1. Sam Uselton Center for Applied Scientific Computing Lawrence Livermore National Lab October 25, 2001 Challenges for Remote Visualization: Remote Viz Is Really Large Data Viz

2. SPU 2 CASC Remote Viz == Large Data Viz The Real Problem is TIME, not Distance. Large : Defined Relative to Available Resources —Data Size vs Bandwidth —Data Size vs Memory —Data Size vs Storage Examples of “TOO MUCH” —56Kb Modem vs 10’s of MegaBytes —10Mb EtherNet vs GigaBytes —Gigabit EtherNet vs 100’s of GigaBytes

3. SPU 3 CASC Some Issues are NOT Visualization Specific How are Remote Sites Accessed? —Find Relevant Data?Same. —Demonstrate Authorization?Same. —Access Content?Same? Can Security Be Guaranteed? —Same Security Requirements? —Implementation Issues?

4. SPU 4 CASC Visualization Activity : A Model Get Data —Find —Demonstrate Authorization —Select / Extract / Derive Data Describe Visualization —Mapping to Geometric, Visual and Other Attributes —Scene, Viewing and Rendering Attributes Generate Images

5. SPU 5 CASC Visualization Activity : A Model Get Data —Find —Demonstrate Authorization —Select / Extract / Derive Data Describe Visualization —Mapping to Geometric, Visual and Other Attributes —Scene, Viewing and Rendering Attributes Generate Images Interaction: —Mapping Controls to Dynamic Attributes —Manipulate Controls

6. SPU 6 CASC Remote Exploration is Harder Than Remote Presentation Exploration Requires Interactive Choices Interactions Affected By Latency (AND Bandwidth) —Time (of course) —Consistency (!) —Multiple Times Variability of Impact —By Interaction Mode: –HapticHead TrackingHand Tracking –Indirect ManipulationCommand Line —By Individual and Expectation

7. SPU 7 CASC Alternatives: Distribute Images Direct Approach: —Fixed Bandwidth Requirement (Good) —High Bandwidth Requirement (BAD) MegaPixel Workstation —1M pixels x 3 Bytes x 60 hz = 180 MB / sec IBM T220 High Resolution LCD (or a Tiled Display) —9M pixels x 3 Bytes x 30 hz = 810 MB /sec —Large Tiled Displays too.

8. SPU 8 CASC Alternatives: Distribute Images … Cleverly Smaller Windows —or lower resolution Generic Compression ( example) —Processing Overhead at BOTH ENDS

9. SPU 9 CASC Alternatives: Distribute Images … Cleverly Application Specific Compression (examples) —Better Compression (Sometimes) —Less Overhead (Sometimes) Batch Mode: Make Movies, ftp, then Play Locally —OR … Make CDs and Ship

10. SPU 10 CASC Alternatives: Distribute the Data Large Data Means Long Delay —Increasing Chances of Failures Large Data May Exceed Local Resources —Memory, Storage, … —… and Wasteful When Some (Most?) Data Is Not Used Batch Mode: Make TarBalls, ftp, then Play Locally —OR … Make CDs and Ship

11. SPU 11 CASC Alternatives: Distribute Graphics Information Geometry, Colors, Textures, … Local Control of View —Solves Latency Problem for Viewing Changes Render Using Local Hardware —Fast and Cheap —Appropriate for Local Display MAY Use Less Total Bandwidth, But Slower Starting

12. SPU 12 CASC Alternatives: Distribute Geometry and App Data Local Control of View Local Color Mapping... Local Quantitative Querying... BUT MORE DATA - Impacting Both Time and Storage

13. SPU 13 CASC Alternatives: Distribute SOME Geometry View Dependence —View Culling —Level-of-Detail —Occlusion Culling Progressive Interruptable

14. SPU 14 CASC Alternatives: Distribute Some DATA View Dependent & Progressive —Trickier: Some Sort-First Processing Extract Geometry Locally —Lower Latency for Changing Geometry (Good) —Heavier Processing Load at Lighter Resource (Bad) Interruptable

15. SPU 15 CASC Alternatives: What Works Best ? It Depends !! Time varying data, Data ”Over There" vs Data ”All Around Me" Dynamic View vs Dynamic Parameter Mapping vs Dynamic Geometry Selection Systems Should Support Multiple Alternatives

16. SPU 16 CASC Comments On “Immersion” Dynamic Head Tracking Controlling View of Scene Powerful Qualitative Impact on Viewer (Good) Stringent Latency Demands, Double Images (BAD) Very Useful for Training and Planning Less Important for Analytical Tasks

17. SPU 17 CASC Comments On Collaboration Group Activity Models: —Presenter(s) and Audience —Simultaneous Independent Activities —Tightly Coordinated Joint Tasks —Asynchronous Activities Which Modes Are Needed For Particular Uses? How to Move Between Models ? —How to Decide ? —How to Indicate ?

18. SPU 18 CASC Acknowledgements David Metz and KGO-TV for Video of the 2001 San Francisco Grand Prix Bicycle Race. ASCI VIEWS, especially the LLNL visualization team. This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48. UCRL - PRES-144889