EPJ Web of Conferences
Volume 119, 2016The 27th International Laser Radar Conference (ILRC 27)
|Number of page(s)||4|
|Section||Poster Session (Advances in Lidar Technologies and Techniques III)|
|Published online||07 June 2016|
Massively Parallel Computation of Soil Surface Roughness Parameters on A Fermi GPU
1 Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
2 State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
3 Space Science and Engineering Center, University of Wisconsin, Madison, WI, 53706, USA
* Email: email@example.com
Published online: 7 June 2016
Surface roughness is description of the surface micro topography of randomness or irregular. The standard deviation of surface height and the surface correlation length describe the statistical variation for the random component of a surface height relative to a reference surface. When the number of data points is large, calculation of surface roughness parameters is time-consuming. With the advent of Graphics Processing Unit (GPU) architectures, inherently parallel problem can be effectively solved using GPUs. In this paper we propose a GPU-based massively parallel computing method for 2D bare soil surface roughness estimation. This method was applied to the data collected by the surface roughness tester based on the laser triangulation principle during the field experiment in April 2012. The total number of data points was 52,040. It took 47 seconds on a Fermi GTX 590 GPU whereas its serial CPU version took 5422 seconds, leading to a significant 115x speedup.
Key words: Soil surface roughness / Correlation length / Graphics Processing Unit (GPU) / High Performance Computing (HPC)
© Owned by the authors, published by EDP Sciences, 2016
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