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Fast Isosurface Visualization on a High-Resolution Scalable Display Wall

Fast Isosurface Visualization on a High-Resolution Scalable Display Wall. Adam Finkelstein Allison Klein Kai Li Princeton University Sponsors: DOE, Intel, NSF. Overview. The display wall environment Motivation Challenges Isosurfaces on the display wall Extraction Rendering

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Fast Isosurface Visualization on a High-Resolution Scalable Display Wall

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  1. Fast Isosurface Visualization on aHigh-Resolution Scalable Display Wall Adam Finkelstein Allison Klein Kai Li Princeton University Sponsors: DOE, Intel, NSF

  2. Overview • The display wall environment • Motivation • Challenges • Isosurfaces on the display wall • Extraction • Rendering • Future directions

  3. Some snapshots

  4. Some snapshots

  5. Some snapshots

  6. Some snapshots

  7. Scalable low-cost display wall 128-CPU Production cluster Commodity projectors 64-CPU cluster Extensible Router PCs w/ 3-D accelerators 32-node I/O cluster ... Wireless links PPPL T1 LAN vBNS AT&T Lab

  8. CPU and graphics hardware performance memory density, disk density (50-60%/year) display resolution (< 5%/year) Time Technology trends

  9. Scalable low-cost display wall • Now: • 8’ × 18’ rear-projection screen • 8 polysilicon LCD projectors deliver 6 million pixels per frame (4096 x 1536) • A network (Myrinet) of 15 Pentium-II 450Mhz (8 have Intergraph graphics accelerators) • Soon: • 15 new-generation projectors will deliver 20 million pixels per frame (6400 x 3072) • A network of new-generation PCs with new-generation 3D graphics accelerators

  10. Multi-projector displays • SGI-based displays • Government labs:ANL, LANL, LLNL, Sandia • Industry: AT&T, Panoram Tech, Trimension, ... • Universities:Minnesota, Stanford, UI Chicago, UNC • PC-based displays • Princeton, Intel, ANL • Next: Illinois, LLNL, Sandia, Lucent, ...

  11. First Video

  12. Research challenges • Parallel rendering • Fast communication • Seamless imaging • Interaction techniques • Spatialized sound • Virtual environments • Visualization systems

  13. Visualization of isosurfaces

  14. Goals • Large data sets • Visible woman • Astrophysical simulations • Large display • Inexpensive • High resolution • Interactive rates • Extraction • Rendering

  15. Runtime components • Extraction Find voxels containing the isosurface. • Communication Send surface information to display. • Rendering Draw the surface.

  16. database display Runtime architecture Extraction Communication Rendering network

  17. Extraction on one processor • Acceleration methods [Cignoni97]: • Spatial -- e.g. octree [Parker,Shen] • Seed -- e.g. seed and traverse [Bajaj] • Value -- e.g. interval tree [Cignoni]

  18. Extraction on one processor • Acceleration methods [Cignoni97]: • Spatial -- e.g. octree [Shen] • Seed -- e.g. seed and traverse [Bajaj] • Value -- e.g. interval tree [Cignoni] • We use filtering search [Chazelle86]

  19. Filtering search • 0.00  • 0.12  • 0.38  • 0.57  • 0.61  • 0.78  • 0.93 

  20. Benefits of filtering search • Nice space / time tradeoff • Better asymptotic worst case • Very easy to code • Trivially parallelizeable

  21. database display Runtime architecture Extraction Communication Rendering network

  22. database display Runtime architecture Extraction Communication Rendering network

  23. Communication • Gigabit network (Myrinet) • Scalable • Virtual memory mapped communication • Currently we ship voxels: • voxel ID • marching cube case • edge interpolants

  24. database display Runtime architecture Extraction Communication Rendering network

  25. Rendering • Rely on PC graphics cards • Static screen-space partitioning • Current bottleneck • Edge blending

  26. Second Video

  27. How do we make it faster? • Rendering: • Next generation of graphics cards • Load balancing • General: • Surface simplification • Multiresolution representations

  28. Broader directions • Other vis techniques • Remote visualization • Compression • Networking: PPPL, AT&T, CorridorOne • Scalable storage server • 3 TB storage • 1.5 GB / sec • Intelligent caching • $150K

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