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| Hauptseite > Publikationsdatenbank > Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems > print |
| Hauptseite > Publikationsdatenbank > Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems > print |
| 001 | 861288 | ||
| 005 | 20210130000752.0 | ||
| 024 | 7 | _ | |a 2128/21807 |2 Handle |
| 037 | _ | _ | |a FZJ-2019-01785 |
| 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 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1552413549_13872 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Multi-GPU systems are in continuous development todeal with the challenges of intensive computational big dataproblems. On the one hand, parallel architectures provide atremendous computation capacity and outstanding scalability.On the other hand, the production path in multi-user environmentsfaces several roadblocks since they do not grant rootprivileges to the users. Containers provide flexible strategiesfor packing, deploying and running isolated applicationprocesses within multi-user systems and enable scientific reproducibility.This paper describes the usage and advantagesthat the uDocker container tool offers for the developmentof deep learning models in the described context. The experimentalresults show that uDocker is more transparent todeploy for less tech-savvy researchers and allows the applicationto achieve processing time with negligible overheadcompared 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 | _ | |u https://juser.fz-juelich.de/record/861288/files/BiDS_2019_poster.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/861288/files/BiDS_2019_poster.pdf?subformat=pdfa |x pdfa |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:861288 |p openaire |p open_access |p VDB |p driver |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |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 poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
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