDETERM
VSL_BRNG_ARS5
VSL_BRNG_PHILOX4X32X10

Supported distributions

Contigurous: Uniform, Gaussian, GaussianMV, Exponential, Laplace, Weibull, Cauchy, Rayleigh, Lognormal, Gumbel, Gamma, Beta

Discrete: UniformDiscrete, UniformBits, UniformBits32, UniformBits64, Bernoulli, Geometric, Binomial, Hypergeometric, Poisson, PoissonV, NegBinomial

Notes

Most of the discrete distributions return values of 32-bit integer. Please be careful when using those distributions.

For more information, please refer to the Intel® Math Kernel Library Developer Reference

License

MIT license