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000867901 1001_ $$0P:(DE-HGF)0$$aErlingsson, Ernir$$b0
000867901 1112_ $$aIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium$$cYokohama$$d2019-07-28 - 2019-08-02$$wJapan
000867901 245__ $$aScalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture
000867901 260__ $$bIEEE$$c2019
000867901 300__ $$a5905-5908
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000867901 520__ $$aThe implementation of efficient remote sensing workflows isessential to improve the access to and analysis of the vastamount of sensed data and to provide decision-makers withclear, timely, and useful information. The Dynamical Exascale Entry Platform (DEEP) is an European pre-exascaleplatform that incorporates heterogeneous High-PerformanceComputing (HPC) systems, i.e., hardware modules which include specialised accelerators. This paper demonstrates thepotential of such diverse modules for the deployment of remote sensing data workflows that include diverse processing tasks. Particular focus is put on pipelines which can usethe Network Attached Memory (NAM), which is a novel supercomputer module that allows near processing and/or fastshared storage of big remote sensing datasets.
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000867901 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x1
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000867901 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$eCorresponding author$$ufzj
000867901 7001_ $$0P:(DE-HGF)0$$aNeukirchen, Helmut$$b2
000867901 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b3$$ufzj
000867901 773__ $$a10.1109/IGARSS.2019.8898487
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