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001     820613
005     20250314084115.0
020 _ _ |a 978-3-319-40526-1
020 _ _ |a 978-3-319-40528-5 (electronic)
024 7 _ |a 10.1007/978-3-319-40528-5_21
|2 doi
024 7 _ |a WOS:000411331500021
|2 WOS
037 _ _ |a FZJ-2016-05885
041 _ _ |a English
100 1 _ |a Vogel, Andreas
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Automated Performance Modeling of the UG4 Simulation Framework
260 _ _ |a Cham, Switzerland
|c 2016
|b Springer International Publishing
295 1 0 |a Software for Exascale Computing - SPPEXA 2013-2015 / Bungartz, Hans-Joachim (Editor) ; Chapter 21 ; ISBN: 978-3-319-40526-1=978-3-319-40528-5
300 _ _ |a 467 - 481
336 7 _ |a BOOK_CHAPTER
|2 ORCID
336 7 _ |a Book Section
|0 7
|2 EndNote
336 7 _ |a bookPart
|2 DRIVER
336 7 _ |a INBOOK
|2 BibTeX
336 7 _ |a Output Types/Book chapter
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336 7 _ |a Contribution to a book
|b contb
|m contb
|0 PUB:(DE-HGF)7
|s 1478618440_18228
|2 PUB:(DE-HGF)
490 0 _ |a Lecture Notes in Computational Science and Engineering
|v 113
520 _ _ |a Many 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.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
536 _ _ |0 G:(DE-Juel-1)ATMLPP
|a ATMLPP - ATML Parallel Performance (ATMLPP)
|c ATMLPP
|x 1
588 _ _ |a Dataset connected to CrossRef Book Series
700 1 _ |a Calotoiu, Alexandru
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Nägel, Arne
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Reiter, Sebastian
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Strube, Alexandre
|0 P:(DE-Juel1)140202
|b 4
|u fzj
700 1 _ |a Wittum, Gabriel
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Wolf, Felix
|0 P:(DE-HGF)0
|b 6
773 _ _ |a 10.1007/978-3-319-40528-5_21
909 C O |o oai:juser.fz-juelich.de:820613
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)140202
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2016
915 _ _ |a No Authors Fulltext
|0 StatID:(DE-HGF)0550
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920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a contb
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406

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Marc 21