Quantitative 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.I...Quantitative 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 multicore 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 patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations.It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)under Grant 41271371 and Grant 41471306the Major International Cooperation and Exchange Project of NSFC under Grant 41120114001+2 种基金the Institute of Remote Sensing and Digital Earth Institute,Chinese Academy of Sciences(CAS-RADI)Innovation project under Grants Y3SG0300CXthe graduate foundation of CAS-RADI under Grant Y4ZZ06101Bthe Joint Doctoral Promotion Program hosted by the Fraunhofer Institute and Chinese Academy of Sciences.Many thanks are due to the Fraunhofer Institute for Algorithms and Scientific Computing SCAI for the multi-core and GPU platform used in this paper.
文摘Quantitative 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 multicore 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 patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations.It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.
