Parallel Programming in Fortran 95 using OpenMP - People

12 2. OpenMP constructs
real(8) :: A(1000), B(1000)
do i = 1, 1000
B(i) = 10 * i
A(i) = A(i) + B(i)
enddo
because the correct value of the matrix B is not ensured until the end of the worksharing
construct !$OMP END DO.
The fact that the iterations of the do-loop are distributed over the different threads
in an unpredictable way 3 , discards certain do-loops from being parallelized. For example:
real(8) :: A(1000)
do i = 1, 999
A(i) = A(i+1)
enddo
The reason for the incorrect working is that the value at the index i+1 is needed in
its unmodified state when performing the iteration i. This will lead to no trouble when
executed in a serial way, but when running in parallel the unmodified state of i+1 is not
granted at the time of computing iteration i. Therefore, an unpredictable result will be
obtained when running in parallel. This situation is known as racing condition: the
result of the code depends on the thread scheduling and on the speed of each processor.
By modifying the previous do-loop it is possible to achieve a parallelizable version leading
to the following parallelized code 4 :
real(8) :: A(1000), dummy(2:1000:2)
!Saves the even indices
!$OMP DO
do i = 2, 1000, 2
dummy(i) = A(i)
enddo
!$OMP END DO
!Updates even indices from odds
!$OMP DO
do i = 0, 998, 2
A(i) = A(i+1)
enddo
!$OMP END DO
!Updates odd indices with evens
!$OMP DO
3 Although the expression ”unpredictable” usually means something negative, in this case it states
something that is inherent in the philosophy of OpenMP, namely the separation between the parallel
programming and the underlying hardware and work distribution.
4 The presented modification is not the only possible one, but at least it allows the parallelization of
the do-loop at expense of additional memory requirements.