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@InProceedings{Moreland2026,
  author    = {Kenneth Moreland and Jefferson Amstutz and Tushar M. Athawale and Vicente Bolea and Mark Bolstad and Hank Childs and Berk Geveci and Cyrus Harrison and Matthew Larsen and Li-Ta Lo and Nicole Marsaglia and Manish Mathai and David Pugmire and Silvio Rizzi and Spiros Tsalikis and Gunther H. Weber},
  booktitle = {VisGap - The Gap between Visualization Research and Visualization Software},
  title     = {Viskores: Integrating Parallel Scientific Visualization Research into Applications},
  year      = {2026},
  month     = jun,
  comment   = {First},
  doi       = {10.2312/visgap.20261000},
  slidesurl = {https://1drv.ms/p/c/afd0e9b2332ffee6/IQA6UmqEjongQobWJDv3sqGsAcHy83MQ28C1dA-0uU59HiY?e=MwDwHg},
}

@InProceedings{Buckley2026,
  author    = {Makani Buckley and Kenneth Moreland and Hank Childs},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Rasterization with Data-Parallel Primitives},
  year      = {2026},
  month     = jun,
  doi       = {10.2312/egpgv.20261000},
}

@Article{Tsalikis2026,
  author  = {Tsalikis, Spiros and Schroeder, Will and Szafir, Daniel and Moreland, Kenneth},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  title   = {Memory-Aware External Facelist Calculation: A Data-Parallel Atomic Hash Counting Approach},
  year    = {2026},
  month   = jun,
  number  = {6},
  pages   = {1--10},
  volume  = {32},
  doi     = {10.1109/TVCG.2026.3694435},
}

@InProceedings{Li2025:presimp,
  author    = {Mingzhe Li and Hamish Carr and Oliver Rübel and Bei Wang and Gunther H. Weber},
  booktitle = {Proceedings of the 15th IEEE Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification},
  year      = {2025},
  month     = nov,
  doi       = {10.1109/LDAV68558.2025.00005},
}

@InProceedings{Ouermi2025,
  author    = {Ouermi, Timbwaoga A. J. and Li, Eric and Moreland, Kenneth and Pugmire, Dave and Johnson, Chris R. and Athawale, Tushar M.},
  booktitle = {2025 IEEE Workshop on Uncertainty Visualization: Unraveling Relationships of Uncertainty, AI, and Decision-Making},
  title     = {Efficient Probabilistic Visualization of Local Divergence of 2D Vector Fields with Independent Gaussian Uncertainty},
  year      = {2025},
  month     = nov,
  note      = {Winner best paper},
  pages     = {12--16},
  doi       = {10.1109/UncertaintyVisualization68947.2025.00006},
}

@Article{Wang2025,
  author  = {Zhe Wang and Kenneth Moreland and Matthew Larsen and James Kress and Hank Childs and David Pugmire},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  title   = {Parallelize Over Data Particle Advection: Participation, Ping Pong Particles, and Overhead},
  year    = {2025},
  month   = oct,
  number  = {10},
  pages   = {7795--7808},
  volume  = {31},
  doi     = {10.1109/TVCG.2025.3557453},
  homeurl = {https://1drv.ms/b/c/afd0e9b2332ffee6/EfDse-zmFXZHl2LdSS0OmXsBc4P8hAu1xRxaWg8F5L5dpw?e=mbz9oC},
}

@Article{Li2025:aug,
  author  = {Li, Mingzhe and Carr, Hamish and Rübel, Oliver and Wang, Bei and Weber, Gunther H.},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  title   = {Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration},
  year    = {2025},
  month   = sep,
  number  = {1},
  pages   = {152--162},
  volume  = {31},
  doi     = {10.1109/TVCG.2024.3456322},
}

@Article{Athawale2025,
  author   = {Athawale, Tushar M. and Wang, Zhe and Pugmire, David and Moreland, Kenneth and Gong, Qian and Klasky, Scott and Johnson, Chris R. and Rosen, Paul},
  journal  = {IEEE Transactions on Visualization and Computer Graphics},
  title    = {Uncertainty Visualization of Critical Points of {2D} Scalar Fields for Parametric and Nonparametric Probabilistic Models},
  year     = {2025},
  month    = jan,
  number   = {1},
  pages    = {108--118},
  volume   = {31},
  abstract = {This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.},
  doi      = {10.1109/TVCG.2024.3456393},
  homeurl  = {https://1drv.ms/b/s!Aub-LzOy6dCviukwdJqdmg66TYBJVw?e=fjc3Lf},
}

@TechReport{ViskoresUsersGuide,
  author      = {Kenneth Moreland},
  institution = {Oak Ridge National Laboratory},
  title       = {The Viskores User's Guide},
  year        = {2025},
  number      = {ORNL/TM-2025/4175},
  type        = {techreport},
  url         = {https://viskores.readthedocs.io/en/v1.1.1/},
}

@InProceedings{Hari2024,
  author    = {Gautam Hari and Nrushad Joshi and Zhe Wang and Qian Gong and Dave Pugmire and Kenneth Moreland and Johnson, Chris R. and Scott Klasky and Norbert Podhorszki and Athawale, Tushar M.},
  booktitle = {Proceedings IEEE Workshop on Uncertainty Visualization},
  title     = {{FunM$^2$C}: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices},
  year      = {2024},
  month     = oct,
  pages     = {43--47},
  abstract  = {Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m's shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.},
  doi       = {10.1109/UncertaintyVisualization63963.2024.00010},
  homeurl   = {https://1drv.ms/b/s!Aub-LzOy6dCviukuIwhlsuGE2zYYDA?e=NyaWVZ},
}

@TechReport{VTKmUsersGuide,
  author      = {Kenneth Moreland},
  institution = {Oak Ridge National Laboratory},
  title       = {The {VTK-m} User's Guide},
  year        = {2024},
  number      = {ORNL/TM-2024/3443},
  type        = {techreport},
  url         = {https://gitlab.kitware.com/vtk/vtk-m-user-guide/-/wikis/home},
}

@Article{Moreland2024,
  author  = {Kenneth Moreland and Tushar M. Athawale and Vicente Bolea and Mark Bolstad and Eric Brugger and Hank Childs and Axel Huebl and Li-Ta Lo and Berk Geveci and Nicole Marsaglia and Sujin Philip and David Pugmire and Silvio Rizzi and Zhe Wang and Abhishek Yenpure},
  journal = {The International Journal of High Performance Computing Applications},
  title   = {{Visualization at exascale: Making it all work with VTK-m}},
  year    = {2024},
  number  = {5},
  pages   = {508--526},
  volume  = {38},
  doi     = {10.1177/10943420241270969},
}

@InProceedings{Bolstad2023,
  author    = {Mark Bolstad and Kenneth Moreland and David Pugmire and David Rogers and Li-Ta Lo and Berk Geveci and Hank Childs and Silvio Rizzi},
  booktitle = {ACM SIGGRAPH 2023 Talks},
  title     = {{VTK-m}: Visualization for the Exascale Era and Beyond},
  year      = {2023},
  month     = aug,
  doi       = {10.1145/3587421.3595466},
}

@InProceedings{Wang2023,
  author    = {Zhe Wang and Tushar M. Athawale and Kenneth Moreland and Jieyang Chen and Chris R. Johnson and David Pugmire},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {{FunMC2}: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices},
  year      = {2023},
  month     = may,
  doi       = {10.2312/pgv.20231081},
}

@Misc{Philip2023,
  author       = {Sujin Philip and Kenneth Moreland and Robert Maynard},
  howpublished = {Kitware Source},
  title        = {{VTK-m} Accelerated Filters in {VTK} and {ParaView}},
  year         = {2023},
  url          = {https://www.kitware.com/vtk-m-accelerated-filters-in-vtk-and-paraview/},
}

@InProceedings{Carr2022,
  author    = {Carr, Hamish A. and Rübel, Oliver and Weber, Gunther H.},
  booktitle = {IEEESymposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Distributed Hierarchical Contour Trees},
  year      = {2022},
  month     = oct,
  note      = {*WinnerBest Paper*},
  pages     = {1--10},
  doi       = {10.1109/LDAV57265.2022.9966394},
}

@Article{Moreland2021,
  author  = {Kenneth Moreland and Robert Maynard and David Pugmire and Abhishek Yenpure and Allison Vacanti and Matthew Larsen and Hank Childs},
  journal = {Parallel Computing},
  title   = {Minimizing Development Costs for Efficient Many-Core Visualization Using {MCD$^3$}},
  year    = {2021},
  month   = dec,
  number  = {102834},
  volume  = {108},
  doi     = {10.1016/j.parco.2021.102834},
}

@Misc{Farber2021,
  author       = {Rob Farber},
  howpublished = {ECP Technical Highlights},
  month        = nov,
  title        = {{ECP} Brings Much Needed Visualization Software to Exascale and {GPU}-Accelerated Systems},
  year         = {2021},
  url          = {https://www.exascaleproject.org/highlight/ecp-brings-much-needed-visualization-software-to-exascale-and-gpu-accelerated-systems/},
}

@Article{Carr2021:opt,
  author  = {Hamish A Carr and Oliver R{\"u}bel and Gunther H Weber and James P Ahrens},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  title   = {Optimization and Augmentation for Data Parallel Contour Trees},
  year    = {2021},
  month   = oct,
  number  = {10},
  pages   = {3471--3485},
  volume  = {28},
  doi     = {10.1109/TVCG.2021.3064385},
}

@InProceedings{Sane2021:ICCS,
  author    = {Sane, Sudhanshu and Johnson, Chris R. and Childs, Hank},
  booktitle = {Computational Science -- ICCS 2021},
  title     = {Investigating In Situ Reduction via Lagrangian Representations for Cosmology and Seismology Applications},
  year      = {2021},
  month     = jun,
  note      = {*Winner: Best Paper*},
  pages     = {436--450},
  doi       = {10.1007/978-3-030-77961-0_36},
}

@InProceedings{Sane2021:EGPGV,
  author    = {Sane, Sudhanshu and Yenpure, Abhishek and Bujack, Roxana and Larsen, Matthew and Moreland, Kenneth and Garth, Christoph and Johnson, Chris R. and Childs, Hank},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps},
  year      = {2021},
  month     = jun,
  note      = {*Winner: Best Paper*},
  doi       = {10.2312/pgv.20211040},
}

@Article{Carr2021:scale,
  author  = {Carr, Hamish A. and Weber, Gunther H. and Sewell, Christopher M. and Rübel, Oliver and Fasel, Patricia and Ahrens, James P.},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  title   = {Scalable Contour Tree Computation by Data Parallel Peak Pruning},
  year    = {2021},
  month   = apr,
  number  = {4},
  pages   = {2437--2454},
  volume  = {27},
  doi     = {10.1109/TVCG.2019.2948616},
}

@InProceedings{Pugmire2021,
  author    = {David Pugmire and Caitlin Ross and Nicholas Thompson and James Kress and Chuck Atkins and Scott Klasky and Berk Geveci},
  booktitle = {ISC High Performance},
  title     = {Fides: A General Purpose Data Model Library for Streaming Data},
  year      = {2021},
  pages     = {495--507},
  doi       = {https://doi.org/10.1007/978-3-030-90539-2_34},
}

@InProceedings{Schwartz2021,
  author    = {Schwartz, Samuel D. and Childs, Hank and Pugmire, David},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Machine Learning-Based Autotuning for Parallel Particle Advection},
  year      = {2021},
  doi       = {10.2312/pgv.20211039},
}

@InProceedings{Hristov2020,
  author    = {Hristov, Petar and Weber, Gunther H. and Carr, Hamish A. and Rübel, Oliver and Ahrens, James P.},
  booktitle = {IEEESymposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Data Parallel Hypersweeps for in Situ Topological Analysis},
  year      = {2020},
  month     = oct,
  pages     = {12--21},
  doi       = {10.1109/LDAV51489.2020.00008},
}

@InProceedings{Lessley2020,
  author    = {Lessley, Brenton and Li, Shaomeng and Childs, Hank},
  booktitle = {Electronic Imaging, Visualization and Data Analysis},
  title     = {{HashFight}: A Platform-Portable Hash Table for Multi-Core and Many-Core Architectures},
  year      = {2020},
  pages     = {376-1-376-13(13)},
  doi       = {10.2352/ISSN.2470-1173.2020.1.VDA-376},
}

@InProceedings{Perciano2020,
  author    = {Perciano, Talita and Heinemann, Colleen and Camp, David and Lessley, Brenton and Bethel, E. Wes},
  booktitle = {High Performance Computing},
  title     = {Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives},
  year      = {2020},
  pages     = {127--145},
  doi       = {10.1007/978-3-030-50743-5_7},
}

@InProceedings{Wang2019,
  author    = {Ko-Chih Wang and Jiayi Xu and Jonathan Woodring and Han-Wei Shen},
  booktitle = {IEEE Pacific Visualization Symposium (PacificVis)},
  title     = {Statistical Super Resolution for Data Analysis and Visualization of Large Scale Cosmological Simulations},
  year      = {2019},
  month     = apr,
  pages     = {303--312},
  doi       = {10.1109/PacificVis.2019.00043},
}

@InProceedings{Yenpure2019,
  author    = {Abhishek Yenpure and Hank Childs and Kenneth Moreland},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Efficient Point Merging Using Data Parallel Techniques},
  year      = {2019},
  doi       = {10.2312/pgv.20191112},
  url       = {http://www.kennethmoreland.com/topology-threading#EGPGV2019},
}

@InProceedings{Lessley2018,
  author    = {Brenton Lessley and Talita Perciano and Colleen Heinemann and David Camp and Hank Childs and E. Wes Bethel},
  booktitle = {Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {{DPP-PMRF}: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives},
  year      = {2018},
  pages     = {34--44},
  doi       = {10.1109/LDAV.2018.8739239},
}

@InProceedings{Pugmire2018,
  author    = {David Pugmire and Abhishek Yenpure and Mark Kim and James Kress and Robert Maynard and Hank Childs and Bernd Hentschel},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Performance-Portable Particle Advection with {VTK-m}},
  year      = {2018},
  pages     = {45--55},
  doi       = {10.2312/pgv.20181094},
  url       = {http://cdux.cs.uoregon.edu/pubs/PugmireEGPGV.pdf},
}

@InProceedings{Lessley2017:Duplicate,
  author    = {Brenton Lessley and Kenneth Moreland and Matthew Larsen and Hank Childs},
  booktitle = {IEEE Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Techniques for Data-Parallel Searching for Duplicate Elements},
  year      = {2017},
  doi       = {10.1109/LDAV.2017.8231845},
}

@InProceedings{Lessley2017:Clique,
  author    = {Brenton Lessley and Talita Perciano and Manish Mathai and Hank Childs and E. Wes Bethel},
  booktitle = {IEEE Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Maximal Clique Enumeration with Data-Parallel Primitives},
  year      = {2017},
  doi       = {10.1109/LDAV.2017.8231847},
  url       = {http://cdux.cs.uoregon.edu/pubs/LessleyLDAV2.pdf},
}

@InProceedings{Li2017:EGPGV,
  author    = {Li, Shaomeng and Marsaglia, Nicole and Chen, Vincent and Sewell, Christopher and Clyne, John and Childs, Hank},
  booktitle = {Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {Achieving Portable Performance For Wavelet Compression Using Data Parallel Primitives},
  year      = {2017},
  pages     = {73--81},
  doi       = {10.2312/pgv.20171095},
  url       = {http://cdux.cs.uoregon.edu/pubs/LiEGPGV.pdf},
}

@InProceedings{Carr2016:LDAV,
  author    = {Hamish Carr and Gunther Weber and Christopher Sewell and James Ahrens},
  booktitle = {Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Parallel Peak Pruning for Scalable SMP Contour Tree Computation},
  year      = {2016},
  doi       = {10.1109/LDAV.2016.7874312},
}

@InProceedings{Lessley2016,
  author    = {Brenton Lessley and Roba Binyahib and Robert Maynard and Hank Childs},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization (EGPGV)},
  title     = {External Facelist Calculation with Data-Parallel Primitives},
  year      = {2016},
  doi       = {10.2312/pgv.20161178},
  url       = {http://cdux.cs.uoregon.edu/pubs/LessleyEGPGV.pdf},
}

@Article{Moreland2016:VTKm,
  author  = {Kenneth Moreland and Christopher Sewell and William Usher and Li-Ta Lo and Jeremy Meredith and David Pugmire and James Kress and Hendrik Schroots and Kwan-Liu Ma and Hank Childs and Matthew Larsen and Chun-Ming Chen and Robert Maynard and Berk Geveci},
  journal = {IEEE Computer Graphics and Applications},
  title   = {{VTK-m}: Accelerating the Visualization Toolkit for Massively Threaded Architectures},
  year    = {2016},
  number  = {3},
  pages   = {48--58},
  volume  = {36},
  doi     = {10.1109/MCG.2016.48},
  url     = {http://dx.doi.org/10.1109/MCG.2016.48},
}

@InProceedings{Larsen2015:VR,
  author    = {Larsen, Matthew and Labasan, Stephanie and Navr\'{a}til, Paul and Meredith, Jeremy and Childs, Hank},
  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
  title     = {Volume Rendering Via Data-Parallel Primitives},
  year      = {2015},
  doi       = {10.2312/pgv.20151155},
  url       = {http://cdux.cs.uoregon.edu/pubs/LarsenEGPGV.pdf},
}

@InProceedings{Larsen2015:RayTrace,
  author    = {Matthew Larsen and Jeremy S. Meredith and Paul A. Navratil and Hank Childs},
  booktitle = {IEEE Pacific Visualization Symposium (PacificVis)},
  title     = {Ray Tracing Within a Data Parallel Framework},
  year      = {2015},
  pages     = {279--286},
  doi       = {10.1109/PACIFICVIS.2015.7156388},
  url       = {http://dx.doi.org/10.1109/PACIFICVIS.2015.7156388},
}

@Article{Moreland2015:SFI,
  author  = {Kenneth Moreland and Matthew Larsen and Hank Childs},
  journal = {Supercomputing Frontiers and Innovations},
  title   = {Visualization for Exascale: Portable Performance is Critical},
  year    = {2015},
  number  = {3},
  volume  = {2},
  doi     = {10.14529/jsfi150306},
  url     = {http://dx.doi.org/10.14529/jsfi150306},
}

@InProceedings{Schroots2015,
  author    = {Hendrik A. Schroots and Kwan-Liu Ma},
  booktitle = {SIGGRAPH Asia Visualization in High Performance Computing},
  title     = {Volume rendering with data parallel visualization frameworks for emerging high performance computing architectures},
  year      = {2015},
  pages     = {3:1--3:4},
  doi       = {10.1145/2818517.2818546},
}

@InProceedings{Sewell2015,
  author    = {Christopher Sewell and Li-ta Lo and Katrin Heitmann and Salman Habib and James Ahrens},
  booktitle = {IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV)},
  title     = {Utilizing many-core accelerators for halo and center finding within a cosmology simulation},
  year      = {2015},
  doi       = {10.1109/LDAV.2015.7348076},
}

@InProceedings{Maynard2013,
  author    = {Robert Maynard and Kenneth Moreland and Utkarsh Ayachit and Berk Geveci and Kwan-Liu Ma},
  booktitle = {Visualization and Data Analysis 2013, Proceedings of SPIE-IS\&T Electronic Imaging},
  title     = {Optimizing Threshold for Extreme Scale Analysis},
  year      = {2013},
  doi       = {10.1117/12.2007320},
}

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