Issue |
EPJ Web of Conferences
Volume 119, 2016
The 27th International Laser Radar Conference (ILRC 27)
|
|
---|---|---|
Article Number | 25018 | |
Number of page(s) | 4 | |
Section | Poster Session (Advances in Lidar Technologies and Techniques III) | |
DOI | https://doi.org/10.1051/epjconf/201611925018 | |
Published online | 07 June 2016 |
https://doi.org/10.1051/epjconf/201611925018
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: lixiaojie@iga.ac.cn
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.