Nowadays, processor development drives towards an increasing number of
logical cores per processing unit. This leads to a growing need for concurrent execution to improve performance of applications. As synchronization
and communication are complex tasks in a multi-core environment, parallelization frameworks are needed. In this thesis, we explored MPI, UPC++,
Charm++, OpenMP and HPX by utilizing their concepts on a Tsunami
approximation model - the SWE-Framework. Implementations were benchmarked on the Cool-MUC2 massively parallel processor with Intel ”Haswell”
nodes. We measured performance, computation and communication time
for strong and weak scaling scenarios on up to 896 processing elements.
Overall, MPI performed best in terms of performance and scaling. UPC++
demonstrated stable communication time with increasing number of ranks,
but showed significantly higher reduction and synchronization costs. Overdecomposition of Charm++ Chares did not lead to performance improvement
on load-imbalanced scenarios, as communication overhead exceeded migration benefit. HPX showed best performance when utilizing two concurrent
tasks per processing core, but overall performed slower than all other frameworks. Concluding, the HPX implementation could be further improved by
adapting to a better fitting parallel concept. Best performance results could
be achieved by utilizing a hybrid UPC++/MPI solution.