Developer Reference

Contents

oneMKL Summary Statistics Usage Model

Description

A typical algorithm for random number generators is as follows:
  1. Create and initialize the object for the dataset.
  2. Call the summary statistics routine to calculate the appropriate estimate.
The following example demonstrates how to calculate mean values for a 3-dimentional dataset filled with random numbers. For dataset creation, the
make_dataset
helper function is used.

Example of Summary Statistics Usage

Buffer API
#include <iostream> #include <vector> #include “CL/sycl.hpp” #include “oneapi/mkl/stats.hpp” int main() { sycl::queue queue; const size_t n_observations = 1000; const size_t n_dims = 3; std::vector<float> x(n_observations * n_dims); // fill x storage with random numbers for(int i = 0; i < n_dims, i++) { for(int j = 0; j < n_observations; j++) { x[j + i * n_observations] = float(std::rand()) / float(RAND_MAX); } } //create buffer for dataset sycl::buffer<float, 1> x_buf(x.data(), x.size()); // create buffer for mean values sycl::buffer<float, 1> mean_buf(n_dims); // create mkl::stats::dataset auto dataset = oneapi::mkl::stats::make_dataset<mkl::stats::layout::row_major>(n_dims, n_observations, x_buf); oneapi::mkl::stats::mean(queue, dataset, mean_buf); // create host accessor for mean_buf to print results auto acc = mean_buf.template get_access<sycl::access::mode::read>(); for(int i = 0; i < n_dims; i++) { std::cout << “Mean value for dimension ” << i << “: ”<< acc[i]<< std::endl; } return 0; }
USM API
#include <iostream> #include <vector> #include “CL/sycl.hpp” #include “oneapi/mkl/stats.hpp” int main() { sycl::queue queue; const size_t n_observations = 1000; const size_t n_dims = 3; sycl::usm_allocator<float, sycl::usm::alloc::shared> allocator(queue); std::vector<float, decltype(allocator)> x(n_observations * n_dims, allocator); // fill x storage with random numbers for(int i = 0; i < n_dims, i++) { for(int j = 0; j < n_observations; j++) { x[j + i * n_observations] = float(std::rand()) / float(RAND_MAX); } } std::vector<float, decltype(allocator)> mean_buf(n_dims, allocator); // create mkl::stats::dataset auto dataset = oneapi::mkl::stats::make_dataset<mkl::stats::layout::row_major>(n_dims, n_observations, x); sycl::event event = oneapi::mkl::stats::mean(queue, dataset, mean); event.wait(); for(int i = 0; i < n_dims; i++) { std::cout << “Mean value for dimension ” << i << “: ”<< mean[i]<< std::endl; } return 0; }
You can also use USM with raw pointers by using the
sycl::malloc_shared/malloc_device
functions. Additionally, examples that demonstrate usage of summary statistics functionality are available in:
${MKL}/examples/dpcpp/stats/source

Product and Performance Information

1

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