001052253 001__ 1052253
001052253 005__ 20260122203308.0
001052253 0247_ $$2doi$$a10.1016/j.procs.2025.08.234
001052253 0247_ $$2ISSN$$a1877-0509
001052253 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00870
001052253 037__ $$aFZJ-2026-00870
001052253 082__ $$a004
001052253 1001_ $$0P:(DE-HGF)0$$aTsai, Yu-Hsiang Mike$$b0
001052253 245__ $$aEnabling Ginkgo as Numerics Backend in nekRS Employing A Loosely-Coupled Configuration File Concept
001052253 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2025
001052253 3367_ $$2DRIVER$$aarticle
001052253 3367_ $$2DataCite$$aOutput Types/Journal article
001052253 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1769088280_20074
001052253 3367_ $$2BibTeX$$aARTICLE
001052253 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001052253 3367_ $$00$$2EndNote$$aJournal Article
001052253 520__ $$aIn computational fluid dynamics (CFD), the choice of numerical methods can significantly impact the overall simulation runtime. While it is virtually impossible to know the optimal solver plus preconditioner configuration for every hardware and application setup, it is valuable for CFD engineers to have access to and evaluate different numerical methods to customize the setup for efficient execution. In this paper, we demonstrate how the Ginkgo high-performance numerical linear algebra library is integrated as a math toolbox into the nekRS state-of-the-art computational fluid dynamics simulation library to give CFD engineers access to a plethora of solvers and preconditioners CFD engineers. Using three application test cases, we demonstrate how picking numerical methods from the Ginkgo library can accelerate simulations on supercomputers featuring NVIDIA’s Ampere GPUs and Grace Hopper superchips.
001052253 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001052253 536__ $$0G:(EU-Grant)101118139$$aInno4Scale - Innovative Algorithms for Applications on European Exascale Supercomputers (101118139)$$c101118139$$fHORIZON-EUROHPC-JU-2022-ALG-02$$x1
001052253 588__ $$aDataset connected to DataCite
001052253 7001_ $$0P:(DE-Juel1)192255$$aBode, Mathis$$b1$$ufzj
001052253 7001_ $$0P:(DE-HGF)0$$aAnzt, Hartwig$$b2$$eCorresponding author
001052253 773__ $$0PERI:(DE-600)2557358-5$$a10.1016/j.procs.2025.08.234$$gVol. 267, p. 72 - 81$$p72 - 81$$tProcedia computer science$$v267$$x1877-0509$$y2025
001052253 8564_ $$uhttps://juser.fz-juelich.de/record/1052253/files/1-s2.0-S1877050925025761-main.pdf$$yOpenAccess
001052253 909CO $$ooai:juser.fz-juelich.de:1052253$$popenaire$$popen_access$$pdriver$$pVDB$$pec_fundedresources$$pdnbdelivery
001052253 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192255$$aForschungszentrum Jülich$$b1$$kFZJ
001052253 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001052253 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-16
001052253 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-16
001052253 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001052253 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001052253 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001052253 9801_ $$aFullTexts
001052253 980__ $$ajournal
001052253 980__ $$aVDB
001052253 980__ $$aUNRESTRICTED
001052253 980__ $$aI:(DE-Juel1)JSC-20090406