If for example I have the following do loop
!$OMP DO PRIVATE(i,j,k,l,...) DEFAULT(SHARED)
do i=1,length
...
do j=k,l
...
end do
end do
!$OMP END DO
Will the division of the work between threads be "length/numberofthreads"? Thanks.
The default schedule is implementation dependent. see: https://computing.llnl.gov/tutorials/openMP/
If you want to control it use SCHEDULE(x) where x is one of STATIC, DYNAMIC, GUIDED, RUNTIME, AUTO. See the link for descriptions.
Related
Explanation of code and approach:
There are various mathematical methods (fortran subroutines) to solve a variable y, each method is sequential and runs on a single thread. The speed of each methods solution is dependent on unknown conditions (i.e. it is a no free lunch situation and I do not know which method is fastest). Therefor, the approach is to run each method on a separate thread, and once a method has found the solution, calculations on the other threads should stop (as they are required for operations after the parallel sections region)
!$omp parallel sections lastprivate(x, y)
!$omp section
call method_1_for_solving_y(x)
!$omp cancel sections
!$omp section
call method_2_for_solving_y(x)
!$omp cancel sections
. . .
!$omp section
call method_z_for_solving_y(x)
!$omp cancel sections
!$omp end parallel sections
The question:
The !$omp cancel sections construct does not completely cancel all operations on the threads that have not found the solution yet, is there a way to completely stop calculations on those threads?
Any additional advice, or possible other approaches would be appreciated.
Regards.
I am trying to parallelise some legacy Fortran code with OpenMP.
Checking for race conditions with Intel Inspector, I have come across a problem in the following code (simplified, tested example):
PROGRAM TEST
!$ use omp_lib
implicit none
DOUBLE PRECISION :: x,y,z
COMMON /firstcomm/ x,y,z
!$OMP THREADPRIVATE(/firstcomm/)
INTEGER :: i
!$ call omp_set_num_threads(3)
!$OMP PARALLEL DO
!$OMP+ COPYIN(/firstcomm/)
!$OMP+ PRIVATE(i)
do i=1,3000
z = 3.D0
y = z+log10(z)
x=y+z
enddo
!$OMP END PARALLEL DO
END PROGRAM TEST
Intel Inspector detects a race condition between the following lines:
!$OMP PARALLEL DO (read)
z = 3.D0 (write)
The Inspector "Disassembly" view offers the following about the two lines, respectively (I do not understand much about these, apart from the fact that the memory addresses in both lines seem to be different):
0x3286 callq 0x2a30 <memcpy>
0x3338 movq %r14, 0x10(%r12)
As in my main application, the problem occurs for one (/some) variable in the common block, but not for others that are treated in what appears to be the same way.
Can anyone spot my mistake, or is this race condition a false positive?
I am aware that the use of COMMON blocks, in general, is discouraged, but I am not able to change this for the current project.
Technically speaking, your example code is incorrect since you are using COPYIN to initialise threadprivate copies with data from uninitialised COMMON BLOCK. But that is not the reason for the data race - adding a DATA statement or simply assigning to x, y, and z before the parallel region does not change the outcome.
This is either a (very old) bug in Intel Fortran Compiler, or Intel is interpreting strangely the text of the OpenMP standard (section 2.15.4.1 of the current version):
The copy is done, as if by assignment, after the team is formed and prior to the start of execution of the associated structured block.
Intel implements the emphasised text by inserting a memcpy at the beginning of the outlined procedure. In other words:
!$OMP PARALLEL DO COPYIN(/firstcomm/)
do i = 1, 3000
...
end do
!$OMP END PARALLEL DO
becomes (in a mixture of Fortran and pseudo-code):
par_region0:
my_firstcomm = get_threadprivate_copy(/firstcomm/)
if (my_firstcomm != firstcomm) then
memcpy(my_firstcomm, firstcomm, size of firstcomm)
end if
// Actual implementation of the DO worksharing construct
call determine_iterations(1, 3000, low_it, high_it)
do i = low_it, high_it
...
... my_firstcomm used here instead of firstcomm
...
end do
call openmp_barrier
end par_region0
MAIN:
// Prepare a parallel region with 3 threads
// and fire the outlined code in the worker threads
call start_parallel_region(3, par_region0)
// Fire the outlined code in the master thread
call par_region0
call end_parallel_region
The outlined procedure first finds the address of the threadprivate copy of the common block, then compares that address to the address of the common block itself. If both addresses match, then the code is being executed in the master thread and no copy is needed, otherwise memcpy is called to make a bitwise copy of the master's data into the threadprivate block.
Now, one would expect that there should be a barrier at the end of the initialisation part and right before the start of the loop, and although Intel employees claim that there is one, there is none (tested with ifort 11.0, 14.0, and 16.0). Even more, the Intel Fortran Compiler does not honour the list of variables in the COPYIN clause and copies the entire common block if any variable contained in it is listed in the clause, i.e. COPYIN(x) is treated the same as COPYIN(/firstcomm/).
Whether those are bugs or features of Intel Fortran Compiler, only Intel could tell. It could also be that I'm misreading the assembly output. If anyone could find the missing barrier, please let me know. One possible workaround would be to split the combined directive and insert an explicit barrier before the worksharing construct:
!$OMP PARALLEL COPYIN(/firstcomm/) PRIVATE(I)
!$OMP BARRIER
!$OMP DO
do i = 1, 3000
z = 3.D0
y = z+log10(z)
x = y+z
end do
!$OMP END DO
!$OMP END PARALLEL
With that change, the data race will shift into the initialisation of the internal dispatch table within the log10 call, which is probably a false positive.
GCC implements COPYIN differently. It creates a shared copy of the threadprivate data of the master thread, which copy it then passes on to the worker threads for use in the copy process.
I am having issues with openmp, described as follows:
I have the serial code like this
subroutine ...
...
do i=1,N
....
end do
end subroutine ...
and the openmp code is
subroutine ...
use omp_lib
...
call omp_set_num_threads(omp_get_num_procs())
!$omp parallel do
do i=1,N
....
end do
!$omp end parallel do
end subroutine ...
No issues with compiling, however when I run the program, there are two major issues compared to the result of serial code:
The program is running even slower than the serial code (which supposedly do matrix multiplications (matmul) in the do-loop
The numerical accuracy seems to have dropped compared to the serial code (I have a check for it)
Any ideas what might be going on?
Thanks,
Xiaoyu
In case of an parallelization using OpenMP, you will need to specify the number of threads your program is to use. You can do so by using the environment variable OMP_NUM_THREADS, e.g. calling your program by means of
OMP_NUM_THREADS=5 ./myprogram
to execute it using 5 threads.
Alternatively, you may set the number of threads at runtime omp_set_num_threads (documentation).
Side Notes
Don't forget to set private variables, if there are any within the loop!
Example:
!$omp parallel do private(prelimRes)
do i = 1, N
prelimRes = myFunction(i)
res(i) = prelimRes + someValue
end do
!$omp end parallel do
Note how the variable prelimRes is declared private so that every thread has its own workspace.
Depending on what you actually do within the loop (i.e. use OpenBLAS), your results may indeed vary (variations should be smaller than 1e-8 with regard to double precision variables) due to the differing, parellel processing.
If you are unsure about what is happening, you should check the CPU load using htop or a similar program while your program is running.
Addendum: Setting the number of threads to automatically match the number of CPUs
If you would like to use the maximum number of useful threads, e.g. use as many threads as there are CPUs, you can do so by using (just like you stated in your question):
subroutine ...
use omp_lib
...
call omp_set_num_threads(omp_get_num_procs())
!$omp parallel do
do i=1,N
....
end do
!$omp end do
!$omp end parallel
end subroutine ...
I'm having problems trying to nest a OMP DO directive inside another OMP DO directive in Fortran.
Here's the following code:
DO in=2,n_niveles
allocate(cvalor(2,npuntosp(in),npuntost(in)))
!allocate(avalor(2,npuntosp(in-1),npuntost(in-1)))
allocate(valor_t2(npuntost(in),npuntosp(in-1),2))
!$OMP PARALLEL NUM_THREADS(hilos) DEFAULT(PRIVATE) FIRSTPRIVATE(n_niveles,in) SHARED(npuntosp,npuntost,cubos,central_reg,sumazm1n,expo,mphi,mtheta)
!$OMP DO SCHEDULE(STATIC)
DO aux=1,cubos(in-1)%ncubos_nivel
...
(some code here)
...
!$OMP PARALLEL NUM_THREADS(hilos) DEFAULT(PRIVATE) FIRSTPRIVATE(cuboj,in) SHARED(valor_t2,cvalor)
!$OMP DO SCHEDULE(STATIC)
do i=1,npuntost(in)
val=mtheta(in-1)%inicio(i,1)
do jj=val,val+mtheta(in-1)%inicio(i,2)
do k=1,npuntosp(in-1)
valor_t2(i,k,1)=valor_t2(i,k,1)+mtheta(in-1)%matriz(i,jj)*sumazm1n(in-1)%region(cuboj)%valor(1,k,jj)
valor_t2(i,k,2)=valor_t2(i,k,2)+mtheta(in-1)%matriz(i,jj)*sumazm1n(in-1)%region(cuboj)%valor(2,k,jj)
end do
end do
do k=1,npuntosp(in)
val=mphi(in-1)%inicio(k,1)
do jj=val,val+mphi(in-1)%inicio(k,2)
cvalor(1,k,i)=cvalor(1,k,i)+valor_t2(i,jj,1)*mphi(in-1)%matriz(jj,k)
cvalor(2,k,i)=cvalor(2,k,i)+valor_t2(i,jj,2)*mphi(in-1)%matriz(jj,k)
end do
end do
end do
!$OMP END DO
!$OMP END PARALLEL
...
(some code here)
...
END DO
!$OMP END DO
!$OMP END PARALLEL
deallocate(cvalor)
deallocate(valor_t2)
END DO
When the code is executed, an access violation exception occurs inside the second OpenMP parallel region. Sometimes that exception is changed for an overflow at the variable valor_t2.
Maybe OpenMP does not support this kind of parallelization, but I've searched over the net and didn't found anything about. I know that OpenMP supports the use of various OMP PARALLEL directives nested one inside another and I know how it works. But I'm having a headache with this problem.
Any ideas about what it's happening?
Thank you so much!
You're going to want to use the collapse clause in the do loop at the top level. See the link below for information:
https://computing.llnl.gov/tutorials/openMP/
As long as the code represented by (some code here) doesn't contain any loops, this should work.
I am trying to sum up of a variable with openmp with code given below.
normr=0.0
!$omp parallel default(private) shared(nelem,normr,cell_data,alphar,betar,k)
!$omp do REDUCTION(+:normr)
do ii=1,nelem
nnodese=cell_data(ii)%num_vertex
pe=cell_data(ii)%porder
ndofe=cell_data(ii)%ndof
num_neighboure=cell_data(ii)%num_neighbour
be=>cell_data(ii)%Force
Ke=>cell_data(ii)%K
Me=>cell_data(ii)%M
pressuree=>cell_data(ii)%p
Rese=>cell_data(ii)%Res
neighbour_indexe=>cell_data(ii)%neighbour_index(:)
Rese(:)=be(:)
Rese(:)=Rese(:)-cmplx(-1.0,1.0*alphar/k)*matmul(Me(:,:),pressuree(:))
Rese(:)=Rese(:)-cmplx(1.0,1.0*k*betar)*matmul(Ke(:,:),pressuree(:))
do jj=1,num_neighboure
nbeindex=neighbour_indexe(jj)
Knbe=>cell_data(ii)%neighbour(jj)%Knb
pressurenb=>cell_data(nbeindex)%p
ndofnb=cell_data(nbeindex)%ndof
Rese(:)=Rese(:)-cmplx(1.0,1.0*k*betar)*matmul(Knbe(:,:),pressurenb(:))
nullify(pressurenb)
nullify(Knbe)
end do
normr=normr+dot_product(Rese(:),Rese(:))
nullify(pressuree)
nullify(Ke)
nullify(Me)
nullify(Rese)
nullify(neighbour_indexe)
nullify(be)
end do
!$omp end do
!$omp end parallel
The result for summed variable, normr, is different for parallel and sequantial code. In one of the posts I have seen that inner loop variable should be defined inside the parallel construct(Why I don't know). I also changed the pointers to locall allocated variables but result did not changed. normr is a saved real variable.
Any suggestions and helps will be appreciated.
Best Regards,
Gokmen
normr can be different for the parallel and the sequential code, because the summation does not take place in the same order. Hence, the difference does not need to be an error and can be expected from the reduction operation.
Not being an error does not necessary mean not being a problem. One way around this would be to move the summation out of the parallel loop:
!$omp parallel default(private) shared(... keep_dot_product)
!$OMP do
do ii=1,nelem
! ...
keep_dot_product(ii) = dot_product(Rese(:),Rese(:))
! ...
end do
!$omp end do
!$omp end parallel
normr = sum(keep_dot_product)