Visible to Intel only — GUID: GUID-79A98327-AB9C-4E1E-88E1-F9E530B77455
Visible to Intel only — GUID: GUID-79A98327-AB9C-4E1E-88E1-F9E530B77455
oneMKL RNG Usage Model
A typical algorithm for random number generators is as follows:
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 CPU devices).
Create and initialize the object for distribution generator.
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 and . 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 <sycl/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 <sycl/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;
}
Additionally, examples that demonstrate usage of random number generators functionality are available in:
${MKL}/share/doc/mkl/examples/sycl/rng/source