Examples¶
The following example demonstrates how to construct the linear spline and perform the interpolation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | #include <cstdint>
#include <iostream>
#include <vector>
#include <CL/sycl.hpp>
#include <oneapi/mkl/experimental/data_fitting.hpp>
constexpr std::int64_t nx = 10'000;
constexpr std::int64_t nsites = 150'000;
int main (int argc, char ** argv) {
sycl::queue q;
sycl::usm_allocator<double, sycl::usm::alloc::shared> alloc(q);
// Allocate memory for spline parameters
std::vector<double, decltype(alloc)> partitions(nx, alloc);
std::vector<double, decltype(alloc)> functions(nx, alloc);
std::vector<double, decltype(alloc)> coeffs(2 * (nx - 1), alloc);
std::vector<double, decltype(alloc)> sites(nsites, alloc);
std::vector<double, decltype(alloc)> results(nsites, alloc);
// Fill parameters with valid data
for (std::int64_t i = 0; i < nx; ++i) {
partitions[i] = 0.1 * i;
functions[i] = i * i;
}
for (std::int64_t i = 0; i < nsites; ++i) {
sites[i] = (0.1 * nx * i) / nsites);
}
namespace df = oneapi::mkl::experimental::data_fitting;
// Set parameters to spline
df::spline<double, df::linear_spline::default_type> spl(q);
spl.set_partitions(partitions.data(), nx)
.set_coefficients(coeffs.data())
.set_function_values(functions.data());
// Construct spline
auto event = spl.construct();
event = df::interpolate(spl, sites.data(), nsites, results.data(), { event });
event.wait();
std::cout << "done" << std::endl;
return 0;
}
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