A domain decomposition strategy for hybrid parallelization of moving particle semi-implicit (MPS) method for computer cluster

作者：Fernandes Davi Teodoro^*; Cheng Liang Yee; Favero Eric Henrique; Nishimoto Kazuo

来源：Cluster Computing, 2015, 18(4): 1363-1377.

DOI：10.1007/s10586-015-0483-3

• Cluster computing
• Domain decomposition
• Particle method
• Computational fluid dynamics

The MPS is a particle method developed to simulate incompressible flows with free surfaces that has several applications in nonlinear hydrodynamics. Much effort has been done to handle the large amount of particles required for simulating practical problems with desired refinement. However, the efficient use of the currently available computational resources, such as computer cluster, remains as a challenge. The present paper proposes a new strategy to parallelize the MPS method for fully distributed computing in cluster, which enables to simulate models with hundreds of millions of particles and keeps the required runtime within reasonable limits, as shown by the analysis of scalability and performance. The proposed strategy uses a non-geometric dynamic domain decomposition method that provides homogeneous load balancing and for very large models the scalability is supra-linear. Also, the domain decomposition (DD) is carried out only in the initial setup. As a result, the DD method is based on renumbering of particles using an original fully distributed sorting algorithm. Moreover, unlike the usual strategies, none of the processors require access to global data of the particles on any time step. Therefore, the limit for the maximum size of the model depends more on the total memory of the allocated nodes than the quantity of the local memory of each node. Thus, by extending the application of MPS method to very large models, this study contributes to consolidating the method as a practical tool to investigate complex engineering problems.

• 出版日期2015-12