Difference between revisions of "Main Page"
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* [[VTK-m Releases|VTK-m Releases Downloads]] | * [[VTK-m Releases|VTK-m Releases Downloads]] | ||
| − | * [ | + | * [https://gitlab.kitware.com/vtk/vtk-m/blob/master/README.md#building Building VTK-m] |
| − | * [ | + | * [https://gitlab.kitware.com/vtk/vtk-m/blob/master/README.md#dependencies Software Dependencies] |
* [https://gitlab.kitware.com/vtk/vtk-m/blob/master/CONTRIBUTING.md Contributing to VTK-m] | * [https://gitlab.kitware.com/vtk/vtk-m/blob/master/CONTRIBUTING.md Contributing to VTK-m] | ||
* [http://vtk.org/mailman/listinfo/vtkm VTK-m Mailing List] | * [http://vtk.org/mailman/listinfo/vtkm VTK-m Mailing List] | ||
Revision as of 12:30, 17 July 2019
| For information on our upcoming Hackathon, see Hackathon 2019-07-30 for logistical information and the working Wiki page. |
Contents
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.
Using VTK-m
- VTK-m Releases Downloads
- Building VTK-m
- Software Dependencies
- Contributing to VTK-m
- VTK-m Mailing List
- VTK-m Users' Guide
- Doxygen Documentation
- Tutorial (under development!)
Contacts
Kenneth Moreland
Sandia National Laboratories
kmorel@sandia.gov
Publications
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.
"Techniques for Data-Parallel Searching for Duplicate Elements." Brenton Lessley, Kenneth Moreland, Matthew Larsen, and Hank Childs. In IEEE Symposium on Large Data Analysis and Visualization (LDAV), October 2017. DOI 10.1109/LDAV.2017.8231845.
"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.
"Parallel Peak Pruning for Scalable SMP Contour Tree Computation." Hamish A. Carr, Gunther H. Weber, Christopher M. Sewell, James P. Ahrens. In IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), October 2016. DOI 10.1109/LDAV.2016.7874312.
"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 with Data Parallel Visualization Frameworks for Emerging High Performance Computing Architectures." Hendrik Schroots and Kwan-Liu Ma, In In Proceedings of Symposium on Visualization in High Performance Computing, November 2015. DOI 10.1145/2818517.2818546.
"Utilizing Many-Core Accelerators for Halo and Center Finding within a Cosmology Simulation." Christopher Sewell, Li-ta Lo, Katrin Heitmann, Salman Habib, James Ahrens. In IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), October 2015. DOI 10.1109/LDAV.2015.7348076. (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
"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.)
"Optimizing Threshold for Extreme Scale Analysis." Robert Maynard, Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, and Kwan-Liu Ma. In Proceedings of SPIE Visualization and Data Analysis, February 2013. (Note: work initially performed in predecessor framework to VTK-m, and was subsequently ported to VTK-m.)
