• Parallel loop (DO or for)
• Parallel section
• General work-sharing (Fortran only)

It is a very simple and mechanical process to divide up the work over a number of threads simply by scheduling a different thread to work on the for-body using a different index value.

The division of labor (splitting the work of a for-loop) of a for-loop can be done in OpenMP through a special Parallel LOOP construct.

#pragma omp parallel { .... #pragma omp for [parameters] for-statement // Parallel Loop .... }

Each iteration of the for-loop is executed exactly once by each thread.

The loop variable used in the Parallel LOOP construct is by default PRIVATE (other variables are still by default SHARED)

double f(double a) { return( 2.0 / sqrt(1 - a*a) ); } int main(int argc, char *argv[]) { int N; double sum; // Shared variable, updated ! double x, w; N = ...; // accuracy of the approximation w = 1.0/N; sum = 0.0; #pragma omp parallel { int i; double mypi, x; mypi = 0.0; #pragma omp for for (i = 0; i < N; i = i + 1) { x = w*(i + 0.5); // Save us the trouble of dividing mypi = mypi + w*f(x); // the work up... } #pragma omp critical { sum = sum + mypi; } } cout << sum; }

• Notice we write the for-loop as if we were summing the integration rectangle in a sequential program

  The C/C++ compiler will insert instructions that distribute the execution of the each iteration of for-loop to some thread - it is no longer your problem to "skip" index count to accomplish load distribution !

• Example Program: (OpenMP compute Pi) --- click here        

• Compile with:
  CC -xopenmp openMP_compute_pi-3.C

• Run with:

  export OMP_NUM_THREADS=8
  a.out 50000000

  Change OMP_NUM_THREADS and see the difference in performance

setenv STACKSIZE nBytes