Abstract
Topology optimization has been used to generate light-weight structures. However, the main issue with its implementation is a large computation time because it involves finite element (FE) simulations coupled with optimization. From the last few years, the graphics processing unit (GPU) has been used for reducing computation time by performing the computation in parallel and, thus, becomes an active research area. In this paper, a fine-grained node-by-node GPU computing strategy is proposed for the matrix-free conjugate gradient FE solver. The strategy is implemented with a customized nodal connectivity strategy. The performance of the proposed implementation is analyzed using three different mesh sizes on an elasticity problem. Results demonstrate \(3\times\) of GPU speedup over a standard CPU implementation.
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Ratnakar, S.K., Sanfui, S., Sharma, D. (2021). GPU-Based Topology Optimization Using Matrix-Free Conjugate Gradient Finite Element Solver with Customized Nodal Connectivity Storage. In: Kumar, N., Tibor, S., Sindhwani, R., Lee, J., Srivastava, P. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9956-9_9
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DOI: https://doi.org/10.1007/978-981-15-9956-9_9
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