Device Distributions

oneMKL - Data Parallel C++ Developer Reference

Download PDF

ID 772045

Date 6/24/2024

Version

Public

A newer version of this document is available. Customers should click here to go to the newest version.

Visible to Intel only — GUID: GUID-8DEAC3B8-17C1-428D-BC42-C6B34F02E95A

View Details

Device Distributions

oneMKL RNG 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. The Device Continuous Distribution Generators table and Device Discrete Distribution Generators table 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.

Device Continuous Distribution Generators

Type of Distribution	Data Types	BRNG Data Type	Description
uniform (Continuous)	float, double	float, double	Uniform continuous distribution on the interval [`a,b`)
gaussian	float, double	float, double	Normal (Gaussian) distribution
exponential	float, double	float, double	Exponential distribution
lognormal	float, double	float, double	Lognormal distribution
beta	float, double	float, double	Beta distribution
gamma	float, double	float, double	Gamma distribution

Device Discrete Distribution Generators

Type of Distribution	Data Types	BRNG Data Type	Description
uniform (Discrete)	integer	float for `uniform_method::standard`, double for `uniform_method::accurate`, and raw output for 64 bit integers	Uniform discrete distribution on the interval [`a,b`)
bits	integer	integer	Bits of underlying BRNG integer sequence
uniform_bits	integer	integer	Uniformly distributed bits in 32/64-bit chunks
bernoulli	integer	float	Bernoulli distribution
poisson	integer	double	Poisson distribution

NOTE:

In case of integer, check the desired distribution for supported data types.
To get continuously distributed numbers of sycl::half, convert the generated numbers to sycl::half as shown below:

queue.submit([&](sycl::handler& cgh) {
  cgh.parallel_for(sycl::range<1>(n / VecSize), [=](sycl::item<1> item) {
      size_t item_id = item.get_id(0);
      oneapi::mkl::rng::device::mcg59<VecSize> engine(seed, item_id * VecSize);
      oneapi::mkl::rng::device::uniform<float> distr;

      auto res = oneapi::mkl::rng::device::generate(distr, engine);
      if constexpr(VecSize == 1) {
          r_acc[item_id] = static_cast<sycl::half>(res);
      }
      else {
          sycl::vec<sycl::half, VecSize> res_;
          res_ = res.template convert<sycl::half>();
          res_.store(item_id, r_acc);
      }
  });
});

Select Your Language

Using Intel.com Search

Quick Links

Recent Searches

Advanced Search

Only search in

oneMKL - Data Parallel C++ Developer Reference

Device Distributions