Let us start with a basic example: we would like to do some preprocessing operation a()
, then we would like to some independent calculations c(0)
, c(1)
, …, and finally some postprocessing z()
once all calculations have finished. In this example, each of the calculations takes roughly the same amount of time. A straightforward for-loop uses only one thread and hence only one core on a multi-core computer:
a();
for (int i = 0; i < 10; ++i) {
c(i);
}
z();
With OpenMP parallel for loops, we can easily parallelize it so that we are making a much better use of the computer. Note that OpenMP automatically waits for all threads to finish their calculations before continuing with the part that comes after the parallel for loop:
a();
#pragma omp parallel for
for (int i = 0; i < 10; ++i) {
c(i);
}
z();
The omp parallel for
directive is just a commonly-used shorthand for the combination of two directives: omp parallel
, which declares that we would like to have multiple threads in this region, and omp for
, which asks OpenMP to assign different iterations to different threads:
a();
#pragma omp parallel
{
#pragma omp for
for (int i = 0; i < 10; ++i) {
c(i);
}
}
z();
A common mistake is to just use omp parallel
together with a for loop. This creates multiple threads for you, but it does not do any work-sharing — all threads simply run all iterations of the loop, which is most likely not what you want:
a();
#pragma omp parallel
for (int i = 0; i < 8; ++i) {
c(i);
}
z();
Another common mistake is to use omp for
alone outside a parallel region. It does not do anything if we do not have multiple threads available:
a();
#pragma omp for
for (int i = 0; i < 8; ++i) {
c(i);
}
z();
While in many cases it is convenient to combine omp parallel
and omp for
in one directive, please remember that you can always split them. This way it is possible to do some thread-specific preprocessing and postprocessing if needed:
a();
#pragma omp parallel
{
b();
#pragma omp for
for (int i = 0; i < 10; ++i) {
c(i);
}
d();
}
z();
For example, this way you can declare local variables and initialize local data structures in a convenient manner.