Visible to Intel only — GUID: GUID-8CC3F7A3-C188-4156-B013-0B36E2F67B5C
Visible to Intel only — GUID: GUID-8CC3F7A3-C188-4156-B013-0B36E2F67B5C
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 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 <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; }
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