Multi-Level-Memory Structures for Adaptive SPH Simulations

Abstract

In this paper we introduce a novel hash map-based sparse data structure for highly adaptive Smoothed Particle Hydrodynamics (SPH) simulations on GPUs. Our multi-level-memory structure is based on stacking multiple independent data structures, which can be created efficiently from the same particle data by utilizing self-similar particle orderings. Furthermore, we propose three neighbor list algorithms that improve performance, or significantly reduce memory requirements, when compared to Verlet-lists for the overall simulation. Overall, our proposed method significantly improves the performance of spatially adaptive methods, allows for the simulation of unbounded domains and reduces memory requirements without interfering with the simulation.