Developer Reference

Contents

oneMKL RNG Usage Model

A typical algorithm for random number generators is as follows:
  1. Create and initialize the object for basic random number generator.
    • Use the
      skip_ahead
      or
      leapfrog
      function if it is required (used in parallel with random number generation for Host and CPU devices).
  2. Create and initialize the object for distribution generator.
  3. Call the generate routine to get random numbers with appropriate statistical distribution.
The following example demonstrates generation of random numbers that is output from basic generator (engine) PHILOX4X32X10. The seed is equal to 777. The generator is used to generate 10,000 normally distributed random numbers with parameters
a
= 5 and
sigma
= 2. The purpose of the example is to calculate the sample mean for normal distribution with the given parameters.

Example of RNG Usage

Buffer API
#include <iostream> #include <vector> #include “CL/sycl.hpp” #include “oneapi/mkl/rng.hpp” #define SEED 777 int main() { sycl::queue queue; const size_t n = 10000; std::vector<double> r(n); // create basic random number generator object oneapi::mkl::rng::philox4x32x10 engine(queue, SEED); // create distribution object oneapi::mkl::rng::gaussian<double, oneapi::mkl::rng::gaussian_method::icdf> distr(5.0, 2.0); { // buffer for random numbers sycl::buffer<double, 1> r_buf(r.data(), r.size()); // perform generation oneapi::mkl::rng::generate(distr, engine, n, r_buf); } double s = 0.0; for(int i = 0; i < n; i++) { s += r[i]; } s /= n; std::cout << “Average = ” << s << std::endl; return 0; }
USM API
#include <iostream> #include <vector> #include “CL/sycl.hpp” #include “oneapi/mkl/rng.hpp” #define SEED 777 int main() { sycl::queue queue; const size_t n = 10000; // create USM allocator sycl::usm_allocator<double, sycl::usm::alloc::shared> allocator(queue); // create vector with USM allocator std::vector<double, decltype(allocator)> r(n, allocator); // create basic random number generator object oneapi::mkl::rng::philox4x32x10 engine(queue, SEED); // create distribution object oneapi::mkl::rng::gaussian<double, oneapi::mkl::rng::gaussian_method::icdf> distr(5.0, 2.0); // perform generation auto event = oneapi::mkl::rng::generate(distr, engine, n, r.data()); // sycl::event object is returned by generate function for synchronization event.wait(); // synchronization can be also done by queue.wait() double s = 0.0; for(int i = 0; i < n; i++) { s += r[i]; } s /= n; std::cout << “Average = ” << s << std::endl; return 0; }
You can also use USM with raw pointers by using the
sycl::malloc_shared
/
sycl::malloc_device
function.
Additionally, examples that demonstrate usage of random number generators functionality are available in:
${MKL}/examples/dpcpp/rng/source

Product and Performance Information

1

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