RAVE: Resource-Aware Visualization Environment Dr. Ian J. Grimstead Prof. Nick J. Avis Prof. David W. Walker Cardiff School of Computer Science Cardiff, Wales, UK

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RAVEResource-AwareVisualization
EnvironmentDr. Ian J. GrimsteadProf. Nick J.
Avis Prof. David W. WalkerCardiff School of
Computer ScienceCardiff, Wales, UK
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Presentation Structure

• Data Visualization Pros and Cons
• A Solution The RAVE project
• Demonstration of RAVE
• How RAVE works
• Latest results
• Conclusion

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Data VisualizationMachine Dependence

• System is often single platform
• Microsoft vs. UNIX vs. Apple Mac vs. ...
• Handheld vs. workstation vs. ...
• Need to buy more copies of the system!

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Data VisualizationMultiple Users

• Hard to collaborate with other users
• Usually must all crowd around one machine
• Unless a large display is available
• One person driving others are passive
• System is not assisting with collaboration

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Data VisualizationSpecialist Equipment

• May require specialist computer
• Capable of displaying complex data
• Prohibitively expensive to own
• User may need to move to machine
• Problem if only one machine
• Overloaded too slow to be usable
• All displays are in use
• What if it breaks?

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Data VisualizationSummary

• Pros
• Can comprehend much more information
• Data is now interactive
• Cons
• Restricted to specific machine/platform
• May require specialist computer
• Hard for users to collaborate

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A SolutionThe RAVE Project

• RAVE supports
• Various types of machine/display
• Immersadesk ? workstation ? PDA
• Multiple machines/resources
• Resource-aware network, machine load
• Multiple users
• Resource sharing
• Collaboration
• RAVE is now demonstrated...

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Demonstration

• Recorded demo
• Resources
• Windows laptop (active clients, Java)
• Remote Linux/Solaris/IRIX servers
• Data servers
• Uses
• WeSC UDDI server
• WeSC Service-Orientated Grid

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Demonstration
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The RAVE ProjectHow it Works

• Each RAVE component now examined
• Data Distribution - Data Server
• Displaying the Data - Active Client
• Lightweight clients - Render Server, Thin Client
• Service Discovery
• Tiled rendering with Active Client
• Remote (dynamic) data feed

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Data Distribution

• First component Data Server
• Acts as a distribution point interpreter
• Understands many types of data
• Uses Java3DXj3D as importer

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Displaying the Data
Isosurface of MRI from Large Geometric Models
Archive (850kpoly, 3 nodes, 19.8Mb raw
data) Bootstrap DS?AC 12.4s
Note Windows XP Diffusion Tensor Imaging, SHEFC
Brain Imaging Research Centre for Scotland,
Martin Connell and Mark Bastin (950kpoly, 2200
nodes, 29.8Mb raw data) Bootstrap DS?AC 20.9s
Geology dataset (10 minute ETOPO from National
Geophysical Data Center (4.6Mpoly, 3 nodes,
109.6Mb raw data) Bootstrap DS?AC 48.3s

• Second component Active RAVE Client
• Active facilities to draw on its own
• Accepts feed from Data Server
• Presents images of data to user

Active RAVE Client
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Lightweight Clients
MolScript VRML of 1PRC molecule (Research
Collaboratory for Structural Bioinformatics
Protein Data Bank) (546kpoly, 29,000 nodes,
23.2Mb raw data) 96.5s DS?RS ( nodes) 3.2fps _at_
400x400 (11Mbit shared wireless)
Isosurface of MRI scan Large Geometric Models
Archive (850kpoly, 3 nodes, 3.2fps _at_ 400x400
11Mbit wireless)

• Third component the Render Server
• Drawn visual sent to Thin RAVE Clients
• Thin-insufficient power/resources to draw data

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Service Discovery

• Servers are advertised on the network
• Using standardised methods
• UDDI, Grid/Web Services
• We can reuse the work of other people
• UDDI4J, Apache Axis, Globus
• Human user can see list of servers
• Select most appropriate one
• Consider speed, memory, bandwidth...
• May already have your required data on it
• Or automatically select with a heuristic

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Remote, Dynamic Data

• Independent simulation can supply Data Server
• Simulation code instrumented
• Transmits scene creation to Data Server
• Subsequent updates also sent
• Data Server reflects updates
• Multiple clients can view live simulation

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Tiled Rendering

• If your machine can nearly cope
• Request assistance from a Render Service
• Automatically select RS with heuristic
• Locally render subset (tile) of data
• Remainder rendered by Render Server

Visualization Data
Active Client
Data Server
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Tiled RenderingLatest Results
Tiling advantage _at_ 600kv?
Perfectly tri-stripped
29,000 nodes 2.2 vp
1.3 vp
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Tiled RenderingDiscussion

• Is it worth it?
• Only in specific circumstances
• When GPU fillrate is local bottleneck
• TL constant between 50 and 100
• Sufficient network bandwidth available
• Examples
• Hand dataset perfectly tristripped
• GPU TL not bottleneck ? 200 speedup
• 1PRC hardly tristripped (2.2 verts/poly)
• GPU TL bottleneck ? 20 slowdown

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RAVE Summary

• Data Server reads data and distributes
• Active Client renders locally
• Thin Client renders via Render Server
• Active Client may request assistance
• All resources shared where possible
• Uses Java to support (most) platforms

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Conclusion

• Visualization great!
• But requires specialist hardware or software
• Often not designed for multiple users
• Solution - RAVE
• Utilise any available machines/resources
• Collaborative work from your desk
• Further information
• http//www.wesc.ac.uk/projectsite/rave/

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Acknowledgements

• Project funding UK DTI SGI
• Diffuse Tensor Imaging dataset
• Martin Connell and Mark Bastin, SHEFC Brain
  Imaging Research Centre for Scotland
• Molecule geometry
• Research Collaboratory for Structural
  Bioinformatics Protein Data Bank, using MolScript
• Skeletal hand
• Large Geometric Models Archive, Georgia Institute
  of Technology
• ETOPO dataset
• National Geophysical Data Center (NGDC)