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| 001 | 867901 | ||
| 005 | 20230918092258.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS.2019.8898487 |2 doi |
| 024 | 7 | _ | |a 2128/23604 |2 Handle |
| 024 | 7 | _ | |a WOS:000519270605170 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-06503 |
| 100 | 1 | _ | |a Erlingsson, Ernir |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium |c Yokohama |d 2019-07-28 - 2019-08-02 |w Japan |
| 245 | _ | _ | |a Scalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture |
| 260 | _ | _ | |c 2019 |b IEEE |
| 300 | _ | _ | |a 5905-5908 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1576509821_476 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 0 |
| 536 | _ | _ | |a DEEP-EST - DEEP - Extreme Scale Technologies (754304) |0 G:(EU-Grant)754304 |c 754304 |f H2020-FETHPC-2016 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Neukirchen, Helmut |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 3 |u fzj |
| 773 | _ | _ | |a 10.1109/IGARSS.2019.8898487 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/867901/files/Erlingsson_IGARSS2019.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/867901/files/Erlingsson_IGARSS2019.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:867901 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)171343 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)132239 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |2 G:(DE-HGF)POF3-500 |v Data-Intensive Science and Federated Computing |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
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