Dave Pugmire Contact PUGMIRE@ORNL.GOV All Publications Minimizing development costs for efficient many-core visualization using MCD3 The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs Spatial coupling of gyrokinetic simulations, a generalized scheme based on first-principles Global adjoint tomography—model GLAD-M25 Parallel Particle Advection Bake-Off for Scientific Visualization Workloads A terminology for in situ visualization and analysis systems Visualization as a Service for Scientific Data ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management Opportunities for Cost Savings with In-Transit Visualization... The moving target of visualization software for an increasingly complex world Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale... Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks In situ particle advection via parallelizing over particles PAVE: An In Situ Framework for Scientific Visualization and Machine Learning Coupling Understanding Performance-Quality Trade-offs in Scientific Visualization Workflows with Lossy Compression Understanding Performance-Quality Trade-offs in Scientific Visualization Workflows with Lossy Compression... A Lifeline-Based Approach for Work Requesting and Parallel Particle Advection A New Generation of Earth Mantle Model from Global Adjoint Tomography Comparing the Efficiency of In Situ Visualization Paradigms at Scale Coupling Exascale Multiphysics Applications: Methods and Lessons Learned In Situ Visualization for Computational Science A View from ORNL: Scientific Data Research Opportunities in the Big Data Age Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned Pagination First page « First Previous page ‹‹ Page 1 Current page 2 Page 3 … Next page ›› Last page Last » Key Links Google Scholar ORCID Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Data and AI Systems Section Visualization Group
Research Highlight Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
Research Highlight Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models
