# pragma omp parallel for reduction(+:norm2) for ( int i = 0, sum = 0.0 i < VECTOR_LEN i ++ ) norm2 += ( v * v ) The following example is a bit more advanced in that even reductions of variables over multiple threads are easily to parallel: This fork-join model of execution eases a lot of the programmer's pain involved with Threads.īeing a loop-centric approach, OpenMP is aimed at codes with long/time-consuming loops.Ī single combined directive pragma omp parallel for will tell the compiler to automatically parallel the ensuing for-loop. Whenever the compiler encompasses a semantic block of code encapsulated in a parallel region, this block of code is transparently compiled into a function, which is passed to a so-called team-of-threads upon entering this semantic block. With regard to ease-of-use, OpenMP is ahead of any other common approach: the source-code is annotated using #pragma omp or !$omp statements, in C/C++ and Fortran respectively. OpenMP is a mature specification to allow easy, portable, and most importantly incremental node-level parallelisation of code.īeing a thread-based approach, OpenMP is aimed at more fine-grained parallelism than MPI.Īlthough there have been extensions to extend OpenMP for inter-node parallelisation, it is a node-level approach aimed to make best usage of a node's cores. More informations about Compilers installed at our clusters is available here: Please refer to the Modules Documentation how to setup your environment on bwUniCluster to load a specific software installation. This page will provide information regarding the supported parallel programming paradigms and specific hints on their usage.

3.4.3.1 Intel MPI without multithreading on the bwUniCluster.

2.3.1 OpenMP with GNU Compiler Collection.

Install openmp and openmpi