Using compute unified device architecture-enabled graphic processing unit to accelerate fast Fourier transform-based regression Kriging interpolation on a MODIS land surface temperature image

Jianhui Xu; Hongda Hu; Hong Shu; Zhiyong Hu

doi:10.1117/1.JRS.10.026036

23 June 2016 Using compute unified device architecture-enabled graphic processing unit to accelerate fast Fourier transform-based regression Kriging interpolation on a MODIS land surface temperature image

Jianhui Xu, Hongda Hu, Hong Shu, Zhiyong Hu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 2, 026036 (June 2016). https://doi.org/10.1117/1.JRS.10.026036

Abstract

Kriging interpolation provides the best linear unbiased estimation for unobserved locations, but its heavy computation limits the manageable problem size in practice. To address this issue, an efficient interpolation procedure incorporating the fast Fourier transform (FFT) was developed. Extending this efficient approach, we propose an FFT-based parallel algorithm to accelerate regression Kriging interpolation on an NVIDIA^® compute unified device architecture (CUDA)-enabled graphic processing unit (GPU). A high-performance cuFFT library in the CUDA toolkit was introduced to execute computation-intensive FFTs on the GPU, and three time-consuming processes were redesigned as kernel functions and executed on the CUDA cores. A MODIS land surface temperature 8-day image tile at a resolution of 1 km was resampled to create experimental datasets at eight different output resolutions. These datasets were used as the interpolation grids with different sizes in a comparative experiment. Experimental results show that speedup of the FFT-based regression Kriging interpolation accelerated by GPU can exceed 1000 when processing datasets with large grid sizes, as compared to the traditional Kriging interpolation running on the CPU. These results demonstrate that the combination of FFT methods and GPU-based parallel computing techniques greatly improves the computational performance without loss of precision.

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Jianhui Xu, Hongda Hu, Hong Shu, and Zhiyong Hu "Using compute unified device architecture-enabled graphic processing unit to accelerate fast Fourier transform-based regression Kriging interpolation on a MODIS land surface temperature image," Journal of Applied Remote Sensing 10(2), 026036 (23 June 2016). https://doi.org/10.1117/1.JRS.10.026036

Published: 23 June 2016

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