Task 2.4 - Parallelizing Matrix Multiplication

You are given a skeleton sequential program for matrix multiplication below:

#include

#include

#include

#define M 500

#define N 500

int main(int argc, char *argv) {

//set number of threads here

omp_set_num_threads(8);

int i, j, k;

double sum;

double **A, **B, **C;

A = malloc(M*sizeof(double *));

B = malloc(M*sizeof(double *));

C = malloc(M*sizeof(double *));

for (i = 0; i < M; i++) {

A[i] = malloc(N*sizeof(double));

B[i] = malloc(N*sizeof(double));

C[i] = malloc(N*sizeof(double));

}

double start, end;

for (i = 0; i < M; i++) {

for (j = 0; j < N; j++) {

A[i][j] = j*1;

B[i][j] = i*j+2;

C[i][j] = j-i*2;

}

}

start = omp_get_wtime();

for (i = 0; i < M; i++) {

for (j = 0; j < N; j++) {

sum = 0;

for (k=0; k < M; k++) {

sum += A[i][k]*B[k][j];

}

C[i][j] = sum;

}

}

end = omp_get_wtime();

printf("Time of computation: %f ", end-start);

}

You are to parallelize this algorithm in three different ways:

Add the necessary pragma to parallelize the outer for loop

Remove the pragma for the outer for loop and create a pragma for the middle for loop

3. Add the necessary pragmas to parallelize both the outer and middle for loops

and collect timing data given 2 thread, 4 threads, 8 threads, 16 threads, 32 threads and three matrix sizes. You

will find that when you run the same program several times, the timing values can vary significantly. Therefore

for each set of conditions, collect five data values and average them. Use a spreadsheet program either from MS Office or OpenOffice to collect and store your data and perform the necessary calculations and illustrate the results visually with Charts.