A General Novel Parallel Framework for SPH-centric Algorithms

Abstract

To date, large-scale fluid simulation with more details employing the Smooth Particle Hydrodynamics (SPH) method or its variants is ubiquitous in computer graphics and digital entertainment applications. Higher accuracy and faster speed are two key criteria evaluating possible improvement of the underlying algorithms within any available framework. Such requirements give rise to high-fidelity simulation with more particles and higher particle density that will unavoidably increase computational cost significantly. In this paper, we develop a new general GPGPU acceleration framework for SPH-centric simulations founded upon a novel neighbor traversal algorithm. Our novel parallel framework integrates several advanced characteristics of GPGPU architecture (e.g., shared memory and register memory). Additionally, we have designed a reasonable task assignment strategy, which makes sure that all the threads from the same CTA belong to the same cell of the grid. With this organization, big bunches of continuous neighboring data can be loaded to the shared memory of a CTA and used by all its threads. Our method has thus low global-memory bandwidth consumption. We have integrated our method into both WCSPH and PCISPH, that are two improved variants in recent years, and demonstrated its performance with several scenarios involving multiple-fluid interaction, dam break, and elastic solid. Through comprehensive tests validated in practice, our work can exhibit up to 2.18x speedup when compared with other state-of-the-art parallel frameworks.