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| Hauptseite > Publikationsdatenbank > Best Paper Award at ISAV 2023 > print |
| 001 | 1018654 | ||
| 005 | 20231130201846.0 | ||
| 037 | _ | _ | |a FZJ-2023-04957 |
| 100 | 1 | _ | |a Bode, Mathis |0 P:(DE-Juel1)192255 |b 0 |u fzj |
| 245 | _ | _ | |a Best Paper Award at ISAV 2023 |
| 260 | _ | _ | |b “ISAV 2023: In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization” workshop |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a Grant |2 EndNote |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Award |b award |m award |0 PUB:(DE-HGF)38 |s 1701331070_23445 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Other |2 DINI |
| 500 | _ | _ | |a The paper is available online: https://doi.org/10.1145/3624062.3624159 |
| 502 | _ | _ | |d 2023 |
| 520 | _ | _ | |a Mathis 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. |
| 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 |
| 700 | 1 | _ | |a Göbbert, Jens Henrik |0 P:(DE-Juel1)168541 |b 1 |u fzj |
| 700 | 1 | _ | |a Windgassen, Jonathan |0 P:(DE-Juel1)185841 |b 2 |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1018654 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)192255 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)168541 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)185841 |
| 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 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a award |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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