Fast Octree Neighborhood Search for SPH Simulations

Abstract

We present a new octree-based neighborhood search method for SPH simulation. A speedup of up to 1.9x is observed in comparison to state-of-the-art methods which rely on uniform grids. While our method focuses on maximizing performance in fixed-radius SPH simulations, we show that it can also be used in scenarios where the particle support radius is not constant thanks to the adaptive nature of the octree acceleration structure. Neighborhood search methods typically consist of an acceleration structure that prunes the space of possible particle neighbor pairs, followed by direct distance comparisons between the remaining particle pairs. Previous works have focused on minimizing the number of comparisons. However, in an effort to minimize the actual computation time, we find that distance comparisons exhibit very high throughput on modern CPUs. By permitting more comparisons than strictly necessary, the time spent on preparing and searching the acceleration structure can be reduced, yielding a net positive speedup. The choice of an octree acceleration structure, instead of the uniform grid typically used in fixed-radius methods, ensures balanced computational tasks. This benefits both parallelism and provides consistently high computational intensity for the distance comparisons. We present a detailed account of high-level considerations that, together with low-level decisions, enable high throughput for performance-critical parts of the algorithm. Finally, we demonstrate the high performance of our algorithm on a number of large-scale fixed-radius SPH benchmarks and show in experiments with a support radius ratio up to 3 that our method is also effective in multi-resolution SPH simulations.

SOMA-BD: Brownian Dynamics Simulation for Soft Matter on GPU

“…Particles can drastically reduce the processing cost of identifying possible interaction partners by navigating adjacent cells within a predetermined radius. Hierarchical techniques Fernández-Fernández et al (2022); Howard et al (2016Howard et al ( , 2019; Ohno et al (2017) use effective tree traversal algorithms to arrange particles into hierarchical data structures that facilitate quick neighbor searches. Because hierarchical methods are flexible and scalable, they may be used in simulations with different system densities and sizes.…”