Question

I have this C++ code.

Loop goes throgh the matrix, finds the min element in each row and subtracts it from each element of corresponding row.
Variable myr is a summ of all min elements

Trying to parallel for:

int min = 0;
int myr = 0;  
int temp[SIZE][SIZE];
int size = 0;
...//some initialization

omp_set_num_threads(1);
start_time = omp_get_wtime();
    #ifdef _OPENMP
    #pragma omp parallel for firstprivate(min, size) reduction(+:myr) 
    #endif
    for(int i = 0; i < size; i++){
        min = INFINITY;
        for(int j = 0; j < size; j++){
                if (temp[i][j] < min)                
                    min = temp[i][j];                        
        }
        myr+=min;
        for(int j = 0; j < size; j++) 
                temp[i][j]-=min;
    }
end_time = omp_get_wtime();

if I set omp_set_num_threads(2); this part of code starts working slower.

My proc has 2 cores

Why code works slower with 2 threads?

Answer 1

There must be some aliasing or something going on. Make things simpler for OpenMP:

int const size0 = size;
#ifdef _OPENMP
#pragma omp parallel for reduction(+:myr) 
#endif
for(int i = 0; i < size0; i++){
    int min = INFINITY;
    int * tmp = temp[i];
    for(int j = 0; j < size0; j++){
            if (tmp[j] < min)                
                min = tmp[j];                        
    }
    for(int j = 0; j < size0; j++) 
            tmp[j]-=min;
    myr+=min;
}

That is, have most of the variables local and const if you may.