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 Releases Downloads
- Building VTK-m
- Using VTK-m in Your Project
- Contributing to VTK-m
- VTK-m Mailing List
- VTK-m Users' Guide (Work in progress.)
- Doxygen Documentation
Sandia National Laboratories
On VTK-m itself
"VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures." Kenneth Moreland, Christopher Sewell, William Usher, Li-ta Lo, Jeremy Meredith, David Pugmire, James Kress, Hendrik Schroots, Kwan-Liu Ma, Hank Childs, Matthew Larsen, Chun-Ming Chen, Robert Maynard, and Berk Geveci. IEEE Computer Graphics and Applications, 36(3), May/June 2016. DOI 10.1109/MCG.2016.48.
"Visualization for Exascale: Portable Performance is Critical." Kenneth Moreland, Matthew Larsen, and Hank Childs. Supercomputing Frontiers and Innovations, 2(3), 2015. DOI 10.14529/jsfi150306.
On Visualization Algorithms in VTK-m
"Performance-Portable Particle Advection with VTK-m '." D. Pugmire, A. Yenpure, M. Kim, J. Kress, R. Maynard, H. Childs, and B. Hentschel. In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), pages 45–55, June 2018.
"Maximal Clique Enumeration with Data-Parallel Primitives'." B. Lessley, T. Perciano, M. Mathai, H. Childs, and E. W. Bethel. In Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), pages 16–25, Oct. 2017.
"Achieving Portable Performance For Wavelet Compression Using Data Parallel Primitives'." S. Li, N. Marsaglia, V. Chen, C. Sewell, J. Clyne, and H. Childs. In Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV), pages 73–81, June 2017.
"External Facelist Calculation with Data-Parallel Primitives'." B. Lessley, R. Binyahib, R. Maynard, and H. Childs. In Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV), pages 10–20, June 2016.
"Volume Rendering Via Data-Parallel Primitives'."M. Larsen, S. Labasan, P. Navratil, J. Meredith, and H. Childs. In Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV), pages 53–62, May 2015. (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
"Ray-Tracing Within a Data Parallel Framework'." M. Larsen, J. Meredith, P. Navratil, and H. Childs. In Proceedings of the IEEE Pacific Visualization Symposium, pages 279–286, Apr. 2015. (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)