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Parameter Adaptation In Situ: Design Impacts and Trade-Offs

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In Situ Visualization for Computational Science

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

This chapter presents a study of parameter adaptation in situ, exploring the resulting trade-offs in rendering quality and workload distribution. Four different use cases are analyzed with respect to configuration changes. First, the performance impact of load balancing and resource allocation variants on both simulation and visualization is investigated using the MegaMol framework. Its loose coupling scheme and architecture enable minimally invasive in situ operation without impacting the stability of the simulation with (potentially) experimental visualization code. Second, Volumetric Depth Images (VDIs) are considered: a compact, view-dependent intermediate representation that can efficiently be generated and used for post hoc exploration. A study of their inherent trade-offs regarding size, quality, and generation time provides the basis for parameter optimization. Third, streaming for remote visualization allows a user to monitor the progress of a simulation and to steer visualization parameters. Compression settings are adapted dynamically based on predictions via convolutional neural networks across different parts of images to achieve high frame rates for high-resolution displays like powerwalls. Fourth, different performance prediction models for volume rendering address offline scenarios (like hardware acquisition planning) as well as dynamic adaptation of parameters and load balancing. Finally, the chapter concludes by summarizing overarching approaches and challenges, discussing the potential role that adaptive approaches can play in increasing the efficiency of in situ visualization.

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Acknowledgements

This work is partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2075 (SimTech)—390740016 and as part of Project A02 of SFB/Transregio 161 (project number 251654672). It was also partially funded by the German Bundesministerium für Bildung und Forschung (BMBF) as part of project “TaLPas” (Task-based Load Balancing and Auto-tuning in Particle Simulations). We would like to thank Intel® Corporation for additional support via the Intel® Graphics and Visualization Institutes of XeLLENCE program (CG #35512501). The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this chapter. Additionally, the authors would like to thank the ls1 Mardyn development team for their support and Matthias Heinen for providing the simulation configurations.

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Authors and Affiliations

  1. University of Groningen, Groningen, The Netherlands

    Steffen Frey

  2. University of Stuttgart, Stuttgart, Germany

    Valentin Bruder, Florian Frieß, Patrick Gralka, Tobias Rau, Thomas Ertl & Guido Reina

Authors
  1. Steffen Frey
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  2. Valentin Bruder
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  3. Florian Frieß
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  4. Patrick Gralka
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  5. Tobias Rau
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  6. Thomas Ertl
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  7. Guido Reina
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Corresponding author

Correspondence to Steffen Frey .

Editor information

Editors and Affiliations

  1. Computer and Information Science, University of Oregon, Eugene, OR, USA

    Hank Childs

  2. Extreme-Scale Data Science and Analytics, Sandia National Laboratories, Livermore, CA, USA

    Janine C. Bennett

  3. Scientific Visualization Lab, University of Kaiserslautern, Kaiserslautern, Germany

    Christoph Garth

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Frey, S. et al. (2022). Parameter Adaptation In Situ: Design Impacts and Trade-Offs. In: Childs, H., Bennett, J.C., Garth, C. (eds) In Situ Visualization for Computational Science. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-81627-8_8

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