% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Cavallaro:861304,
      author       = {Cavallaro, Gabriele and Kozlov, Valentin and Götz, Markus
                      and Riedel, Morris},
      title        = {{R}emote {S}ensing {D}ata {A}nalytics with the {U}docker
                      {C}ontainer {T}ool using {M}ulti-{GPU} {D}eep {L}earning
                      {S}ystems},
      address      = {Luxembourg},
      publisher    = {Publications Office of the European Union},
      reportid     = {FZJ-2019-01799},
      pages        = {177-180},
      year         = {2019},
      comment      = {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},
      booktitle     = {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},
      abstract     = {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.},
      month         = {Feb},
      date          = {2019-02-19},
      organization  = {Conference on Big Data from Space
                       (BiDS'19), Munich (Germany), 19 Feb
                       2019 - 21 Feb 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) / DEEP-HybridDataCloud - Designing and Enabling
                      E-infrastructures for intensive Processing in a Hybrid
                      DataCloud (777435)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304 /
                      G:(EU-Grant)777435},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/861304},
}