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001026000 1001_ $$00000-0002-6677-7520$$aMateevitsi, Victor A.$$b0
001026000 1112_ $$aWorkshops of The International Conference on High Performance Computing, Network, Storage, and Analysis$$cDenver, CO$$d2023-11-12 - 2023-11-17$$gSC-W 2023$$wUSA
001026000 245__ $$aScaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI
001026000 260__ $$aNew York, USA$$bAssociation for Computing Machinery$$c2023
001026000 29510 $$aProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis - ACM New York, NY, USA, 2023
001026000 300__ $$a862–867
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001026000 520__ $$aIn the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data "snapshots" to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use-cases and report on runs performed on Polaris, Argonne Leadership Computing Facility’s (ALCF) 44 Petaflop supercomputer and Jülich Wizard for European Leadership Science (JUWELS) Booster, Jülich Supercomputing Centre’s (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology.
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001026000 7001_ $$0P:(DE-Juel1)192255$$aBode, Mathis$$b1$$ufzj
001026000 7001_ $$00000-0003-1444-0624$$aFerrier, Nicola$$b2
001026000 7001_ $$0P:(DE-HGF)0$$aFischer, Paul$$b3
001026000 7001_ $$0P:(DE-Juel1)168541$$aGöbbert, Jens Henrik$$b4
001026000 7001_ $$00000-0002-6955-869X$$aInsley, Joseph A.$$b5
001026000 7001_ $$00000-0002-1680-675X$$aLan, Yu-Hsiang$$b6
001026000 7001_ $$00000-0002-5646-5689$$aMin, Misun$$b7
001026000 7001_ $$00000-0002-6418-5767$$aPapka, Michael E.$$b8
001026000 7001_ $$00000-0003-0803-5761$$aPatel, Saumil$$b9
001026000 7001_ $$00000-0002-3804-2471$$aRizzi, Silvio$$b10
001026000 7001_ $$0P:(DE-Juel1)185841$$aWindgassen, Jonathan$$b11
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001026000 773__ $$a10.1145/3624062.3624159
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