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David W. Walker Ian J. Grimstead Cardiff School of Computer Science [email protected]. RAVE : Resource-Aware Visualization Environment. Presentation Structure. Data Visualization: Pros and Cons A Solution: The RAVE project Demonstration of RAVE How RAVE works Future Work Conclusion. - PowerPoint PPT Presentation
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1Singapore IHPC, January 2006
2Singapore IHPC, January 2006
David W. WalkerIan J. Grimstead
Cardiff School of Computer [email protected]
RAVE:Resource-Aware
Visualization Environment
3Singapore IHPC, January 2006
Presentation Structure
● Data Visualization: Pros and Cons● A Solution: The RAVE project● Demonstration of RAVE● How RAVE works● Future Work● Conclusion
4Singapore IHPC, January 2006
Data Visualization:Simulations
● Test theories without physically building● Cheaper to construct new tests● Can run for long periods without human
intervention● Simulations produce lots of information
● But - hard to understand...Flow ratio Area Segment
23.2 #1 213.2 #34 4
... ... ...
Too much info...
Flow ratio
Sample ASample B
...or too little
5Singapore IHPC, January 2006
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 Research
Generated with IBM Open Visualization Data Explorer
6Singapore IHPC, January 2006
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!
7Singapore IHPC, January 2006
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
8Singapore IHPC, January 2006
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?
9Singapore IHPC, January 2006
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
10Singapore IHPC, January 2006
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...
11Singapore IHPC, January 2006
Demonstration(via Screenshots)
● Recorded demo – screen shots● Resources:
● Windows laptop (thin & active clients, Java)● Remote Linux/Solaris/IRIX servers
● Data servers + Render servers● PDA (thin client, C++/QTopia)
● Used:● WeSC UDDI server● WeSC Service-Orientated Grid
12Singapore IHPC, January 2006
Run UDDI Manager
Interrogating UDDI server,populating tableMachines responding / time-outSort by availability
13Singapore IHPC, January 2006
Create Data Service
Select service
Enter:1/ Instance name,
2/ Instance description,3/ Data bootstrap URL
New service listed
Ready to createActive Client
14Singapore IHPC, January 2006
Active Client
Select interaction
Drag mouse/stylus to activate interaction
(move/rotate/etc)
Can now interact with scene
15Singapore IHPC, January 2006
Create Render Service
Select render serviceConnects to selected
Data ServiceNew instance listed
Ready to create Thin Client
16Singapore IHPC, January 2006
Thin Client
Same GUI asActive Client(Uses WS to
populate menu)
Navigate by dragging in
window(akin to VRML steer mode)We can see the
avatar of theActive Client
17Singapore IHPC, January 2006
Tiled Rendering
Add a tile
1/ UDDI server interrogated2/ Render Service withsame data set discovered3/ Render Service asked to render a tile4/ Active Client continues to render until tile arrives
Remote assistantlisted with FPS
18Singapore IHPC, January 2006
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
19Singapore IHPC, January 2006
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
20Singapore IHPC, January 2006
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
21Singapore IHPC, January 2006
● 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)
22Singapore IHPC, January 2006
Performance / Issues● Performance with Java3D
● NVidia Quadro FX 700 off-screen rendering● ~37 Mpoly/sec with DTI dataset (~950kp)● ~0.8 Mpoly/sec with galleon (~5.5kp)
● Needs high polygon scenes● Waits too long before buffer flip?
● Issues with Java3D● Tricky to release memory● Had to be brave and produce IA64 build● Off-screen rendering requires on-screen
window (IRIX)
23Singapore IHPC, January 2006
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
KnownMachines
Instances onSelected Machine
MachineAttributes/Usage
Create new Instanceon Selected Machine
Render Services(similar to Data Services)
Create Active or Thin Client
24Singapore IHPC, January 2006
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
25Singapore IHPC, January 2006
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
26Singapore IHPC, January 2006
Connection to AccessGrid
RAVE can supply AccessGrid
● Render Server supplies H.261 video feed
● Wide-area distribution of visualization
● Interact with existing clients.
27Singapore IHPC, January 2006
AccessGrid and RAVE
28Singapore IHPC, January 2006
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
29Singapore IHPC, January 2006
Current & Future Work● Data Server stream actions to disk (done)
● Asynchronous collaboration through playback● Automated migration of services
● Implementation of failsafe● Collaboration support
● Gesticulation, data mark-up● Further resource-awareness
● Image compression, data down-sampling● Further investigation of work distribution
● Scene graph distribution
30Singapore IHPC, January 2006
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/
31Singapore IHPC, January 2006
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)