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

SPU 2CASC

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

SPU 3CASC

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?

SPU 4CASC

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

SPU 5CASC

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

SPU 6CASC

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:

–Haptic Head Tracking Hand Tracking–Indirect Manipulation Command Line

— By Individual and Expectation

SPU 7CASC

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.

SPU 8CASC

Alternatives: Distribute Images … Cleverly

Smaller Windows— or lower resolution

Generic Compression (example)— Processing Overhead at BOTH ENDS

SPU 9CASC

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

SPU 10CASC

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

SPU 11CASC

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

SPU 12CASC

Alternatives: Distribute Geometry and App Data

Local Control of View

Local Color Mapping ...

Local Quantitative Querying ...

BUT MORE DATA - Impacting Both Time and Storage

SPU 13CASC

Alternatives: Distribute SOME Geometry

View Dependence— View Culling— Level-of-Detail— Occlusion Culling

Progressive

Interruptable

SPU 14CASC

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

SPU 15CASC

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

SPU 16CASC

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

SPU 17CASC

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 ?

SPU 18CASC

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