July 1 – 6 , 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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Dagstuhl Report, Volume 8, Issue 7 Dagstuhl Report
Aims & Scope
List of Participants
Dagstuhl's Impact: Documents available


The workshop identified ten challenges for in situ processing that require significant research. These challenges were identified by spending the first day of the workshop with participants giving short presentations on their experiences with in situ processing, with a special focus on unsolved problems. The participant perspectives were then organized into the ten research challenges. Over the following days, sub-groups discussed each of the ten challenges and then presented the key points of their discussions to the group and received feedback. Shortly after the workshop, the leaders of each sub-group wrote summaries for its associated research challenge; these summaries are the basis of this report.

The ten challenges identified by our participants were:

  • Data quality and reduction, i.e., reducing data in situ and then exploring it post hoc, which is likely the form that will enable exploration of large data sets on future supercomputers.
  • Workflow specification, i.e., how to specify the composition of different tools and applications to facilitate the in situ discovery process.
  • Workflow execution, i.e., how to efficiently execute specified workflows, including workflows that are very complex.
  • Exascale systems, which will have billion-way concurrency and disks that are slow relative to their ability to generate data.
  • Algorithmic challenges, i.e., algorithms will need to integrate into in situ ecosystems and still perform efficiently.
  • Use cases beyond exploratory analysis, i.e., ensembles for uncertainty quantification and decision optimization, computational steering, incorporation of other data sources, etc.
  • Exascale data, i.e., the data produced by simulations on exascale machines will, in many cases, be fundamentally different than that of previous machines.
  • Cost models, which can be used to predict performance before executing an algorithm and thus be used to optimize performance overall.
  • The convergence of HPC and Big Data for visualization and analysis, i.e., how can developments in one field, such as machine learning for Big Data, be used to accelerate techniques in the other?
  • Software complexity, heterogeneity, and user-facing issues, i.e., the challenges that prevent user adoption of in situ techniques because in situ software is complex, computational resources are complex, etc.

From group discussion, two other important topics emerged that do not directly lead to open research questions, but rather are concerned with effective organization of the often highly interdisciplinary research into in situ techniques. To address these, two panels were held to facilitate effective discussion. Finally, the workshop featured technical presentations by participants on recent results related to in situ visualization.

Summary text license
  Creative Commons BY 3.0 Unported license
  Janine C. Bennett, Hank Childs, Christoph Garth, and Bernd 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


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


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