Concurrent Structural Optimization on Massively Parallel Supercomputer
Publication: Journal of Structural Engineering
Volume 121, Issue 11
Abstract
Genetic-algorithm (GA)–based structural optimization can be parallelized to a high degree on new generation of scalable distributed-memory multiprocessors. In this paper, a mixed computational model is presented for GA-based structural optimization of large space structures on massively parallel supercomputers. Parallelism is exploited at both coarse-grained design optimization level in genetic search using the multiple-instruction–multiple-data model of computing and fine-grained fitness function evaluation level using the single-instruction–multiple-data model of computing. The latter model involves the development of a data-parallel iterative preconditioner-conjugate-gradient algorithm for the solution of the resulting system of linear equations. The model has been implemented on Connection Machine CM-5 and applied to optimization of large space steel structures subjected to the constraints of the American Institute of Steel Construction's allowable stress design specifications. The model and concurrent algorithm developed in this research is highly scalable. A peak performance of 2.4 giga–floating-point operations per second is achieved using 512 processors for a structure consisting of 4,016 elements.
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Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 121 • Issue 11 • November 1995
Pages: 1588 - 1597
Copyright
Copyright © 1995 American Society of Civil Engineers.
History
Published online: Nov 1, 1995
Published in print: Nov 1995
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