1AHM September 2005

2AHM September 2005

RAVE:Resource-Aware

Visualization Environment

Dr. Ian J. GrimsteadProf. Nick J. Avis

Prof. David W. Walker

Cardiff School of Computer Science

Cardiff, Wales, UK

3AHM September 2005

Presentation Structure

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

4AHM September 2005

Data Visualization:Simulations

● Test theories without physically building

● Cheaper to construct new tests● Can run overnight without human intervention

● Simulations produce lots of information● But - hard to understand...

Flow ratio Area Segment23.2 #1 213.2 #34 4

... ... ...

Too much info...

Flow ratio

Sample ASample B

...or too little

5AHM September 2005

Data Visualization:Comprehension

● Solution–graphical visualization of data● View a model of the data, not the data

● Massachusetts Bay● Colours, contours,...● Easier to

comprehend● Data is now

interactiveImage courtesy of IBM ResearchGenerated with IBM Open Visualization Data Explorer

6AHM September 2005

Data Visualization:Machine 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!

7AHM September 2005

Data Visualization:Multiple 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

8AHM September 2005

Data Visualization:Specialist 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?

9AHM September 2005

Data Visualization:Summary

● 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

10AHM September 2005

A Solution:The 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...

11AHM September 2005

Demonstration

● Recorded demo● Resources:

● Windows laptop (active clients, Java)● Remote Linux/Solaris/IRIX servers

● Data servers● Uses:

● WeSC UDDI server● WeSC Service-Orientated Grid

12AHM September 2005

Demonstration

13AHM September 2005

The RAVE Project:How 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

14AHM September 2005

Data Distribution● First component: Data Server● Acts as a distribution point & interpreter

● Understands many types of data● Uses Java3D+Xj3D as importer

Data to be visualised

DataServer

Internetor remote machine

VisualizationData

RAVEClient

RAVEClient

RAVEClient

15AHM September 2005

Displaying the Data● Second component: Active RAVE Client

● “Active” – facilities to draw on its own● Accepts feed from Data Server● Presents images of data to user

VisualizationData

DataServer

Active RAVE Client

Visual drawn on local machine

Isosurface of MRI from Large Geometric Models Archive (~850kpoly, 3

nodes, 19.8Mb raw data)Bootstrap DS→AC: 12.4s

Note: Windows XPDiffusion 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

16AHM September 2005

● Third component: the Render Server● Drawn visual sent to Thin RAVE Clients

● “Thin”-insufficient power/resources to draw data

Interaction

Visual

Lightweight Clients

DataServer

Thin Client

VisualizationData

RenderServer

Visual drawnoff-screen (hidden)

Isosurface of MRI scan Large Geometric Models Archive (~850kpoly, 3

nodes, 3.2fps @ 400x400 11Mbit wireless)

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 @ 400x400 (11Mbit shared wireless)

17AHM September 2005

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

18AHM September 2005

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

19AHM September 2005

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

DataServer

DrawnVisual

Render Server

DrawnVisual

Render Server

Active Client

UDDIServer

Available RS

Searchfor RS

20AHM September 2005

Tiled Rendering:Latest ResultsFPS of On-Screen (GeForce Go!) 420

vs Off-Screen (GeForce 5200)

1.0

10.0

100.0

1000.0

0 200 400 600 800 1,000 1,200 1,400

# 1,000 Vertices

Lo

g1

0 F

PS

100% on-screen

50% on-screen

50% off-screenGalleon

Elle

SkinHand

Frog

DTI

1PRC

Tiling advantag

e@ 600kv?Perfectly tri-

stripped

~29,000 nodes;

~2.2 v:p

~1.3 v:p

21AHM September 2005

Tiled Rendering:Discussion

● Is it worth it?● Only in specific circumstances:● When GPU fillrate is local bottleneck● T&L constant between 50% and 100%● Sufficient network bandwidth available

● Examples:● Hand dataset – perfectly tristripped

● GPU T&L not bottleneck 200% speedup● 1PRC – hardly tristripped (2.2 verts/poly)

● GPU T&L bottleneck 20% slowdown

22AHM September 2005

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

23AHM September 2005

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/

24AHM September 2005

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)