Name – Harsh Parmar
Date – 09-02-2024
Reg No. – 21BCE11052
Winter semester 2023-24
CSE3009 - Parallel and Distributed Computing
OpenMP LAB PROGRAMS (LAB-1)
1. OpenMP – Basic programs such as Vector addition, Dot Product
A) Vector Addition
#include <iostream>
#include <vector>
#include <omp.h>
void vectorAddition(const std::vector<int>& a, const std::vector<int>& b, std::vector<int>& result) {
for (int i = 0; i < a.size(); ++i) {
result[i] = a[i] + b[i];
}
}
int main() {
const int size = 20; // Reduced size of the vectors
std::vector<int> a(size);
std::vector<int> b(size);
std::vector<int> result(size);
for (int i = 0; i < size; ++i) {
a[i] = i;
b[i] = size - i;
}
vectorAddition(a, b, result);
std::cout << "Result of vector addition:" << std::endl;
for (int i = 0; i < 15; ++i) {
std::cout << result[i] << " ";
}
std::cout << std::endl;
return 0;
}
Name – Harsh Parmar
Reg No. – 21BCE11052
B)Dot Product
#include <iostream>
#include <vector>
#include <omp.h>
int dotProduct(const std::vector<int>& a, const std::vector<int>& b) {
int result = 0;
for (int i = 0; i < a.size(); ++i) {
result += a[i] * b[i];
}
return result;
}
int main() {
const int size = 500;
std::vector<int> a(size);
std::vector<int> b(size);
for (int i = 0; i < size; ++i) {
a[i] = i;
b[i] = size - i;
}
int result = dotProduct(a, b);
std::cout << "Dot product: " << result << std::endl;
return 0;
}
Date – 09-02-2024
Name – Harsh Parmar
Reg No. – 21BCE11052
2. OpenMP – Loop work-sharing and sections work-sharing
#include <iostream>
#include <omp.h>
int main() {
const int N = 100;
int array[N];
#pragma omp parallel for
for (int i = 0; i < N; ++i) {
array[i] = omp_get_thread_num(); // Assigning thread ID to each element
}
std::cout << "Loop Work-sharing:" << std::endl;
for (int i = 0; i < N; ++i) {
std::cout << "Element " << i << ": Thread " << array[i] << std::endl;
}
std::cout << std::endl;
#pragma omp parallel sections
{
#pragma omp section
{
std::cout << "Section 1 executed by Thread " << omp_get_thread_num() << std::endl;
}
#pragma omp section
{
std::cout << "Section 2 executed by Thread " << omp_get_thread_num() << std::endl;
}
}
return 0;
}
Date – 09-02-2024
Name – Harsh Parmar
Reg No. – 21BCE11052
Date – 09-02-2024
Name – Harsh Parmar
Reg No. – 21BCE11052
Date – 09-02-2024
Name – Harsh Parmar
Date – 09-02-2024
Reg No. – 21BCE11052
3. OpenMP – Combined parallel loop reduction and Orphaned parallel loop reduction
#include <iostream>
#include <omp.h>
int main() {
const int N = 500;
int sum = 0;
#pragma omp parallel for reduction(+:sum)
for (int i = 0; i < N; ++i) {
sum += i;
}
std::cout << "Combined parallel loop reduction: " << sum << std::endl;
int sum2 = 0;
#pragma omp parallel for reduction(+:sum2)
for (int i = 0; i < N; ++i) {
sum2 += i;
}
std::cout << "Orphaned parallel loop reduction: " << sum2 << std::endl;
return 0;
}
Name – Harsh Parmar
Date – 09-02-2024
Reg No. – 21BCE11052
4. OpenMP – Matrix multiply (specify run of a GPU card, large scale data … Complexity of the
problem need to be specified)
#include <iostream>
#include <vector>
#include <omp.h>
void matrixMultiply(const std::vector<std::vector<int>>& A, const std::vector<std::vector<int>>& B,
std::vector<std::vector<int>>& result) {
#pragma omp target teams distribute parallel for collapse(2)
for (int i = 0; i < A.size(); ++i) {
for (int j = 0; j < B[0].size(); ++j) {
int temp = 0;
for (int k = 0; k < A[0].size(); ++k) {
temp += A[i][k] * B[k][j];
}
result[i][j] = temp;
}
}
}
int main() {
const int size = 1000; // Size of the matrices (large-scale data)
std::vector<std::vector<int>> A(size, std::vector<int>(size));
std::vector<std::vector<int>> B(size, std::vector<int>(size));
std::vector<std::vector<int>> result(size, std::vector<int>(size));
for (int i = 0; i < size; ++i) {
for (int j = 0; j < size; ++j) {
A[i][j] = i + j;
B[i][j] = i - j;
}
}
matrixMultiply(A, B, result);
std::cout << "Result of matrix multiplication:" << std::endl;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::cout << result[i][j] << " ";
}
std::cout << std::endl;
}
return 0;
}
Name – Harsh Parmar
Reg No. – 21BCE11052
Date – 09-02-2024