Difference between revisions of "DoLoops performance in Fortran"

Revision as of 16:24, 21 October 2010

What is the best performance that Fortran can give when computing do-loops over large matrices? The test-case below shows a four-times performance increase when looping over (k,j,i) insteado of (i,j,k).

Test-case

Hardware

• Intel Core i7 - 870
• 8 GB Ram

Code

program DoloopsOpenmp

   use moduleDoloopsOpenmp, only: makeloop

   implicit none
   
   integer, dimension(:,:,:), pointer      :: mycube
   integer                                 :: M = 1
   real                                    :: elapsedtime
   real                                    :: time = 0.0

   do while (M < 1000)

       write(*,*) 'Insert the cube size M (or insert 1000 to exit): '
       read(*,*) M
       
       if (M > 999) exit

       allocate(mycube(1:M,1:M,1:M))

       !Tic()    
       time = elapsedtime(time)

       call makeloop(mycube)

       !Toc()
       time = elapsedtime(time)
       
       write(*,10) time
       write(*,*) 
        
       deallocate(mycube)
       nullify(mycube)

   end do

   10 format('Time elapsed: ',F6.2)
   
end program DoloopsOpenmp

!This function computes the time
real function elapsedtime(lasttime)

   integer     :: count, count_rate
   real        :: lasttime

   call system_clock(count, count_rate)
   
   elapsedtime = count * 1.0 / count_rate - lasttime

end function elapsedtime

module moduleDoloopsOpenmp

   use omp_lib

   implicit none

   private

   public :: makeloop

contains

   subroutine makeloop(cubicmatrix)
   
       !Arguments --------------
       integer, dimension(:,:,:), pointer  :: cubicmatrix
   
       !Local variables --------
       integer                             :: i, j, k, lb, ub
       !$ integer                          :: CHUNK, NTHREADS
       
       lb = lbound(cubicmatrix,3)
       ub = ubound(cubicmatrix,3)        
       
       !$OMP PARALLEL PRIVATE(i,j,k)        
       !$ NTHREADS = OMP_GET_NUM_THREADS()
       !!$ CHUNK = 10
       !$ CHUNK = max( ( ub - lb ) / NTHREADS + 1, 1)        
       !$OMP DO SCHEDULE(DYNAMIC, CHUNK)
       do k = lb, ub
       do j = lb, ub
       do i = lb, ub
   
           cubicmatrix(i,j,k) = cubicmatrix(i,j,k) + 1
   
       end do
       end do
       end do
       !$OMP END DO
       !$OMP END PARALLEL

   end subroutine makeloop
   
end module moduleDoloopsOpenmp

Results

DO (i,j,k) / NO CHUNK

Table A.1 - Debug do(i,j,k) Size Time 100 0.04 200 0.37 300 1.58 400 7.60 500 19.66 600 41.65

Table A.2 - Debug openmp without !$OMP PARALLEL directives do(i,j,k) Size Time 100 0.04 200 0.37 300 1.58 400 7.27 500 19.34 600 41.34

Table A.3 - Debug openmp with one !$OMP PARALLEL DO directive do(i,j,k) Size Time 100 0.02 200 0.19 300 0.70 400 1.86 500 4.05 600 7.83

DO (k,j,i) / NO CHUNK

Table B.1 - Debug do(k,j,i) Size Time 100 0.04 200 0.31 300 1.22 400 3.36 500 7.55 600 14.88

Table B.2 - Debug openmp without !$OMP PARALLEL directives do(k,j,i) Size Time 100 0.04 200 0.31 300 1.21 400 3.36 500 7.82 600 15.07

Table B.3 - Debug openmp with one !$OMP PARALLEL DO directive do(k,j,i) Size Time 100 0.02 200 0.09 300 0.36 400 0.94 500 2.04 600 3.89

DO (k,j,i) / STATIC CHUNK = (UBOUND - LBOUND) / NTHREADS + 1

Table C.3 - Debug openmp with one !$OMP PARALLEL DO directive do(k,j,i) Size Time 100 0.02 200 0.15 300 0.42 400 1.03 500 2.12 600 3.97

DO (k,j,i) / STATIC CHUNK = 10

Table D.3 - Debug openmp with one !$OMP PARALLEL DO directive do(k,j,i) Size Time 100 0.02 200 0.16 300 0.43 400 1.04 500 2.18 600 4.05

DO (k,j,i) / DYNAMIC CHUNK = 10

Table E.3 - Debug openmp with one !$OMP PARALLEL DO directive do(k,j,i) Size Time 100 0.01 200 0.10 300 0.36 400 0.93 500 2.01 600 3.89

DO (k,j,i) / DYNAMIC CHUNK = (UBOUND - LBOUND) / NTHREADS + 1

Table F.3 - Debug openmp with one !$OMP PARALLEL DO directive do(k,j,i) Size Time 100 0.02 200 0.09 300 0.39 400 1.04 500 2.14 600 4.00

Conclusions

• do(k,j,i) Vs do(i,j,k) ==> 2 to 4 times faster!
• dynamic small chunks, or no chunk at all yield 10% increased performance over large dynamic chunks. Probably better off with no-chunk.
• More test-cases representing different scenarios of do-loops may yield different choices of CHUNK/scheduling.