Integration of General Sparse Matrix and Parallel Computing Technologies for Large–Scale Structural Analysis

Abstract

Both general sparse matrix and parallel computing technologies are integrated in this study as a finite element solution of large–scale structural problems in a PC cluster environment. The general sparse matrix technique is first employed to reduce execution time and storage requirements for solving the simultaneous equilibrium equations in finite element analysis. To further reduce the time required for large–scale structural analyses, two parallel processing approaches for sharing computational workloads among collaborating processors are then investigated. One approach adopts a publicly available parallel equation solver, called SPOOLES, to directly solve the sparse finite element equations, while the other employs a parallel substructure method for the finite element solution. This work focuses more on integrating the general sparse matrix technique and the parallel substructure method for large–scale finite element solutions. Additionally, numerical studies have been conducted on several large–scale structural analyses using a PC cluster to investigate the effectiveness of the general sparse matrix and parallel computing technologies in reducing time and storage requirements in large–scale finite element structural analyses.