用快速kNN搜索改进GPU加速的自适应IDW插值算法
Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search
摘要：
提出了一种基于现代图形处理器（GPU）的高效并行自适应逆距离加权（AIDW）插值算法。该算法是我们以前的GPU加速AIDW算法的改进，采用快速k-近邻（kNN）搜索。在AIDW算法中，需要为每个插值点找到多个最近邻数据点，自适应地确定幂参数，然后利用幂参数对插值点进行加权插值，得到期望的插值点预测值。在这项工作中，我们开发了一种快速kNN搜索方法的基础上的空间划分的数据结构，甚至网格，以改善以前的GPU加速AIDW算法。改进算法由kNN搜索和加权插值两个阶段组成。为了评估改进算法的性能，我们进行了五组实验测试。实验结果表明：（1）改进后的算法与相应的串行算法相比，可以实现高达1017的加速比;（2）改进后的算法比我们以前的GPU加速AIDW算法至少快两倍;（3）快速kNN搜索的使用可以显著提高整个GPU加速AIDW算法的计算效率。
英文摘要：
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-Nearest Neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. Experimental results show that: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
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