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

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.