Within the field of molecular dynamics, force and distance calculations are causing a long run-time. To decrease it, neighborhood-based particle search algorithms and parallelization strategies are used. The library AutoPas applies auto-tuning to dynamically choose the best strategy. Therefore, all options are tested linearly. To optimize this search, one needs to know which parameters are influencing the currently best strategy. This thesis analyzes the strategies regarding their behavior depending on different factors. For a high density an increasing number of particles leads to the parallelizations sorting by particle container type, leading to the assumption that for those scenarios the particle search algorithm is more important than the parallelization strategy. A high number of particles favored a Verlet Lists approach, while scenarios with a large domain performed best for Verlet Cluster Lists strategies. With growing in-homogeneity the run-time increased.

Keywords: Molecular Dynamics, Parallelization, Auto-Tuning, Automatic Algorithm Selection