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#include <iostream>
#include <vector>
#include <algorithm> // sort
#include <cstdlib> // srand, rand, atoi
#include <ctime> // time
const int RANDOM_MAX = 1000;
const int RANDOM_MIN = 1;
void openMpConvolution(std::vector<double>& output,
const std::vector<double>& input,
const std::vector<double>& kernel,
const int numberOfThreads,
const int chunkSize)
{
size_t i;
double sum;
int middle = kernel.size() / 2;
output.resize(input.size());
#pragma omp parallel for \
default(shared) private(i) \
schedule(dynamic, chunkSize) \
num_threads(numberOfThreads)
for (i = 0; i < input.size(); i++) {
sum = 0;
for (int j = -middle; j <= middle; j++)
if ( (int)i+j < 0 ) {
sum += input[0] * kernel[j+middle];
} else if ( i+j > input.size()-1 ) {
sum += input[input.size()-1] * kernel[j+middle];
} else {
sum += input[i+j] * kernel[j+middle];
}
output[i] = sum;
}
}
int main(int argc, char* argv[])
{
if (argc != 3) {
std::cout << "Usage: " << argv[0] << " <NUMBER_OF_THREADS> <DATA_SIZE>" << std::endl;
exit(1);
}
const int NUMBER_OF_THREADS = atoi(argv[1]);
const int DATA_SIZE = atoi(argv[2]);
const int CHUNK_SIZE = DATA_SIZE / NUMBER_OF_THREADS;
// initialize random seed
srand(time(NULL));
// fillup data vector
std::vector<double> data(DATA_SIZE);
for (int i = 0; i < DATA_SIZE; i++)
data[i] = rand() % RANDOM_MAX + RANDOM_MIN;
// the kernel for the gaussian smooth
double kernelArray[7] = { 0.06, 0.061, 0.242, 0.383, 0.242, 0.061, 0.06 };
std::vector<double> kernel (kernelArray, kernelArray + sizeof(kernelArray) / sizeof(double) );
// the convolution is not in-place, the result is stored in output
std::vector<double> output(DATA_SIZE);
clock_t start = clock();
openMpConvolution(output, data, kernel, NUMBER_OF_THREADS, CHUNK_SIZE);
clock_t end = clock();
double elapsed = ((double) (end - start)) / CLOCKS_PER_SEC;
std::cout << elapsed << std::endl;
return 0;
}