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.

翻译：现场处理已被广泛认为是现代高性能计算系统大规模仿真输出可视化和分析的有效方法。基于批处理的现场可视化最常用方法之一是图像或视频方法。在这种方法中，每个时间步从不同视角生成大量渲染图像，已被证明对主仿真结果的详细分析非常有用。然而，在主仿真运行之前的测试运行和模型校准过程中，快速概览可能就足够且实用。在本工作中，我们专注于通过使用信息熵选择能提供尽可能多信息的视角，以最大化后续视觉分析任务。然而，简单地在每个可视化时间步跟随所选视角，可能会导致视频变化过快，从而影响理解。因此，我们还研究了一种高效的相机路径估计方法，用于在固定时间间隔内连接所选视角，以生成平滑视频。生成的视频有望帮助快速理解底层仿真现象，并有助于缩小时间感兴趣区域，从而在主仿真运行的图像或视频视觉分析过程中，最小化详细探索的周转时间。我们使用OpenFOAM计算流体动力学应用程序，在基于x86的服务器和基于ARM A64FX的超级计算机（Fugaku）上实施并评估了所提出的方法，并获得了领域科学家的积极评价。