Efficient and Scalable Algorithms for Smoothed Particle Hydrodynamics on Hybrid Shared/Distributed-Memory Architectures

Abstract

This paper describes a new fast and implicitly parallel approach to neighbor-finding in multiresolution smoothed particle hydrodynamics (SPH) simulations. This new approach is based on hierarchical cell decompositions and sorted interactions, within a task-based formulation. It is shown to be faster than traditional tree-based codes and to scale better than domain decomposition--based approaches on hybrid shared/distributed-memory parallel architectures, e.g., clusters of multicores, achieving a $40\times$ speedup over the Gadget-2 simulation code.