@incollection{Feld.Garcke.Liu.ea:2017,
abstract = {The Aerosol Optical Depth (AOD) is a significant optical property of aerosols and is applied to the atmospheric correction of remotely sensed surface features as well as for monitoring volcanic eruptions, forest fires, and air quality in general, as well as gathering data for climate predictions on the basis of observations from satellites. We have developed an AOD retrieval workflow for processing satellite data not only with ordinary CPUs but also with parallel processors and GPU accelerators in a distributed hardware environment. This workflow includes pre-processing procedures which are followed by the runtime dominating main retrieval method.},
address = {Cham},
author = {Feld, Dustin and Garcke, Jochen and Liu, Jia and Schricker, Eric and Soddemann, Thomas and Xue, Yong},
booktitle = {Scientific Computing and Algorithms in Industrial Simulations: Projects and Products of Fraunhofer SCAI},
doi = {10.1007/978-3-319-62458-7_17},
editor = {Griebel, Michael and Sch{\"{u}}ller, Anton and Schweitzer, Marc Alexander},
isbn = {978-3-319-62458-7},
mendeley-groups = {Remote Sensing Liu},
pages = {341--358},
  annote	= {other},
publisher = {Springer International Publishing},
title = {{Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures}},
url = {https://doi.org/10.1007/978-3-319-62458-7{\_}17},
  pdf		= {http://garcke.ins.uni-bonn.de/research/pub/GPU-energy-SCAI.pdf},
year = {2017}
}