Parallel-Vector Algorithms for Analysis of Large Structures

Abstract

In analysis of large space structures, the major computational steps are evaluation and assembly of the structure stiffness matrix and solution of the resulting simultaneous linear equations. In this paper we present efficient parallel-vector algorithms for these steps of structural analysis. The goal is to optimize the performance of the algorithms through judicious combination of parallel processing and vectorization. Parallel-vector algorithms are presented for solution of linear simultaneous equations using Cholesky decomposition and preconditioned conjugate gradient approaches. The algorithms are applied to three large space structures modeling the exterior envelope of high-rise and super high-rise building structures in the range of 50–162 stories with up to 6,136 members. Performance results are presented in terms of central-processing-unit time on a Cray Y-MP8/864 supercomputer, MFLOPS (millions of floating point operations per second), and speedup.