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@INPROCEEDINGS{Erlingsson:867901,
author = {Erlingsson, Ernir and Cavallaro, Gabriele and Neukirchen,
Helmut and Riedel, Morris},
title = {{S}calable {W}orkflows for {R}emote {S}ensing {D}ata
{P}rocessing with the {D}eep-{E}st {M}odular
{S}upercomputing {A}rchitecture},
publisher = {IEEE},
reportid = {FZJ-2019-06503},
pages = {5905-5908},
year = {2019},
abstract = {The 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.},
month = {Jul},
date = {2019-07-28},
organization = {IGARSS 2019 - 2019 IEEE International
Geoscience and Remote Sensing
Symposium, Yokohama (Japan), 28 Jul
2019 - 2 Aug 2019},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {512 - Data-Intensive Science and Federated Computing
(POF3-512) / DEEP-EST - DEEP - Extreme Scale Technologies
(754304)},
pid = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304},
typ = {PUB:(DE-HGF)8},
UT = {WOS:000519270605170},
doi = {10.1109/IGARSS.2019.8898487},
url = {https://juser.fz-juelich.de/record/867901},
}