1. Unstructured Data Partitioning for Large Scale Visualization CSCAPES Workshop June, 2008 Kenneth Moreland Sandia National Laboratories Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

2. The Parallel Visualization Pipeline Read Isosurface Reflect Render

3. The Parallel Visualization Pipeline Read Isosurface Reflect Render Read Isosurface Reflect Render Read Isosurface Reflect Render Read Isosurface Reflect Render

4. Data Parallel Pipelines Duplicate pipelines run independently on different partitions of data.

5. Data Parallel Pipelines Duplicate pipelines run independently on different partitions of data.

6. Data Parallel Pipelines Some operations will work regardless. –Example: Clipping.

7. Data Parallel Pipelines Some operations will work regardless. –Example: Clipping.

8. Data Parallel Pipelines Some operations will work regardless. –Example: Clipping.

9. Data Parallel Pipelines Some operations will have problems. –Example: External Faces

10. Data Parallel Pipelines Some operations will have problems. –Example: External Faces

11. Data Parallel Pipelines Ghost cells can solve most of these problems.

12. Data Parallel Pipelines Ghost cells can solve most of these problems.

13. Data Partitioning Partitions should be load balanced and spatially coherent.

14. Data Partitioning Partitions should be load balanced and spatially coherent.

15. Data Partitioning Partitions should be load balanced and spatially coherent.

16. The Parallel Visualization Pipeline Read Isosurface Reflect Render Read Isosurface Reflect Render Read Isosurface Reflect Render Read Isosurface Reflect Render

17. Parallel Rendering

19. Tiled Displays

20. Rendering Translucent Geometry

21. Unstructured Volume Rendering in Parallel

26. Mesh Partitioning

27. Partitioning on Spatial Structure: K-D Tree

28. K-D Trees Provide Query Structures What elements are closest to here?

29. K-D Trees Provide Query Structures What regions / elements intersect this view frustum?

30. K-D Trees Provide Query Structures What is the visibility order of the regions from this viewpoint? 1 23 4 56 7 8

31. Reconstructing Connectivity Information May not be unique. Neighbor info usually missing.

32. Reconstructing Connectivity Information

33. Future Work Code Optimization and Cleanup Integration of other partitioning algorithms. Better Data Type Support. Better Temporal Support.