1. Fast Isosurface Visualization on a High-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

7. Scalable low-cost display wall 32-node I/O cluster PCs w/ 3-D accelerators... 64-CPU cluster Commodity projectors 128-CPU Production cluster Extensible Router T1PPPL Wireless links AT&T Lab LAN vBNS

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

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. Runtime architecture databasedisplay network ExtractionCommunicationRendering

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. Runtime architecture databasedisplay network ExtractionCommunicationRendering

22. Runtime architecture databasedisplay network ExtractionCommunicationRendering

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

24. Runtime architecture databasedisplay network ExtractionCommunicationRendering

25. 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