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This paper presents a GPU-accelerated implementation of two-dimensional SmartLaplacian smoothing. This implementation is developed under the guideline ofour paradigm for accelerating Laplacianbased mesh smoothing [13]. Two types ofcommonly used data layouts, Array-of-Structures (AoS) and Structure-of-Arrays(SoA) are used to represent triangular meshes in our implementation. Twoiteration forms that have different choices of the swapping of intermediatedata are also adopted. Furthermore, the feature CUDA Dynamic Parallelism (CDP)is employed to realize the nested parallelization in Smart Laplacian smoothing.Experimental results demonstrate that: (1) our implementation can achieve thespeedups of up to 44x on the GPU GT640; (2) the data layout AoS can alwaysobtain better efficiency than the SoA layout; (3) the form that needs to swapintermediate nodal coordinates is always slower than the one that does not swapdata; (4) the version of our implementation with the use of the feature CDP isslightly faster than the version where the CDP is not adopted.

On the Accelerating of Two-dimensional Smart Laplacian Smoothing on the GPU