Home > Publications database > Enabling Ginkgo as Numerics Backend in nekRS Employing A Loosely-Coupled Configuration File Concept > print

001     1052253
005     20260122203308.0
024 7 _ |a 10.1016/j.procs.2025.08.234
|2 doi
024 7 _ |a 1877-0509
|2 ISSN
024 7 _ |a 10.34734/FZJ-2026-00870
|2 datacite_doi
037 _ _ |a FZJ-2026-00870
082 _ _ |a 004
100 1 _ |a Tsai, Yu-Hsiang Mike
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Enabling Ginkgo as Numerics Backend in nekRS Employing A Loosely-Coupled Configuration File Concept
260 _ _ |a Amsterdam [u.a.]
|c 2025
|b Elsevier
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1769088280_20074
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a In computational fluid dynamics (CFD), the choice of numerical methods can significantly impact the overall simulation runtime. While it is virtually impossible to know the optimal solver plus preconditioner configuration for every hardware and application setup, it is valuable for CFD engineers to have access to and evaluate different numerical methods to customize the setup for efficient execution. In this paper, we demonstrate how the Ginkgo high-performance numerical linear algebra library is integrated as a math toolbox into the nekRS state-of-the-art computational fluid dynamics simulation library to give CFD engineers access to a plethora of solvers and preconditioners CFD engineers. Using three application test cases, we demonstrate how picking numerical methods from the Ginkgo library can accelerate simulations on supercomputers featuring NVIDIA’s Ampere GPUs and Grace Hopper superchips.
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
|f POF IV
|x 0
536 _ _ |a Inno4Scale - Innovative Algorithms for Applications on European Exascale Supercomputers (101118139)
|0 G:(EU-Grant)101118139
|c 101118139
|f HORIZON-EUROHPC-JU-2022-ALG-02
|x 1
588 _ _ |a Dataset connected to DataCite
700 1 _ |a Bode, Mathis
|0 P:(DE-Juel1)192255
|b 1
|u fzj
700 1 _ |a Anzt, Hartwig
|0 P:(DE-HGF)0
|b 2
|e Corresponding author
773 _ _ |a 10.1016/j.procs.2025.08.234
|g Vol. 267, p. 72 - 81
|0 PERI:(DE-600)2557358-5
|p 72 - 81
|t Procedia computer science
|v 267
|y 2025
|x 1877-0509
856 4 _ |u https://juser.fz-juelich.de/record/1052253/files/1-s2.0-S1877050925025761-main.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1052253
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)192255
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 0
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-16
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-16
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21