TY - JOUR
T1 - A new multi-resolution parallel framework for SPH
AU - Ji, Zhe
AU - Fu, Lin
AU - Hu, Xiangyu Y.
AU - Adams, Nikolaus A.
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - In this paper we present a new multi-resolution parallel framework, which is designed for large-scale SPH simulations of fluid dynamics. An adaptive rebalancing criterion and monitoring system is developed to integrate the CVP partitioning method as rebalancer to achieve dynamic load balancing of the system. A localized nested hierarchical data structure is developed in cooperation with a tailored parallel fast-neighbor-search algorithm to handle problems with arbitrarily adaptive smoothing-length and to construct ghost buffer particles in remote processors. The concept of “diffused graph” is proposed in this paper to improve the performance of the graph-based communication strategy. By utilizing the hybrid parallel model, the framework is able to exploit the full parallel potential of current state-of-the-art clusters based on Distributed Shared Memory (DSM) architectures. A range of gas dynamics benchmarks are investigated to demonstrate the capability of the framework and its unique characteristics. The performance is assessed in detail through intensive numerical experiments at various scales.
AB - In this paper we present a new multi-resolution parallel framework, which is designed for large-scale SPH simulations of fluid dynamics. An adaptive rebalancing criterion and monitoring system is developed to integrate the CVP partitioning method as rebalancer to achieve dynamic load balancing of the system. A localized nested hierarchical data structure is developed in cooperation with a tailored parallel fast-neighbor-search algorithm to handle problems with arbitrarily adaptive smoothing-length and to construct ghost buffer particles in remote processors. The concept of “diffused graph” is proposed in this paper to improve the performance of the graph-based communication strategy. By utilizing the hybrid parallel model, the framework is able to exploit the full parallel potential of current state-of-the-art clusters based on Distributed Shared Memory (DSM) architectures. A range of gas dynamics benchmarks are investigated to demonstrate the capability of the framework and its unique characteristics. The performance is assessed in detail through intensive numerical experiments at various scales.
KW - Centroidal Voronoi Particle method
KW - Compressible fluid dynamics
KW - Edge coloring
KW - Fast neighbor search
KW - High-performance parallel computing
KW - Smoothed Particle Hydrodynamics
UR - http://www.scopus.com/inward/record.url?scp=85055485198&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2018.09.043
DO - 10.1016/j.cma.2018.09.043
M3 - Article
AN - SCOPUS:85055485198
SN - 0045-7825
VL - 346
SP - 1156
EP - 1178
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
ER -