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This paper presents an efficient parallel Adaptive Inverse Distance Weighting(AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). Thepresented algorithm is an improvement of our previous GPU-accelerated AIDWalgorithm by adopting fast k-Nearest Neighbors (kNN) search. In AIDW, it needsto find several nearest neighboring data points for each interpolated point toadaptively determine the power parameter; and then the desired prediction valueof the interpolated point is obtained by weighted interpolating using the powerparameter. In this work, we develop a fast kNN search approach based on thespace-partitioning data structure, even grid, to improve the previousGPU-accelerated AIDW algorithm. The improved algorithm is composed of thestages of kNN search and weighted interpolating. To evaluate the performance ofthe improved algorithm, we perform five groups of experimental tests.Experimental results show that: (1) the improved algorithm can achieve aspeedup of up to 1017 over the corresponding serial algorithm; (2) the improvedalgorithm is at least two times faster than our previous GPU-accelerated AIDWalgorithm; and (3) the utilization of fast kNN search can significantly improvethe computational efficiency of the entire GPU-accelerated AIDW algorithm.

Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search