001018654 001__ 1018654
001018654 005__ 20231130201846.0
001018654 037__ $$aFZJ-2023-04957
001018654 1001_ $$0P:(DE-Juel1)192255$$aBode, Mathis$$b0$$ufzj
001018654 245__ $$aBest Paper Award at ISAV 2023
001018654 260__ $$b“ISAV 2023: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization” workshop
001018654 3367_ $$2DataCite$$aOther
001018654 3367_ $$2EndNote$$aGrant
001018654 3367_ $$2BibTeX$$aMISC
001018654 3367_ $$0PUB:(DE-HGF)38$$2PUB:(DE-HGF)$$aAward$$baward$$maward$$s1701331070_23445
001018654 3367_ $$2ORCID$$aOTHER
001018654 3367_ $$2DINI$$aOther
001018654 500__ $$aThe paper is available online: https://doi.org/10.1145/3624062.3624159
001018654 502__ $$d2023
001018654 520__ $$aMathis Bode, Jens Henrik Göbbert, Jonathan Windgassen and their collaborators from Argonne National Laboratory (USA) have won the Best Paper Award for their paper “Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI”. It was presented at the “ISAV 2023: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization” workshop, which took place in conjunction with the SC23 on 13 November 2023 in Denver, Colorado, USA.The team describes in their paper a novel pipeline for in situ and in transit visualization and analysis utilizing SENSEI, ADIOS2, and ParaView over Python. The aim is to solve the dilemma having to choose between data accuracy or decreasing the resolution for Computational Fluid Dynamics on GPU-powered HPC systems. Their approach makes more regular data snapshots directly from memory and thus bypasses the pitfalls of checkpointing. The application NekRS is a GPU-centric thermal-fluid simulation, which showcases diverse in situ and in transit strategies. Experiments on the Polaris and JUWELS Booster supercomputers were conducted to demonstrate real-world implications, which offered crucial insights how efficient data management can be achieved without compromising accuracy.
001018654 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001018654 7001_ $$0P:(DE-Juel1)168541$$aGöbbert, Jens Henrik$$b1$$ufzj
001018654 7001_ $$0P:(DE-Juel1)185841$$aWindgassen, Jonathan$$b2$$ufzj
001018654 909CO $$ooai:juser.fz-juelich.de:1018654$$pVDB
001018654 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192255$$aForschungszentrum Jülich$$b0$$kFZJ
001018654 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168541$$aForschungszentrum Jülich$$b1$$kFZJ
001018654 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185841$$aForschungszentrum Jülich$$b2$$kFZJ
001018654 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$$x0
001018654 920__ $$lyes
001018654 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001018654 980__ $$aaward
001018654 980__ $$aVDB
001018654 980__ $$aI:(DE-Juel1)JSC-20090406
001018654 980__ $$aUNRESTRICTED