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Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

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The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.

Geodesic grids are commonly used to model the surface of a sphere and are widely applied in numerical simulations of geoscience applications. These applications range from biodiversity, to climate change and to ocean circulation. Direct volume rendering of scalar fields defined on a geodesic grid facilitates scientists in visually understanding their large scale data. Previous solutions requiring to first transform the geodesic grid into another grid structure (e.g., hexahedral or tetrahedral grid) for using graphics hardware are not acceptable for large data, because such approaches incur significant computing and storage overhead. In this paper, we present a new method for efficient ray casting of geodesic girds by leveraging the power of Graphics Processing Units (GPUs). A geodesic grid can be directly fetched from storage or streamed from simulations to the rendering stage without the need of any intermediate grid transformation. We have designed and implemented a new analytic scheme to efficiently perform value interpolation for ray integration and gradient calculations for lighting. This scheme offers a more cost-effective rendering solution over the existing direct rendering approach. We demonstrate the effectiveness of our rendering solution using real-world geoscience data.