OpenMP C program run slower than sequential code

Question

I am a newbie to OpenMP, trying to parallelize Jarvis's algorithm. However it turns out that the parallel program take 2-3 times longer compare to sequential code.

Is it that the problem itself cannot be parallelize? Or there is something wrong in how i parallelize it.

This is my openMP program for the problem, with 2 parts being parallelize:

#include <stdio.h>
#include <sys/time.h>
#include <omp.h>

typedef struct Point
{
int x, y;
} Point;

// To find orientation of ordered triplet (p, q, r).
// The function returns
// 0 for colinear points
// 1 as Clockwise
// 2 as Counterclockwise
int orientation(Point p, Point i, Point q)
{
int val = (i.y - p.y) * (q.x - i.x) -
          (i.x - p.x) * (q.y - i.y);
if (val == 0) return 0;  // colinear
return (val > 0)? 1: 2; // clock or counterclock wise
}

// Prints convex hull of a set of n points.
void convexHull(Point points[], int n)
{
// There must be at least 3 points
if (n < 3) return;

// Initialize array to store results
Point results[n];
int count = 0;

// Find the leftmost point
int l = 0,i;

#pragma omg parallel shared (n,l) private (i)
{
    #pragma omp for
    for (i = 1; i < n; i++)
    {
        #pragma omp critical
        {
            if (points[i].x < points[l].x)
            l = i;
        }
    }

}

// Start from leftmost point, keep moving counterclockwise
// until reach the start point again.
int p = l, q;
do
{
    // Add current point to result
    results[count]= points[p];
    count++;

    q = (p+1)%n;
    int k;

    #pragma omp parallel shared (p) private (k)
    {
        #pragma omp for 
        for (k = 0; k < n; k++)
        {
           // If i is more counterclockwise than current q, then
           // update i as new q
           #pragma omp critical
           {
            if (orientation(points[p], points[k], points[q]) == 2)
               q = k;
           }
        }       

    }

    // Now q is the most counterclockwise with respect to p
    // Set p as q for next iteration, to add q to result
    p = q;


} while (p != l);  // While algorithm does not return to first point

// Print Result
int j;
for (j = 0; j < count; j++){
  printf("(%d,%d)\n", results[j].x,results[j].y);
}

}

int main()
{
//declaration for start time, end time
//and total executions for the algorithm
struct timeval start, end;
int i, num_run = 100;

gettimeofday(&start,NULL);

Point points[] = {{0, 3}, {2, 2}, {1, 1}, {2, 1},
                    {3, 0}, {0, 0}, {3, 3}};

int n = sizeof(points)/sizeof(points[0]);

convexHull(points, n);

gettimeofday(&end,NULL);

int cpu_time_used = (((end.tv_sec - start.tv_sec) * 1000000) + (end.tv_usec 
- start.tv_usec));
printf("\n\nExecution time: %d ms\n", cpu_time_used);
return 0;
}

---

Tried to make the input subtantial enough by adding in these lines of code:

Point points[3000];
int i;
for(i=0;i<3000;i++) {
    points[i].x = rand()%100;
    points[i].y = rand()%100;
    int j;
    for(j=i+1;j<3000;j++) {
        if(points[i].x==points[j].x) {
            if(points[i].y==points[j].y) {
            i--; 
            break;
            }
        }
    }
}

But it crashes sometimes

Answer 1

In the following piece of your code, the whole content of the parallel for loop is wrapped into a critical statement. This means that this part of the code will never be entered by more than on thread at a time. Having multiple threads work one at a time will not go faster than if a single thread had gone through all iterations. But on top of that some time is lost in synchronization overhead (each thread must acquire a mutex before entering the critical section and release it afterwards).

int l = 0,i;
#pragma omp parallel shared (n,l) private (i)
{
    #pragma omp for
    for (i = 1; i < n; i++)
    {
        #pragma omp critical
        {
            if (points[i].x < points[l].x)
            l = i;
        }
    }
}

The serial code needs to be somewhat refactored for parallelization. Reduction is often a good approach for simple operations: have each thread compute a partial result on one part of the iterations (e.g. partial minimum, partial sum) than merge all the results into a global one. For supported operations, the #pragma omp for reduction(op:var) syntax can be used. But in this case, the reduction has to be done manually.

See how the following code relies on local variables to track the index of minimum x, then uses a single critical section to compute the global minimum index.

int l = 0,i;
#pragma omp parallel shared (n,l) private (i)
{
    int l_local = 0; //This variable is private to the thread

    #pragma omp for nowait
    for (i = 1; i < n; i++)
    {
        // This part of the code can be executed in parallel
        // since all write operations are on thread-local variables
        if (points[i].x < points[l_local].x)
            l_local = i;
    }

    // The critical section is entered only once by each thread
    #pragma omp critical
    {
    if (points[l_local].x < points[l].x)
        l = l_local;
    }

    #pragma omp barrier
    // a barrier is needed in case some more code follow
    // otherwise there is an implicit barrier at the end of the parallel region
}

The same principle should be applied to the second parallel loop, which suffer from the same issue of actually being entirely serialized by the critical statement.