000857816 001__ 857816
000857816 005__ 20210129235719.0
000857816 0247_ $$2doi$$a10.1109/IGARSS.2018.8517378
000857816 0247_ $$2Handle$$a2128/20220
000857816 037__ $$aFZJ-2018-06783
000857816 1001_ $$0P:(DE-HGF)0$$aErlingsson, Ernir$$b0
000857816 1112_ $$aIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium$$cValencia$$d2018-07-22 - 2018-07-27$$wSpain
000857816 245__ $$aScaling Support Vector Machines Towards Exascale Computing for Classification of Large-Scale High-Resolution Remote Sensing Images
000857816 260__ $$bIEEE$$c2018
000857816 300__ $$a1792-1795
000857816 3367_ $$2ORCID$$aCONFERENCE_PAPER
000857816 3367_ $$033$$2EndNote$$aConference Paper
000857816 3367_ $$2BibTeX$$aINPROCEEDINGS
000857816 3367_ $$2DRIVER$$aconferenceObject
000857816 3367_ $$2DataCite$$aOutput Types/Conference Paper
000857816 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1543482108_22948
000857816 520__ $$aProgress 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.
000857816 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000857816 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x1
000857816 588__ $$aDataset connected to CrossRef Conference
000857816 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$ufzj
000857816 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b2$$ufzj
000857816 7001_ $$0P:(DE-HGF)0$$aNeukirchen, Helmut$$b3
000857816 773__ $$a10.1109/IGARSS.2018.8517378
000857816 8564_ $$uhttps://juser.fz-juelich.de/record/857816/files/Erlingsson_IGARSS_2018.pdf$$yOpenAccess
000857816 8564_ $$uhttps://juser.fz-juelich.de/record/857816/files/Erlingsson_IGARSS_2018.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000857816 909CO $$ooai:juser.fz-juelich.de:857816$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access$$popenaire
000857816 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171343$$aForschungszentrum Jülich$$b1$$kFZJ
000857816 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132239$$aForschungszentrum Jülich$$b2$$kFZJ
000857816 9131_ $$0G:(DE-HGF)POF3-512$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vData-Intensive Science and Federated Computing$$x0
000857816 9141_ $$y2018
000857816 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000857816 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000857816 980__ $$acontrib
000857816 980__ $$aVDB
000857816 980__ $$aUNRESTRICTED
000857816 980__ $$aI:(DE-Juel1)JSC-20090406
000857816 9801_ $$aFullTexts