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 the Message Passing Interface - MPI) with the shared memory parallelization inside of each node (e.g., with OpenMP or MPI-3.0 shared memory). This course analyses the strengths and weaknesses of several parallel programming models on clusters of SMP nodes. Tools for hybrid programming such as thread/process placement support and performance analysis are presented in a "how-to" section. This course provides scientific training in Computational Science, and in addition, the scientific exchange of the participants among themselves.
The first day is dedicated to the theory. The second day provides hands-on exercises.
This course is organized by the Vienna Scientific Cluster (VSC), TU Wien in cooperation with the High-Performance Computing-Center Stuttgart (HLRS) and the Erlangen Regional Computing Center (RRZE).
See course web-page in Vienna --> http://vsc.ac.at/training/2018/HY-VSC/