Marsaglia, NicoleMathai, ManishFields, StefanChilds, HankBujack, RoxanaTierny, JulienSadlo, Filip2022-06-022022-06-022022978-3-03868-175-51727-348Xhttps://doi.org/10.2312/pgv.20221065https://diglib.eg.org:443/handle/10.2312/pgv20221065High-performance computing trends are requiring in situ processing increasingly often. This work considers automating camera placement for in situ visualization, specifically of isosurfaces, which is needed when there is no human in the loop and no a priori knowledge of where to place the camera. Our approach utilizes Viewpoint Quality (VQ) metrics, which quantify which camera positions provide the most insight. We have two primary contributions. First, we introduce an approach parallelizing the calculation of VQ metrics, which is necessary for usage in an in situ setting. Second, we introduce an algorithm for searching for a good camera position that balances between maximizing VQ metric score and minimizing execution time. We evaluate our contributions with an in situ performance study on a supercomputer. Our findings confirm that our approach is viable, and in particular that we can find good viewpoints with small execution time.Attribution 4.0 International License10.2312/pgv.2022106549-5911 pages