面向多尺度拓扑优化的渐进均匀化GPU并行算法研究

Efficient GPU parallel strategy for multi-scale topology optimization via asymptotic homogenization

针对多尺度结构拓扑设计计算效率低等问题,提出了一种基于水平集渐进均匀化的多尺度拓扑优化并行算法。基于通用图形处理器(graphics processing unit,GPU),通过水平集初始化、大型稀疏刚度矩阵方程求解以及本构矩阵并行计算,可大幅提升渐进均匀化算法的效率。实验结果表明,当三维晶胞单元网格细化至分辨率为10万时,多尺度结构拓扑优化GPU并行算法较CPU串行算法快数十倍。

In response to the low computational efficiency in the context of multi-scale structural topology design,an efficient asymptotic homogenization GPU parallel strategy is presented.The strategy leverages the graphics processing unit(GPU)and investigates parallel strategies for level set initialization,large sparse stiffness matrix equations solving and constitutive properties computing.Experimental results demonstrate that the computing efficiency of the asymptotic homogenization can be greatly improved by adopting the parallel strategies,in particular,when refining a three-dimensional unit cell grid to a resolution of 100000,the GPU parallel strategy achieves a speedup of two orders of magnitude compared to the CPU serial.