Data from: Accelerating adaptive IDW Interpolation algorithm on a GPU

This paper focuses on designing and implementing parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithms by utilizing the Graphics Processing Unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to data points’ spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e., the naive version without profiting from shared memory and the tiled version taking advantage of shared memory. We also implement the naive version and the tiled version using two data layouts, Structure of Arrays (SoA) and Array of aligned Structures (AoaS), on both single and double precision. We then evaluate the performance of the parallel AIDW by comparing it with its corresponding serial algorithm on three different machines featured with the GPUs GT730M, M5000, and K40c. The experimental results indicate that: (1) there is no significant difference in the computational efficiency when different data layouts are employed; (2) the tiled version is always slightly faster than the naive version; and (3) on single precision the achieved speedup can be up to 763 (on the GPU M5000), while on double precision the obtained highest speedup is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm is publicly available.