This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/21819 in citations.
Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
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 fa...
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Personal Name(s): | Cavallaro, Gabriele |
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Kozlov, Valentin / Götz, Markus / Riedel, Morris | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: |
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 |
Imprint: |
Luxembourg
Publications Office of the European Union
2019
|
Physical Description: |
177-180 |
Conference: | Conference on Big Data from Space (BiDS'19), Munich (Germany), 2019-02-19 - 2019-02-21 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud DEEP - Extreme Scale Technologies Data-Intensive Science and Federated Computing |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
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. |