  In-situ processing has widely been recognized as an effective approach for
the visualization and analysis of large-scale simulation outputs from modern
HPC systems. One of the most common approaches for batch-based in-situ
visualization is the image- or video-based approach. In this kind of approach,
a large number of rendered images are generated from different viewpoints at
each time step and has proven useful for detailed analysis of the main
simulation results. However, during test runs and model calibration runs before
the main simulation run, a quick overview might be sufficient and useful. In
this work, we focused on selecting the viewpoints which provide as much
information as possible by using information entropy to maximize the subsequent
visual analysis task. However, by simply following the selected viewpoints at
each of the visualization time steps will probably lead to a rapidly changing
video, which can impact the understanding. Therefore, we have also worked on an
efficient camera path estimation approach for connecting selected viewpoints,
at regular intervals, to generate a smooth video. This resulting video is
expected to assist in rapid understanding of the underlying simulation
phenomena and can be helpful to narrow down the temporal region of interest to
minimize the turnaround time during detailed visual exploration via image- or
video-based visual analysis of the main simulation run. We implemented and
evaluated the proposed approach using the OpenFOAM CFD application, on an
x86-based Server and an ARM A64FX-based supercomputer (Fugaku), and we obtained
positive evaluations from domain scientists.
