Contribution to a conference proceedings

FZJ-2018-06783

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Scaling Support Vector Machines Towards Exascale Computing for Classification of Large-Scale High-Resolution Remote Sensing Images

Erlingsson, E. ; Cavallaro, G.FZJ* ; Riedel, M.FZJ* ; Neukirchen, H.

2018
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/20220  doi:10.1109/IGARSS.2018.8517378

Abstract: Progress in sensor technology leads to an ever-increasing amount of remote sensing data which needs to be classified in order to extract information. This big amount of data requires parallel processing by running parallel implementations of classification algorithms, such as Support Vector Machines (SVMs), on High-Performance Computing (HPC) clusters. Tomorrow's supercomputers will be able to provide exascale computing performance by using specialised hardware accelerators. However, existing software processing chains need to be adapted to make use of the best fitting accelerators. To address this problem, a mapping of an SVM remote sensing classification chain to the Dynamical Exascale Entry Platform (DEEP), a European pre-exascale platform, is presented. It will allow to scale SVM-based classifications on tomorrow's hardware towards exascale performance.

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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 2018

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Database coverage:

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