Browsing by Author "Childs, Hank"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Automatic In Situ Camera Placement for Isosurfaces of Large-Scale Scientific Simulations
    (The Eurographics Association, 2022) Marsaglia, Nicole; Mathai, Manish; Fields, Stefan; Childs, Hank; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
    High-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.
  • Thumbnail Image
    Item
    Efficient Point Merging Using Data Parallel Techniques
    (The Eurographics Association, 2019) Yenpure, Abhishek; Childs, Hank; Moreland, Kenneth; Childs, Hank and Frey, Steffen
    We study the problem of merging three-dimensional points that are nearby or coincident. We introduce a fast, efficient approach that uses data parallel techniques for execution in various shared-memory environments. Our technique incorporates a heuristic for efficiently clustering spatially close points together, which is one reason our method performs well against other methods. We then compare our approach against methods of a widely-used scientific visualization library accompanied by a performance study that shows our approach works well with different kinds of parallel hardware (many-core CPUs and NVIDIA GPUs) and data sets of various sizes.