In Molecular Dynamics simulations, it is important to analyze the characteristics of the particles. Modern simulation tools like AutoPas provide auto-selection of different algorithmic configurations at runtime, which can be time-consuming when there are many configurations to choose from. This thesis aims to implement a new tuning strategy called Cluster-Based Tuning, which groups simulation data into clusters and assigns configurations to each cluster that are expected to perform well. We observed speedups between 4 and 28 times in the tuning phases in two different scenarios against full-search, and speedups in total force update time between 9% and 16%.     «

In Molecular Dynamics simulations, it is important to analyze the characteristics of the particles. Modern simulation tools like AutoPas provide auto-selection of different algorithmic configurations at runtime, which can be time-consuming when there are many configurations to choose from. This thesis aims to implement a new tuning strategy called Cluster-Based Tuning, which groups simulation data into clusters and assigns configurations to each cluster that are expected to perform well. We...     »