Hauptseite > Publikationsdatenbank > Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems > print

001     861304
005     20210130000756.0
024 7 _ |a 2128/21819
|2 Handle
037 _ _ |a FZJ-2019-01799
100 1 _ |a Cavallaro, Gabriele
|0 P:(DE-Juel1)171343
|b 0
111 2 _ |a Conference on Big Data from Space (BiDS'19)
|c Munich
|d 2019-02-19 - 2019-02-21
|w Germany
245 _ _ |a Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
260 _ _ |a Luxembourg
|c 2019
|b Publications Office of the European Union
295 1 0 |a Proc. of the 2019 conference on Big Data from Space (BiDS’2019), EUR 29660 EN, ISBN 978-92-76-00034-1, doi:10.2760/848593
300 _ _ |a 177-180
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
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|s 1552379280_32480
|2 PUB:(DE-HGF)
336 7 _ |a Contribution to a book
|0 PUB:(DE-HGF)7
|2 PUB:(DE-HGF)
|m contb
520 _ _ |a Multi-GPU systems are in continuous development to deal with the challenges of intensive computational big data problems. On the one hand, parallel architectures provide a tremendous computation capacity and outstanding scalability. On the other hand, the production path in multi-user environment faces several roadblocks since they do not grant root privileges to the users. Containers provide flexible strategies for packing, deploying and running isolated application processes within multi-user systems and enable scientific reproducibility. This paper describes the usage and advantages that the uDocker container tool offers for the development of deep learning models in the described context. The experimental results show that uDocker is more transparent to deploy for less tech-savvy researchers and allows the application to achieve processing time with negligible overhead compared to an uncontainerized environment.
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
536 _ _ |a DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (777435)
|0 G:(EU-Grant)777435
|c 777435
|f H2020-EINFRA-2017
|x 2
700 1 _ |a Kozlov, Valentin
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Götz, Markus
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Riedel, Morris
|0 P:(DE-Juel1)132239
|b 3
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/861304/files/BiDS_2019_paper.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/861304/files/BiDS_2019_paper.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:861304
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910 1 _ |a Forschungszentrum Jülich
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|6 P:(DE-Juel1)171343
910 1 _ |a Forschungszentrum Jülich
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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
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980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts

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