A current trend in high-performance computing is to achieve performance portability, meaning to produce high-performing code portable to multiple architectures. For this purpose, the molecular dynamics library AutoPas employs auto-tuning to choose the best-performing algorithm for the current scenario and architecture at runtime. This work extends AutoPas with a portable force functor based on the vectorization abstraction module Google Highway and evaluates its performance.

Apart from abstracting vector intrinsics, other famous particle simulators, e.g., GROMACS, already suggest various options for filling vector registers with different particles. As their focus is the influence of the underlying architecture, we investigate which other parameters affect the optimal choice of the vectorization order. We also consider the physical properties of the experiments, e.g., the density of the scenario, as these influence the sizes of the particle buffers. Additionally, we take the impact of AutoPas’ different neighbor identification algorithms into account. Finally, we extend AutoPas’ auto-tuning mechanism to allow the best-performing vectorization order to be chosen at runtime.