14. AOCL-RNG - 5.2 English - 57404

AOCL User Guide (57404)

Document ID
57404
Release Date
2025-12-29
Version
5.2 English

AOCL-RNG is a high-performance implementation of vector based Random Number Generator library which provides a set of pseudo-random number generators, quasi-random number generator and statistical distribution functions optimized for AMD “Zen”-based processors.

Statistical distribution functions include continuous as well as discrete type. This library provides random numbers of type Integer, Single Precision and Double Precision. This library provides seven base generators and twenty-three distribution functions.

Furthermore, customization is possible to use a custom-built generator as the base generator for all distribution generators.

This library depends on other libraries AOCL-LibM and Single-threaded AOCL-BLAS.

AOCL-RNG provides following base generators and distribution functions:

  • Pseudo-Random Number Generator
    • NAG Basic Generator

    • Wichmann-Hill Generators

    • L’Ecuyer’s Combined Recursive (MRG32K3A) Generator

    • Mersenne Twister Generator

    • SIMD-oriented Fast Mersenne Twister (SFMT) Generator

    • Blum-Blum-Shub Generator (deprecated)

  • Quasi-Random Number Generator
    • Sobol Generator

  • Statistical continuous distribution functions
    • Beta Distribution

    • Cauchy Distribution

    • Chi-square Distribution

    • Exponential Distribution

    • Gamma Distribution

    • Gaussian or Normal Distribution

    • Lognormal Distribution

    • Uniform Distribution

    • Weibull Distribution

    • F-Distribution

    • Logistic Distribution

    • Students T Distribution

    • Triangular Distribution

    • VonMises Distribution

    • Multivariate Gaussian or Normal Distribution

    • Multivariate Students T Distribution

  • Statistical discrete distribution functions
    • Binomial Distribution

    • Geometric Distribution

    • Hypergeometric Distribution

    • Negative Binomial Distribution

    • Poisson Distribution

    • Uniform Distribution

    • Multinomial Distribution

Refer RNG documentation for more information.