A toolkit of scientific visualization algorithms for emerging processor architectures.

Image Credit: Matthew Larsen, LLNL. This image is of an idealized Inertial Confinement Fusion (ICF) simulation of a Rayleigh-Taylor instability with two fluids mixing in a spherical geometry.

Getting started

Download the latest release and then follow the instructions. If you're not already a VTK user and want to learn more about the original toolkit, check it out.

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What is VTK-m?

One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential.

VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

VTK-m Publications

Please use the first paper when referencing VTK-m in scientific publications.

bib file

  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. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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.)
  15. 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.)
  16. 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
  17. 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
  18. 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.)
  19. 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.)