openmp

The cluster that I work with recently switched from SGE to SLURM. I was wondering what the difference between sbatch options --ntasks and --cpus-per-task ? --ntasks seemed appropriate for some MPI jobs that I ran but did not seem appropriate for some OpenMP jobs that I ran. For the OpenMP jobs in my SLURM script, I specified: #SBATCH --ntasks=20 All the nodes in the partition are 20core machines, so only 1 job should run per machine. However, multiple jobs were running simultaneously on each node. Tasks in SLURM are basically processes / mpi ranks - it seems you just want a single task. A task

What schedule algorithm is used when no schedule clause is specified? I.e.: #pragma omp parallel for for (int i = 0; i < n; ++i) Foo(i); Start from the documentation that you have linked to. Section 2.7.1.1 Determining the Schedule of a Worksharing Loop reads: If the loop directive does not have a schedule clause then the current value of the def-sched-var ICV determines the schedule. The sentence preceding the quoted one refers to Section 2.3.1 which reads: def-sched-var - controls the implementation defined default scheduling of loop regions. There is one copy of this ICV per device. The

How can I parallelize an array shift with OpenMP? I've tryed a few things but didn't get any accurate results for the following example (which rotates the elements of an array of Carteira objects, for a permutation algorithm): void rotaciona(int i) { Carteira aux = this->carteira[i]; for(int c = i; c < this->size - 1; c++) { this->carteira[c] = this->carteira[c+1]; } this->carteira[this->size-1] = aux; } Thank you very much! This is an example of a loop with loop-carried dependencies , and so can't be easily parallelized as written because the tasks (each iteration of the loop) aren't

I'm sitting with some stuff here trying to make orphaning work, and reduce the overhead by reducing the calls of #pragma omp parallel . What I'm trying is something like: #pragma omp parallel default(none) shared(mat,mat2,f,max_iter,tol,N,conv) private(diff,k) { #pragma omp master // I'm not against using #pragma omp single or whatever will work { while(diff>tol) { do_work(mat,mat2,f,N); swap(mat,mat2); if( !(k%100) ) // Only test stop criteria every 100 iteration diff = conv[k] = do_more_work(mat,mat2); k++; } // end while } // end master } // end parallel The do_work depends on the previous

I'm attempting to add OpenMP to a project that is building with CMake. I'm having no problem building it on Linux with the standard CMake/OpenMP addition: find_package(OpenMP) if (OPENMP_FOUND) set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") endif() Unfortunately this doesn't seem to work on macOS targets. When cmake is called, the following error is given: -- Could NOT find OpenMP_C (missing: OpenMP_C_FLAGS) -- Could NOT find OpenMP_CXX

I assume thread creation and deletion could be costly. Does OpenMP try reuse existing threads? For example, #pragma omp parallel sections num_threads(4) { #pragma omp section { ... worker A ... } #pragma omp section { ... worker B ... } } #pragma omp parallel sections num_threads(4) { #pragma omp section { ... worker C ... } #pragma omp section { ... worker D ... } } In execution, does OpenMP allocate 5 threads or 3 (in which C and D reuse the threads that A and B used)? In your example, a team of 4 "working" threads will be created/activated upon entry of your first parallel section, and 2 of