Multiplying matrix openMP is slower than sequential - c

Question

I am new to C and have created a program that creates two arrays and then multiplies them using openMP. When I compare them the sequential is quicker than the openMP way.
#include <stdio.h>
#include <stdio.h>
#include <omp.h>
#include <time.h>
#define SIZE 1000
int arrayOne[SIZE][SIZE];
int arrayTwo[SIZE][SIZE];
int arrayThree[SIZE][SIZE];
int main()
{
int i=0, j=0, k=0, sum = 0;
//creation of the first array
for(i = 0; i < SIZE; i++){
for(j = 0; j < SIZE; j++){
arrayOne[i][j] = 2;
/*printf("%d \t", arrayOne[i][j]);*/
}
}
//creation of the second array
for(i = 0; i < SIZE; i++){
for(j = 0; j < SIZE; j++){
arrayTwo[i][j] = 3;
/*printf("%d \t", arrayTwo[i][j]);*/
}
}
clock_t begin = clock();
//Matrix Multiplication (No use of openMP)
for (i = 0; i < SIZE; ++i) {
for (j = 0; j < SIZE; ++j) {
for (k = 0; k < SIZE; ++k) {
sum = sum + arrayOne[i][k] * arrayTwo[k][j];
}
arrayThree[i][j] = sum;
sum = 0;
}
}
clock_t end = clock();
double time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
printf("Time taken without openMp: %f \n", time_spent);
//Matrix Multiplication Using openMP
printf("---------------------\n");
clock_t new_begin = clock();
#pragma omp parallel private(i, j, sum, k) shared (arrayOne, arrayTwo, arrayThree)
{
#pragma omp for schedule(static)
for (i = 0; i < SIZE; i++) {
for(j = 0; j < SIZE; j++) {
for(k = 0; k < SIZE; k++) {
sum = sum + arrayOne[i][k] * arrayTwo[k][j];
}
arrayThree[i][j] = sum;
sum = 0;
}
}
}
clock_t new_end = clock();
double new_time_spent = (double)(new_end - new_begin) / CLOCKS_PER_SEC;
printf("Time taken WITH openMp: %f ", new_time_spent);
return 0;
}
The sequential way takes 0.265000 while the openMP takes 0.563000.
I have no idea why, any solutions?
Updated code to global arrays and make them larger but still takes double the run time.

Answer 1

OpenMP needs to create and destroy threads, which results in extra overhead. Such overhead is quite small, but for a very small workload, like yours, it can still be significant.
to use OpenMP more efficiently, you should give it a large workload (larger matrices) and make thread-related overhead not the dominant factor in your runtime.