000820613 001__ 820613
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000820613 020__ $$a978-3-319-40526-1
000820613 020__ $$a978-3-319-40528-5 (electronic)
000820613 0247_ $$2doi$$a10.1007/978-3-319-40528-5_21
000820613 0247_ $$2WOS$$aWOS:000411331500021
000820613 037__ $$aFZJ-2016-05885
000820613 041__ $$aEnglish
000820613 1001_ $$0P:(DE-HGF)0$$aVogel, Andreas$$b0$$eCorresponding author
000820613 245__ $$aAutomated Performance Modeling of the UG4 Simulation Framework
000820613 260__ $$aCham, Switzerland$$bSpringer International Publishing$$c2016
000820613 29510 $$aSoftware for Exascale Computing - SPPEXA 2013-2015 / Bungartz, Hans-Joachim (Editor)   ; Chapter 21 ; ISBN: 978-3-319-40526-1=978-3-319-40528-5
000820613 300__ $$a467 - 481
000820613 3367_ $$2ORCID$$aBOOK_CHAPTER
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000820613 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$bcontb$$mcontb$$s1478618440_18228
000820613 4900_ $$aLecture Notes in Computational Science and Engineering$$v113
000820613 520__ $$aMany scientific research questions such as the drug diffusion through the upper part of the human skin are formulated in terms of partial differential equations and their solution is numerically addressed using grid based finite element methods. For detailed and more realistic physical models this computational task becomes challenging and thus complex numerical codes with good scaling properties up to millions of computing cores are required. Employing empirical tests we presented very good scaling properties for the geometric multigrid solver in Reiter et al. (Comput Vis Sci 16(4):151–164, 2013) using the UG4 framework that is used to address such problems. In order to further validate the scalability of the code we applied automated performance modeling to UG4 simulations and presented how performance bottlenecks can be detected and resolved in Vogel et al. (10,000 performance models per minute—scalability of the UG4 simulation framework. In: Träff JL, Hunold S, Versaci F (eds) Euro-Par 2015: Parallel processing, theoretical computer science and general issues, vol 9233. Springer, Springer, Heidelberg, pp 519–531, 2015). In this paper we provide an overview on the obtained results, present a more detailed analysis via performance models for the components of the geometric multigrid solver and comment on how the performance models coincide with our expectations.
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000820613 588__ $$aDataset connected to CrossRef Book Series
000820613 7001_ $$0P:(DE-HGF)0$$aCalotoiu, Alexandru$$b1
000820613 7001_ $$0P:(DE-HGF)0$$aNägel, Arne$$b2
000820613 7001_ $$0P:(DE-HGF)0$$aReiter, Sebastian$$b3
000820613 7001_ $$0P:(DE-Juel1)140202$$aStrube, Alexandre$$b4$$ufzj
000820613 7001_ $$0P:(DE-HGF)0$$aWittum, Gabriel$$b5
000820613 7001_ $$0P:(DE-HGF)0$$aWolf, Felix$$b6
000820613 773__ $$a10.1007/978-3-319-40528-5_21
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000820613 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)140202$$aForschungszentrum Jülich$$b4$$kFZJ
000820613 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000820613 9141_ $$y2016
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000820613 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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