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Most HPC systems are clusters of shared memory nodes. Such SMP nodes can be small multi-core CPUs up to large many-core CPUs. Parallel programming may combine the distributed memory parallelization on the node interconnect (e.g., with MPI) with the shared memory parallelization inside of each node (e.g., with OpenMP or MPI-3.0 shared memory). This course analyzes the strengths and weaknesses of several parallel programming models on clusters of SMP nodes. Multi-socket-multi-core systems in highly parallel environments are given special consideration. MPI-3.0 has introduced a new shared memory programming interface, which can be combined with inter-node MPI communication. It can be used for direct neighbor accesses similar to OpenMP or for direct halo copies, and enables new hybrid programming models. These models are compared with various hybrid MPI+OpenMP approaches and pure MPI. Numerous case studies and micro-benchmarks demonstrate the performance-related aspects of hybrid programming. Tools for hybrid programming such as thread/process placement support and performance analysis are presented in a "how-to" section. Hands-on exercises give attendees the opportunity to try the new MPI shared memory interface and explore some pitfalls of hybrid MPI+OpenMP programming. This course provides scientific training in Computational Science, and in addition, the scientific exchange of the participants among themselves. Mind the new date: 14.1.2016