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Large-scale robust topology optimization using multi-GPU systems
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DOI：10.1016/j.cma.2016.08.016.png)
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Abstract
En 中文
Robust topology optimization of continuum structures is an intensive computational task due to the use of uncertainty propagation methods to estimate the statistical metrics within the topology optimization process. Such a computational problem is exacerbated for large finite element (FE) models in terms of memory consumption and processing time. For these reasons, the efficient resolution of robust topology optimization with large models remains an important computational challenge. This work aims to alleviate these computational constraints proposing a well-suited strategy for Graphics Processing Unit (GPU) computing. Such a proposal exploits the multilevel parallelism provided by multi-GPU systems for the parallel execution both within FE models and through uncertainty propagation methods. Task-level parallelism is used to concurrently evaluate the independent simulation models arising from a sparse grid stochastic collocation method. Data-level parallelism with different granularities is then exploited for the efficient resolution of each simulation model and the computation required by the topology optimization process. The resolution of the different calculations of robust topology optimization pipeline using multi-CPU systems are compared to the classically used multi-CPU implementation achieving significant speedups. (C) 2016 Elsevier B.V. All rights reserved,
Keywords:
Topology optimization
Robust design
Large-scale
Multi-GPU computing
Stochastic collocation
Random fields
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