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

TL;DR

This paper introduces a GPU-based adaptive IDW interpolation algorithm enhanced with a fast kNN search method, significantly boosting computational efficiency and outperforming previous implementations.

Contribution

The paper develops a fast kNN search approach using space-partitioning data structures to improve GPU-accelerated AIDW interpolation performance.

Findings

Achieves up to 1017x speedup over serial algorithms

At least twice as fast as previous GPU-accelerated AIDW

Fast kNN search significantly enhances computational efficiency

Abstract

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.