Parallelization with OpenMP

What is paralelization ?

Parallelizing a code in FORTRAN can allow to gain much time. It is not too difficult but one needs to be careful. Parallelization means that the execution of the code will be cut in pieces and sent to different cpus (or cores), such that they can all work at the same time on the code, which will improve the speed.

Using OpenMP

On easy way to parallelize is to use OpenMP. On Unix machines OpenMP is included (if not go here).
All the commands that refer to OpenMP must be preceeded by !$ or !$OMP. The first so-called sentinel is used when the following code is normal but must only be executed when compiling the program in parallel (for example!$ use omp_lib which indicates that one needs the OpenMP library, see example below). The second sentinel is used when the code is an OpenMP directive (see below).
To use OpenMP you must first load the module in the files that will use OpenMP commands. For example
program myprogram
!$ use omp_lib
implicit none
real :: a
some code here with parallelization (see below)
end program
When compiling with gfortran (on Unix machines), you will need to use a flag -fopenmp:
gfortran -fopenmp -o myexec myprogram.f90
With the Fortran compiler, the flag is simply -openmp :
ifort -openmp -o myexec myprogram.f90

Do-loop with OpenMP

The most commonly use of OpenMP is in do-loop. Imagine you have a the following simple code:
program myprogram
implicit none
real :: a(100)
do i=1,100
a(i) = 2*i
end do
end program
In the above code, the loop computes something at each iteration, independently of previous iterations. This is the perfect environment to use parallilization with OpenMP. OpenMP will execute the computation of different iterations on different cores at the same time. Without OpenMP each iteration would have to be finished before the next one starts, i.e. it will all be computed sequenially. To compute using OpenMP, you can write:
program myprogram
!$ use omp_lib
implicit none
real :: a(100)
!$omp parallel do
do i=1,100
a(i) = 2*i
end do
!$omp end parallel do
end program
This way, OpenMP will automatically choose the number of cores at disposal and send to each of them approximately the same number of tasks. Here by default the variable a is shared (see below).
You can know the number of threads (or cores) by using the OpenMP subroutine omp_get_num_threads() (returns an integer) before entering the parallel region (do-loop). You can get the thread number by using omp_get_thread_num() inside the parallel region (do-loop).
Note that to break lines in an OpenMP instruction you need ,& at the end of the line and !$openmp& at the beginning of the next line.
A useful feature of parallel loops is the collaps() keyword. It allows to parallelize several nested loops. For example, for two nested loops:
!$omp parallel do collapse(2)
Another feature is the reduction. It allows openmp to automatically do a certain number of operations (addition, max, min...) on one variable, at the end of the parallel section. For example
!$omp parallel do reduction(+:sumvariable)

Other instructions with OpenMP

Important instructions in OpenMP are single(where only one instance executes the code, for example to print something), critical(where only one instance at a time executes the code) and barrier(where all instances must meet before continuing the code). It is important to give a specific name to each criticalregion.
program myprogram
!$ use omp_lib
implicit none
...
...
!$omp single
print*, "this will only be printed one time"
!$omp end single
!$omp critical (nameofcriticalsection)
print*, "this is gonna print one instance at a time"
!$omp end critical (nameofcriticalsection)
!$omp barrier
end program

Private / Shared Variables with OpenMP

Note that each core (thread) will write its own value on the variables. It is therefore important to indicate if the variable is to be shared by the threads or not. If shared, each thread reads the value that has been potentially given by another thread. If private, each variable is duplicated in each thread at the beginning (with no value assigned to it!) and lives its own life. At the end of the parallel block, the variable has no value anymore.
The dummy integer of the loop is necessarily private (no need to indicate it is private).
Parameters cannot be declared as shared or private because their value cannot change. In some sense, they are necessarily shared.
An example of code using private and shared indicators [code to be checked by running it]:
program myprogram
!$ use omp_lib
implicit none
real, parameter : pi = 3.1416
real :: sharedvar = 2.0
real :: privatevar
real :: a(100)
!$omp parallel default(none) shared(sharedvar, a) private(privatevar) do
do i=1,100
$! privatevar = omp_get_thread_num()
a(i) = sharedvar*pi*i
end do
!$omp end parallel do
end program
The default(none) indicates that all variables must be specified as either shared or private, except the dummy for the loop, i and the parameters, here pi (I think - to check).

OpenMP with modules

Module variables can only be shared, unless you use a special trick. The trick is that you need to declare them as threadprivate in the module where they are defined. Note that you need to have the line !$ use omp_lib at the beginning of the module.
module mymodule
!$ use omp_lib
implicit none
real :: a
!$ threadprivate(a)
end mymodule

Caution

Do not use print*, when parallelizing. It causes crashes. Write your stuff in a file, using the critical command for example.
Last modified 4yr ago