【摘 要】
：
Topology optimization problems require the repeated solution finite elements problems, for a large-scale problem the solution time is typically very high.Driven by the insatiable market demand for rea
【机 构】
：
State Key laboratory of Advanced Design and Manufacture for Vehicle Body(Hunan University), 410082,
【出 处】
：
7th China-Japan-Korea Joint Symposium on Optimization of Str
论文部分内容阅读
Topology optimization problems require the repeated solution finite elements problems, for a large-scale problem the solution time is typically very high.Driven by the insatiable market demand for real-time, high-definition 3D graphics, the programmable Graphic Processor Unit (GPU) has evolved into a highly parallel, multithreads, manycore processor with tremendous computational horsepower.Nowadays, it has gained more and more attention as a kind of general parallel processor, followed by various general purpose GPU computing technologies represented by NVIDIA CUDA.In this paper, a parallel topology optimization method is proposed to deal with large-scale structural design problems based on GPU.To minimize the data transfer between host and device, the entire parallel process of analysis and topology optimization has been implemented on GPU.The parallel algorithms presented here are based on the solid isotropic material with Bi-directional Evolutionary Structural Optimization (BESO) formulation of the topology optimization problem and used a soft-kill method.Considering the characteristic of GPU computation, the conjugate gradient iterative solver with a nodal based visual finite element assembly scheme is introduced for the finite method problem.This strategy can reduce the GPU memory usage during the optimization, and it can easily be made in parallel by mapping one node to one GPU thread.The programming details to realize the parallel optimization on GPU is presented in this paper.Several high API offered by NVIDIA CUDA is adopted to reduce the parallel programming difficulty.To obtain higher computational efficiency, the original serial algorithm is optimized.Finally, several numerical examples are considered, more than 100 times speedup can be obtained in handling large-scale design problems on a personal computing with a NVIDIA GTX 580 GPU.It shows the scalability and effectiveness of the present parallel approach.
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