Improving the parallel efficiency of large-scale structural dynamic analysis using a hierarchical approach

Abstract

In order to improve the parallel efficiency of large-scale structural dynamic analysis, a hierarchical approach adapted to the hardware topology of multi-core clusters is proposed. The hierarchical approach is constructed based on the strategies of two-level partitioning and two-level condensation. The data for parallel computing is first prepared through two-level partitioning to guarantee the load balancing within and across nodes. Then during the analysis of each time step, the convergence rate of interface problem is significantly improved by further reducing its size with two-level condensation. Furthermore, the communication overheads are considerably reduced by separating the intra-node and inter-node communications and minimizing the inter-node communication. Numerical experiments conducted on Dawning-5000A supercomputer indicate that the hierarchical approach was superior in performance compared with the conventional Newmark algorithm based on the domain decomposition method.