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Merge pull request #148 from SENSEI-insitu/isav_23_paper
NekRS ISAV 23 paper vignette
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Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI | ||
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V. A. Mateevitsi, M. Bode, N. Ferrier, P. Fischer, J. H. Göbbert, J. A. Insley, Y. H. Lan, M. Min, M. E. Papka, S. Patel, S. Rizzi, J. Windgassen | ||
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Full Text | ||
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Link to the full text `PDF <https://dl.acm.org/doi/abs/10.1145/3624062.3624159>`_ . | ||
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Abstract | ||
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In 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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