Contribution to a conference proceedings

FZJ-2019-06503

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Scalable Workflows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture

Erlingsson, E. ; Cavallaro, G. (Corresponding author)FZJ* ; Neukirchen, H. ; Riedel, M.FZJ*

2019
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/23604  doi:10.1109/IGARSS.2019.8898487

Abstract: 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.

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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)

Appears in the scientific report 2019

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Database coverage:

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