Difference between revisions of "Main Page"
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=== On Visualization Algorithms in VTK-m === | === On Visualization Algorithms in VTK-m === | ||
| − | Particle advection: "'[http://cdux.cs.uoregon.edu/pubs/PugmireEGPGV.pdf 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. | + | Particle advection: "''[http://cdux.cs.uoregon.edu/pubs/PugmireEGPGV.pdf 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 cliques on graphs: "'[http://cdux.cs.uoregon.edu/pubs/LessleyLDAV2.pdf 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. | + | Maximal cliques on graphs: "''[http://cdux.cs.uoregon.edu/pubs/LessleyLDAV2.pdf 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. |
== For Developers == | == For Developers == | ||
Revision as of 12:27, 25 July 2018
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
- Using VTK-m in Your Project
- Contributing to VTK-m
- VTK-m Mailing List
- VTK-m Users' Guide (Work in progress.)
- Doxygen Documentation
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
Particle advection: "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 cliques on graphs: "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.
