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Computational Science, Engineering & Technology Series
ISSN 1759-3158 CSETS: 40
ADVANCES IN PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: P. Iványi, B.H.V. Topping and G. Várady
Chapter 3
Massively Parallel Evolutionary Structural Optimization for High Resolution Architecture Design J. Martínez-Frutos and D. Herrero-Pérez
Computational Mechanics & Scientific Computing Group, Department of Structures and Construction, Technical University of Cartagena, Murcia, Spain J. Martínez-Frutos, D. Herrero-Pérez , "Massively Parallel Evolutionary
Structural Optimization for High
Resolution Architecture Design", in P. Iványi, B.H.V. Topping and G. Várady, (Editors), "Advances in
Parallel, Distributed, Grid
and
Cloud Computing
for
Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 3, pp 29-49, 2017. doi:10.4203/csets.40.3
Keywords: GPU computing, evolutionary structural optimization, sustainable architecture,
multilevel preconditioner, large-scale..
Abstract
This paper shows how the proper use of massively parallel architectures can increase
significantly the tractable resolution of topology optimization problems in the field of
architecture and urban design. Evolutionary topology optimization techniques are redefining
architectural practice providing structurally sound and aesthetically pleasing
architectural designs, which commonly mimic nature’s own evolutionary optimization
process. Though these techniques provide architects with a powerful tool to integrate
function and form in a synergistic way, the resolution of the models to obtain
proper designs may be challenging both in computation and memory consumption
terms. This work aims to alleviate these constraints proposing a well-suited strategy
for Graphics Processing Unit (GPU) computing. Such a proposal makes use of fine grained
assembly-free methods along with multilevel parallelizable preconditioner,
which notably increase the tractable resolution of the models. The stages of the evolutionary
topology optimization pipeline using GPU are compared to the classically
used CPU implementation achieving significant speedups. The proposal is evaluated
in high resolution real-world architecture and urban designs.
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