  This paper presents a GPU-accelerated implementation of two-dimensional Smart
Laplacian smoothing. This implementation is developed under the guideline of
our paradigm for accelerating Laplacianbased mesh smoothing [13]. Two types of
commonly used data layouts, Array-of-Structures (AoS) and Structure-of-Arrays
(SoA) are used to represent triangular meshes in our implementation. Two
iteration forms that have different choices of the swapping of intermediate
data 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 the
speedups of up to 44x on the GPU GT640; (2) the data layout AoS can always
obtain better efficiency than the SoA layout; (3) the form that needs to swap
intermediate nodal coordinates is always slower than the one that does not swap
data; (4) the version of our implementation with the use of the feature CDP is
slightly faster than the version where the CDP is not adopted.
