1 / 30

Unstructured Data Partitioning for Large Scale Visualization

Unstructured Data Partitioning for Large Scale Visualization. CSCAPES Workshop June, 2008 Kenneth Moreland Sandia National Laboratories.

odysseus-grimes
Download Presentation

Unstructured Data Partitioning for Large Scale Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. Read Isosurface Reflect Render The Parallel Visualization Pipeline

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

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

  14. Parallel Rendering

  15. Parallel Rendering

  16. Tiled Displays

  17. Rendering Translucent Geometry

  18. Unstructured Volume Rendering in Parallel

  19. Unstructured Volume Rendering in Parallel

  20. Unstructured Volume Rendering in Parallel

  21. Unstructured Volume Rendering in Parallel

  22. Unstructured Volume Rendering in Parallel

  23. Mesh Partitioning

  24. Partitioning on Spatial Structure: K-D Tree

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

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

  27. K-D Trees Provide Query Structures 8 4 What is the visibility order of the regions from this viewpoint? 7 1 5 6 2 3

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

  29. Reconstructing Connectivity Information

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

More Related