Information Entropy-based Camera Focus Point and Zoom Level Adjustment for Smart In-Situ Visualization.

With the recent developments in computational science and HPC technology, large-scale numerical simulations have become common in various scientific and technological fields. The output volume data from these simulations have also become larger and more complex, creating a problem for the time-consuming input/output to/from the HPC storage system. To solve this problem, in-situ visualization has been used. However, the output data for posterior analysis is usually a large set of image data obtained from the visualization, and there is a lack of interactivity compared to the conventional analysis, which loads the volume data from the storage, after the simulation, and executes interactive visual exploration. To compensate for these problems, in-situ visualization often places multiple viewpoints in the simulation space and generates images from all of them. However, this may result in a huge number of images, and as a result, this can require time and effort to locate important visualization images that can provide clues to obtain knowledge during the analysis. To solve this problem, this study estimates the regions where important changes occur in the simulation, based on information entropy calculated from the visualization images, and generates a sequence of animated images focusing on these regions. In-situ visualization has widely been recognized as an effective approach for analyzing large-scale simulation outputs from modern HPC systems by reducing the inherent I/O bottleneck problem. However, batch-based in-situ visualization, such as the image- and video-based approaches, can produce large amounts of rendering results for the subsequent offline visual analysis. Therefore, this can make it difficult to gain rapid insight into the simulation results during post-hoc visual analysis. To minimize this problem, we have worked on a smart visualization approach focusing on extracting a set of images that may facilitate the rapid understanding of the underlying simulated phenomena as an alternative to accelerate the process of obtaining scientific knowledge. In this work, we present a method for automatically adjusting the camera focus point and zoom level during in-situ visualization in an attempt to obtain the most suitable rendering images for facilitating visual analysis. We integrated the proposed method with the existing in-situ smooth camera path estimation framework, for evaluation purposes, and used two CFD simulation codes and two HPC systems (x86-based server system and Arm-based Fugaku supercomputer) for the evaluations. We obtained encouraging results from the preliminary evaluations, and we are planning further improvements by working closely with domain expert collaborators.