Contribution to a conference proceedings/Contribution to a book

FZJ-2019-01799

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems

Cavallaro, G.FZJ* ; Kozlov, V. ; Götz, M. ; Riedel, M.FZJ*

2019
Publications Office of the European Union Luxembourg

Please use a persistent id in citations: http://hdl.handle.net/2128/21819

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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)
2. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
3. DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (777435) (777435)

Appears in the scientific report 2019

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Database coverage:

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