Paper Info
Title
Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search.
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. The experimental results indicate: (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.
Year
DOI
Venue
2016
10.1186/s40064-016-3035-2
SpringerPlus
Keywords
Field
DocType
Graphics Processing Unit (GPU),Inverse Distance Weighting (IDW),Spatial interpolation,k-nearest neighbors (kNN)
Data point,Ramer–Douglas–Peucker algorithm,Multivariate interpolation,Inverse distance weighting,Computer science,Interpolation,Algorithm,FSA-Red Algorithm,Graphics processing unit,Speedup
Journal
Volume
Issue
ISSN
abs/1601.05904
1
2193-1801
Citations 
PageRank 
References 
6
0.59
28
Authors
3
Name
Order
Citations
PageRank
Gang Mei1194.56
Nengxiong Xu2226.00
Liangliang Xu360.59