Browsing by Author "Moreland, Kenneth"

Now showing 1 - 3 of 3
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    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.
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    FunMC^2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices
    (The Eurographics Association, 2023) Wang, Zhe; Athawale, Tushar M.; Moreland, Kenneth; Chen, Jieyang; Johnson, Chris R.; Pugmire, David; Bujack, Roxana; Pugmire, David; Reina, Guido
    Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to multi-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC2). FunMC2 accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC2 based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC2 based on a single GPU is around 5× to 9× faster than FunMC2 running by OpenMP. Moreover, FunMC2 is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC2 can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.
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    Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations
    (The Eurographics Association, 2024) Pugmire, David; Choi, Jong Y.; Klasky, Scott; Moreland, Kenneth; Suchyta, Eric; Athawale, Tushar M.; Wang, Zhe; Chang, Choong-Seock; Ku, Seung-Hoe; Hager, Robert; Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas
    Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.