01. – 06. Juli 2018, Dagstuhl-Seminar 18271

In Situ Visualization for Computational Science


Janine C. Bennett (Sandia National Labs – Albuquerque, US)
Hank Childs (University of Oregon – Eugene, US)
Christoph Garth (TU Kaiserslautern, DE)
Bernd Hentschel (RWTH Aachen, DE)

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In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to recent trends in the development of high-performance computers. The in situ paradigm shows great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, there are many open research topics that remain. For example, the paradigm is widely viewed as limiting when it comes to exploration-oriented use cases. Additionally, it will require visualization systems to become increasingly complicated and constrained in usage. The purpose of this Dagstuhl Seminar is to reflect on the current state of in situ visualization and to develop a research agenda to advance the methodology of in situ visualization.

The seminar brings together three communities (visualization researchers, high performance computing researchers, and computational science stakeholders) to initiate a structured discussion around three central and interweaving areas of research:

  • In situ workflows and methodologies. In situ processing has evolved into an umbrella term to describe a family of related approaches, each of which processes data as it is generated. Recent work has identified key design choices for in situ systems that imply software engineering challenges, choice of programming model, constraints on the visualization software, and more. Unfortunately, only a small subset of these aspects has been properly explored and evidence collected for these systems is mostly anecdotal. Thus, discussion at the seminar will focus on these system engineering aspects, informed by requirements of practical applications.
  • In situ data reduction and compression. Simulations have already outgrown the ability to retain the full data produced. In practice, this is typically addressed by storing an (often ad hoc defined) subset of the data or a naïvely resolution-reduced version (from which it is simply assumed will support meaningful analyses). Scientists are taking steps towards more intelligent data reduction and compression methods. These techniques omit irrelevant or redundant information, transform data to a more compact representation, or apply tailored compression algorithms, preserving sufficient data fidelity to yield meaningful flexibility in post hoc visualization. The goal of addressing this topic at the seminar is to both identify promising methodological approaches towards in situ data selection, reduction, and compression, as well as deriving measures for their effectiveness and efficiency.
  • Predictive models for in situ visualization costs. Real-world use of in situ visualization in computational science centrally hinges on understanding performance implications and overall cost. Predictive models that can accurately estimate the cost of specific in situ visualization approaches are a key requirement of utmost importance. At the seminar, we aim to improve understanding of such predictive models by bringing together practitioners in computational science with in situ visualization researchers. We will discuss the central question of how to formulate such models by combining theoretical aspects of the underlying algorithms and empirical results on runtime properties of benchmark problems.

The seminar will include presentations on the state-of-the-art on these topics, as well as perspective and research talks by the participants. Based on the identification of common themes, work will continue in several break-out sessions, where smaller groups discuss specialized topics of shared interest. We highly encourage participants to propose further topics, prior to or during the seminar.

  Creative Commons BY 3.0 DE
  Janine C. Bennett, Hank Childs, Christoph Garth, and Bernhard Hentschel


  • Computer Graphics / Computer Vision
  • Data Structures / Algorithms / Complexity
  • Modelling / Simulation


  • In situ visualization
  • Large-scale visualization
  • Scientific data analysis
  • Applications of visualization
  • Computational science


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