000851724 001__ 851724 000851724 005__ 20210129235007.0 000851724 0247_ $$2doi$$a10.1109/IPDPSW.2018.00019 000851724 0247_ $$2Handle$$a2128/19669 000851724 0247_ $$2WOS$$aWOS:000541051600007 000851724 037__ $$aFZJ-2018-05256 000851724 1001_ $$0P:(DE-Juel1)138688$$aKreuzer, Anke$$b0$$ufzj 000851724 1112_ $$a2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)$$cVancouver$$d2018-05-21 - 2018-05-25$$gIPDPS$$wCanada 000851724 245__ $$aApplication Performance on a Cluster-Booster System 000851724 260__ $$bIEEE$$c2018 000851724 300__ $$a69 - 78 000851724 3367_ $$2ORCID$$aCONFERENCE_PAPER 000851724 3367_ $$033$$2EndNote$$aConference Paper 000851724 3367_ $$2BibTeX$$aINPROCEEDINGS 000851724 3367_ $$2DRIVER$$aconferenceObject 000851724 3367_ $$2DataCite$$aOutput Types/Conference Paper 000851724 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1536673727_25366 000851724 520__ $$aThe DEEP projects have developed a variety of hardware and software technologies aiming at improving the efficiency and usability of next generation high-performance computers. They evolve around an innovative concept for heterogeneous systems: the Cluster-Booster architecture. In it, a general purpose cluster is tightly coupled to a many-core system (the Booster). This modular way of integrating heterogeneous components enables applications to freely choose the kind of computing resources on which it runs most efficiently. Codes might even be partitioned to map specific requirements of codeparts onto the best suited hardware. This paper presents for the first time measurements done by a real world scientific application demonstrating the performance gain achieved with this kind of code-partition approach. 000851724 536__ $$0G:(DE-HGF)POF3-513$$a513 - Supercomputer Facility (POF3-513)$$cPOF3-513$$fPOF III$$x0 000851724 536__ $$0G:(EU-Grant)287530$$aDEEP - Dynamical Exascale Entry Platform (287530)$$c287530$$fFP7-ICT-2011-7$$x1 000851724 536__ $$0G:(EU-Grant)610476$$aDEEP-ER - DEEP Extended Reach (610476)$$c610476$$fFP7-ICT-2013-10$$x2 000851724 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x3 000851724 588__ $$aDataset connected to CrossRef Conference 000851724 7001_ $$0P:(DE-Juel1)132090$$aEicker, Norbert$$b1$$ufzj 000851724 7001_ $$0P:(DE-HGF)0$$aAmaya, Jorge$$b2 000851724 7001_ $$0P:(DE-Juel1)142361$$aSuarez, Estela$$b3$$eCorresponding author$$ufzj 000851724 770__ $$z978-1-5386-5555-9 000851724 773__ $$a10.1109/IPDPSW.2018.00019$$p69 - 78 000851724 8564_ $$uhttps://juser.fz-juelich.de/record/851724/files/Kreuzer_HCW2018.pdf$$yOpenAccess 000851724 8564_ $$uhttps://juser.fz-juelich.de/record/851724/files/Kreuzer_HCW2018.pdf?subformat=pdfa$$xpdfa$$yOpenAccess 000851724 909CO $$ooai:juser.fz-juelich.de:851724$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access$$popenaire 000851724 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138688$$aForschungszentrum Jülich$$b0$$kFZJ 000851724 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132090$$aForschungszentrum Jülich$$b1$$kFZJ 000851724 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)142361$$aForschungszentrum Jülich$$b3$$kFZJ 000851724 9131_ $$0G:(DE-HGF)POF3-513$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vSupercomputer Facility$$x0 000851724 9141_ $$y2018 000851724 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000851724 920__ $$lyes 000851724 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 000851724 980__ $$acontrib 000851724 980__ $$aVDB 000851724 980__ $$aUNRESTRICTED 000851724 980__ $$aI:(DE-Juel1)JSC-20090406 000851724 9801_ $$aFullTexts