This title appears in the Scientific Report :
2019
Please use the identifier:
http://dx.doi.org/10.1109/IGARSS.2019.8898487 in citations.
Please use the identifier: http://hdl.handle.net/2128/23604 in citations.
Scalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture
Scalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture
The implementation of efficient remote sensing workflows isessential to improve the access to and analysis of the vastamount of sensed data and to provide decision-makers withclear, timely, and useful information. The Dynamical Exascale Entry Platform (DEEP) is an European pre-exascaleplatform that...
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Personal Name(s): | Erlingsson, Ernir |
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Cavallaro, Gabriele (Corresponding author) / Neukirchen, Helmut / Riedel, Morris | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
IEEE
2019
|
Physical Description: |
5905-5908 |
DOI: |
10.1109/IGARSS.2019.8898487 |
Conference: | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama (Japan), 2019-07-28 - 2019-08-02 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
DEEP - Extreme Scale Technologies Data-Intensive Science and Federated Computing |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/23604 in citations.
The implementation of efficient remote sensing workflows isessential to improve the access to and analysis of the vastamount of sensed data and to provide decision-makers withclear, timely, and useful information. The Dynamical Exascale Entry Platform (DEEP) is an European pre-exascaleplatform that incorporates heterogeneous High-PerformanceComputing (HPC) systems, i.e., hardware modules which include specialised accelerators. This paper demonstrates thepotential of such diverse modules for the deployment of remote sensing data workflows that include diverse processing tasks. Particular focus is put on pipelines which can usethe Network Attached Memory (NAM), which is a novel supercomputer module that allows near processing and/or fastshared storage of big remote sensing datasets. |