二维的Smart Laplacian光滑在GPU上的加速
On the Accelerating of Two-dimensional Smart Laplacian Smoothing on the GPU
摘要：
本文提出了一种GPU加速实现的二维的【形】智能拉普拉斯平滑。该实现是在我们的范例的指导下开发的，用于加速基于Laplacian的网格平滑[13]。两种类型的常用数据布局，阵列的结构（AoS）和阵列的结构（SoA）被用来表示三角形网格在我们的实现。采用了两种迭代形式，对中间数据的交换有不同的选择。此外，CUDA动态并行（CDP）功能，实现智能拉普拉斯平滑嵌套并行。实验结果表明：（1）在GPU GT 640上，我们的实现可以获得高达44倍的加速比;（2）数据布局AoS总是可以获得比SoA布局更好的效率;（3）需要交换中间节点坐标的表单总是比不交换数据的表单慢;（4）使用CDP功能的实现版本比不采用CDP的版本稍快。
英文摘要：
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.
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