ABSTRACTQuantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth. However, the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures. In this article, taking the aerosol optical depth (AOD) retrieval as a study case, we exploit parallel computing methods for high efficient geophysical parameter retrieval. We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. According to their individual potential for parallelization, several procedures were adapted and implemented for a successful parallel execution on multi-core processors and Graphics Processing Units (GPUs). The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU. To specifically address the time-consuming model retrieval part, hybrid parallel...
['Jia Liu', 'Dustin Feld', 'Yong Xue', 'Jochen Garcke', 'Thomas Soddemann', 'Peiyuan Pan']
An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data