THE LINUX FOUNDATION PROJECTS

Publications

  1. Moreland, K., Sewell, C., Usher, W., Lo, L.-T., Meredith, J., Pugmire, D., Kress, J., Schroots, H., Ma, K.-L., Childs, H., Larsen, M., Chen, C.-M., Maynard, R., & Geveci, B. (2016). VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures. IEEE Computer Graphics and Applications, 36(3), 48–58. https://doi.org/10.1109/MCG.2016.48
  2. Moreland, K. (2024). The VTK-m User’s Guide (Techreport ORNL/TM-2024/3443; Number ORNL/TM-2024/3443). Oak Ridge National Laboratory. https://gitlab.kitware.com/vtk/vtk-m-user-guide/-/wikis/home
  3. Moreland, K., Athawale, T. M., Bolea, V., Bolstad, M., Brugger, E., Childs, H., Huebl, A., Lo, L.-T., Geveci, B., Marsaglia, N., Philip, S., Pugmire, D., Rizzi, S., Wang, Z., & Yenpure, A. (2024). Visualization at exascale: Making it all work with VTK-m. The International Journal of High Performance Computing Applications, 38(5), 508–526. https://doi.org/10.1177/10943420241270969
  4. Bolstad, M., Moreland, K., Pugmire, D., Rogers, D., Lo, L.-T., Geveci, B., Childs, H., & Rizzi, S. (2023, August). VTK-m: Visualization for the Exascale Era and Beyond. ACM SIGGRAPH 2023 Talks. https://doi.org/10.1145/3587421.3595466
  5. Philip, S., Moreland, K., & Maynard, R. (2023). VTK-m Accelerated Filters in VTK and ParaView. Kitware Source.
    https://www.kitware.com/vtk-m-accelerated-filters-in-vtk-and-paraview/
  6. Wang, Z., Athawale, T. M., Moreland, K., Chen, J., Johnson, C. R., & Pugmire, D. (2023, May). FunMC2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). https://doi.org/10.2312/pgv.20231081
  7. Carr, H. A., Rübel, O., Weber, G. H., & Ahrens, J. P. (2021). Optimization and Augmentation for Data Parallel Contour Trees. IEEE Transactions on Visualization and Computer Graphics, 28(10), 3471–3485. https://doi.org/10.1109/TVCG.2021.3064385
  8. Farber, R. (2021). ECP Brings Much Needed Visualization Software to Exascale and GPU-Accelerated Systems. ECP Technical Highlights.
    https://www.exascaleproject.org/highlight/ecp-brings-much-needed-visualization-software-to-exascale-and-gpu-accelerated-systems/
  9. Moreland, K., Maynard, R., Pugmire, D., Yenpure, A., Vacanti, A., Larsen, M., & Childs, H. (2021). Minimizing Development Costs for Efficient Many-Core Visualization Using MCD^3. Parallel Computing, 108(102834). https://doi.org/10.1016/j.parco.2021.102834
  10. Pugmire, D., Ross, C., Thompson, N., Kress, J., Atkins, C., Klasky, S., & Geveci, B. (2021). Fides: A General Purpose Data Model Library for Streaming Data. ISC High Performance, 495–507. https://doi.org/https://doi.org/10.1007/978-3-030-90539-2_34
  11. Sane, S., Johnson, C. R., & Childs, H. (2021). Investigating In Situ Reduction via Lagrangian Representations for Cosmology and Seismology Applications. Computational Science – ICCS 2021, 436–450. https://doi.org/10.1007/978-3-030-77961-0_36
    Winner: Best Paper
  12. Sane, S., Yenpure, A., Bujack, R., Larsen, M., Moreland, K., Garth, C., Johnson, C. R., & Childs, H. (2021, June). Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). https://doi.org/10.2312/pgv.20211040
    Winner: Best Paper
  13. Schwartz, S. D., Childs, H., & Pugmire, D. (2021). Machine Learning-Based Autotuning for Parallel Particle Advection. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). https://doi.org/10.2312/pgv.20211039
  14. Lessley, B., Li, S., & Childs, H. (2020). HashFight: A Platform-Portable Hash Table for Multi-Core and Many-Core Architectures. Electronic Imaging, Visualization and Data Analysis, 376–371-376–313(13). https://doi.org/10.2352/ISSN.2470-1173.2020.1.VDA-376
  15. Perciano, T., Heinemann, C., Camp, D., Lessley, B., & Bethel, E. W. (2020). Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives. High Performance Computing, 127–145. https://doi.org/10.1007/978-3-030-50743-5_7
  16. Wang, K.-C., Xu, J., Woodring, J., & Shen, H.-W. (2019). Statistical Super Resolution for Data Analysis and Visualization of Large Scale Cosmological Simulations. IEEE Pacific Visualization Symposium (PacificVis), 303–312. https://doi.org/10.1109/PacificVis.2019.00043
  17. Yenpure, A., Childs, H., & Moreland, K. (2019). Efficient Point Merging Using Data Parallel Techniques. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). https://doi.org/10.2312/pgv.20191112
  18. Lessley, B., Perciano, T., Heinemann, C., Camp, D., Childs, H., & Bethel, E. W. (2018). DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives. Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), 34–44. https://doi.org/10.1109/LDAV.2018.8739239
  19. Pugmire, D., Yenpure, A., Kim, M., Kress, J., Maynard, R., Childs, H., & Hentschel, B. (2018). Performance-Portable Particle Advection with VTK-m. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), 45–55. https://doi.org/10.2312/pgv.20181094
  20. Lessley, B., Moreland, K., Larsen, M., & Childs, H. (2017). Techniques for Data-Parallel Searching for Duplicate Elements. IEEE Symposium on Large Data Analysis and Visualization (LDAV). https://doi.org/10.1109/LDAV.2017.8231845
  21. Lessley, B., Perciano, T., Mathai, M., Childs, H., & Bethel, E. W. (2017). Maximal Clique Enumeration with Data-Parallel Primitives. IEEE Symposium on Large Data Analysis and Visualization (LDAV). https://doi.org/10.1109/LDAV.2017.8231847
  22. Li, S., Marsaglia, N., Chen, V., Sewell, C., Clyne, J., & Childs, H. (2017). Achieving Portable Performance For Wavelet Compression Using Data Parallel Primitives. Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV), 73–81. https://doi.org/10.2312/pgv.20171095
  23. Carr, H., Weber, G., Sewell, C., & Ahrens, J. (2016). Parallel Peak Pruning for Scalable SMP Contour Tree Computation. Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV). https://doi.org/10.1109/LDAV.2016.7874312
  24. Lessley, B., Binyahib, R., Maynard, R., & Childs, H. (2016). External Facelist Calculation with Data-Parallel Primitives. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). https://doi.org/10.2312/pgv.20161178
  25. Larsen, M., Labasan, S., Navrátil, P., Meredith, J., & Childs, H. (2015). Volume Rendering Via Data-Parallel Primitives. Eurographics Symposium on Parallel Graphics and Visualization. https://doi.org/10.2312/pgv.20151155
    (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
  26. Larsen, M., Meredith, J. S., Navratil, P. A., & Childs, H. (2015). Ray Tracing Within a Data Parallel Framework. IEEE Pacific Visualization Symposium (PacificVis), 279–286. https://doi.org/10.1109/PACIFICVIS.2015.7156388
    (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
  27. Moreland, K., Larsen, M., & Childs, H. (2015). Visualization for Exascale: Portable Performance is Critical. Supercomputing Frontiers and Innovations, 2(3). https://doi.org/10.14529/jsfi150306
  28. Schroots, H. A., & Ma, K.-L. (2015). Volume rendering with data parallel visualization frameworks for emerging high performance computing architectures. SIGGRAPH Asia Visualization in High Performance Computing, 3:1–3:4. https://doi.org/10.1145/2818517.2818546
  29. Sewell, C., Lo, L.-ta, Heitmann, K., Habib, S., & Ahrens, J. (2015). Utilizing many-core accelerators for halo and center finding within a cosmology simulation. IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV). https://doi.org/10.1109/LDAV.2015.7348076
    (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
  30. Maynard, R., Moreland, K., Ayachit, U., Geveci, B., & Ma, K.-L. (2013). Optimizing Threshold for Extreme Scale Analysis. Visualization and Data Analysis 2013, Proceedings of SPIE-IS&T Electronic Imaging. https://doi.org/10.1117/12.2007320
    (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)