Compressed Neighbour Lists for SPH

Abstract

We propose a novel compression scheme to store neighbour lists for iterative solvers that employ Smoothed Particle Hydrodynamics (SPH). The compression scheme is inspired by Stream VByte, but uses a non-linear mapping from data to data bytes, yielding memory savings of up to 87%. It is part of a novel variant of the Cell-Linked-List (CLL) concept that is inspired by compact hashing with an improved processing of the cell-particle relations. We show that the resulting neighbour search outperforms compact hashing in terms of speed and memory consumption. Divergence-Free SPH (DFSPH) scenarios with up to 1.3 billion SPH particles can be processed on a 24-core PC using 172 GB of memory. Scenes with more than 7 billion SPH particles can be processed in a Message Passing Interface (MPI) environment with 112 cores and 880 GB of RAM. The neighbour search is also useful for interactive applications. A DFSPH simulation step for up to 0.2 million particles can be computed in less than 40 ms on a 12-core PC.