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In Situ Visualization for Computational Science

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Hank Childs

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

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    You can also search for this editor in PubMed   Google Scholar

  2. Janine C. Bennett

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Christoph Garth

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

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    You can also search for this editor in PubMed   Google Scholar

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

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Table of contents (20 papers)

  1. Front Matter

    Pages i-xv
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  2. In Situ Visualization for Computational Science: Background and Foundational Topics

    • Hank Childs, Janine C. Bennett, Christoph Garth
    Pages 1-8

  3. Data Reduction Techniques

    1. Front Matter

      Pages 9-9
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    2. Sampling for Scientific Data Analysis and Reduction

      • Ayan Biswas, Soumya Dutta, Terece L. Turton, James Ahrens
      Pages 11-36

    3. In Situ Wavelet Compression on Supercomputers for Post Hoc Exploration

      • Shaomeng Li, John Clyne, Hank Childs
      Pages 37-59

    4. In Situ Statistical Distribution-Based Data Summarization and Visual Analysis

      • Soumya Dutta, Subhashis Hazarika, Han-Wei Shen
      Pages 61-90

    5. Exploratory Time-Dependent Flow Visualization via In Situ Extracted Lagrangian Representations

      • Sudhanshu Sane, Hank Childs
      Pages 91-109

  4. Workflows and Scheduling

    1. Front Matter

      Pages 111-111
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    2. Unlocking Large Scale Uncertainty Quantification with In Transit Iterative Statistics

      • Alejandro Ribés, Théophile Terraz, Yvan Fournier, Bertrand Iooss, Bruno Raffin
      Pages 113-136

    3. Decaf: Decoupled Dataflows for In Situ Workflows

      • Orcun Yildiz, Matthieu Dreher, Tom Peterka
      Pages 137-158

    4. Parameter Adaptation In Situ: Design Impacts and Trade-Offs

      • Steffen Frey, Valentin Bruder, Florian Frieß, Patrick Gralka, Tobias Rau, Thomas Ertl et al.
      Pages 159-182

    5. Resource-Aware Optimal Scheduling of In Situ Analysis

      • Preeti Malakar, Venkatram Vishwanath, Christopher Knight, Todd Munson, Michael E. Papka
      Pages 183-202

  5. Tools

    1. Front Matter

      Pages 203-203
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    2. Leveraging Production Visualization Tools In Situ

      • Kenneth Moreland, Andrew C. Bauer, Berk Geveci, Patrick O’Leary, Brad Whitlock
      Pages 205-231

    3. The Adaptable IO System (ADIOS)

      • David Pugmire, Norbert Podhorszki, Scott Klasky, Matthew Wolf, James Kress, Mark Kim et al.
      Pages 233-254

    4. Ascent: A Flyweight In Situ Library for Exascale Simulations

      • Matthew Larsen, Eric Brugger, Hank Childs, Cyrus Harrison
      Pages 255-279

    5. The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale

      • E. Wes Bethel, Burlen Loring, Utkarsh Ayachit, David Camp, Earl P. N. Duque, Nicola Ferrier et al.
      Pages 281-306

    6. In Situ Solutions with CinemaScience

      • David H. Rogers, Soumya Dutta, Divya Banesh, Terece L. Turton, Ethan Stam, James Ahrens
      Pages 307-328

  6. New Research Results and Looking Forward

    1. Front Matter

      Pages 329-329
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    2. Deep Learning-Based Upscaling for In Situ Volume Visualization

      • Sebastian Weiss, Jun Han, Chaoli Wang, Rüdiger Westermann
      Pages 331-352
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Keywords

About this book

This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing.


Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future. 



Editors and Affiliations

  • Computer and Information Science, University of Oregon, Eugene, USA

    Hank Childs

  • Extreme-Scale Data Science and Analytics, Sandia National Laboratories, Livermore, USA

    Janine C. Bennett

  • Scientific Visualization Lab, University of Kaiserslautern, Kaiserslautern, Germany

    Christoph Garth

Bibliographic Information

  • Book Title: In Situ Visualization for Computational Science

  • Editors: Hank Childs, Janine C. Bennett, Christoph Garth

  • Series Title: Mathematics and Visualization

  • DOI: https://doi.org/10.1007/978-3-030-81627-8

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-81626-1Published: 05 May 2022

  • Softcover ISBN: 978-3-030-81629-2Published: 06 May 2023

  • eBook ISBN: 978-3-030-81627-8Published: 04 May 2022

  • Series ISSN: 1612-3786

  • Series E-ISSN: 2197-666X

  • Edition Number: 1

  • Number of Pages: XV, 460

  • Number of Illustrations: 17 b/w illustrations, 184 illustrations in colour

  • Topics: Visualization, Computational Science and Engineering

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