Transcript Slide 1

Singapore IHPC, January 2006
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RAVE:
Resource-Aware
Visualization Environment
David W. Walker
Ian J. Grimstead
Cardiff School of Computer Science
[email protected]
Singapore IHPC, January 2006
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Presentation Structure
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Data Visualization: Pros and Cons
A Solution: The RAVE project
Demonstration of RAVE
How RAVE works
Future Work
Conclusion
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Data Visualization:
Simulations
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Test theories without physically building
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Cheaper to construct new tests
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Can run for long periods without human intervention
Simulations produce lots of information
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But - hard to understand...
Flow ratio
Flow ratio Area Segment
23.2
#1
2
13.2 #34
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...
...
...
Too much info...
Singapore IHPC, January 2006
Sample
A
Sample
B
...or too little
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Data Visualization:
Comprehension
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Solution–graphical visualization of data
View a model of the data, not the data
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Massachusetts Bay
Colours, contours,...
Easier to comprehend
Data is now
interactive
Image courtesy of IBM Research
Generated with IBM Open
Visualization Data Explorer
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Data Visualization:
Machine Dependence
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System is often single platform
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Microsoft vs. UNIX vs. Apple Mac vs. ...
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Handheld vs. workstation vs. ...
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Need to buy more copies of the system!
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Data Visualization:
Multiple Users
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Hard to collaborate with other users
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Usually – must all crowd around one machine
● Unless a large display is available
One person “driving” – others are passive
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System is not assisting with collaboration
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Data Visualization:
Specialist Equipment
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May require specialist computer
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Capable of displaying complex data
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Prohibitively expensive to own
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User may need to move to machine
Problem if only one machine
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Overloaded – too slow to be usable
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All displays are in use
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What if it breaks?
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Data Visualization:
Summary
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Pros:
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Can comprehend much more information
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Data is now interactive
Cons:
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Restricted to specific machine/platform
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May require specialist computer
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Hard for users to collaborate
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A Solution:
The RAVE Project
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RAVE supports:
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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
(via Screenshots)
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Recorded demo – screen shots
Resources:
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Windows laptop (thin & active clients, Java)
Remote Linux/Solaris/IRIX servers
● Data servers + Render servers
PDA (thin client, C++/QTopia)
Used:
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WeSC UDDI server
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WeSC Service-Orientated Grid
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Run UDDI Manager
Int errogat ing UDDI server,
Sort
by availabilit
y
Machines
responding
populat
ing t able/ t im e-out
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Create Data Service
Select service
New service list ed
Ent er:
Ready t o creat e
Act iv e Clie n t 1/ Inst ance nam e,
2/ Inst ance descript ion,
3/ Dat a boot st rap URL
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Active Client
Can now interact
with scene
Select interaction
Drag mouse/stylus to
activate interaction
(move/rotate/etc)
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Create Render Service
Select render service
New inst ance list ed
Connect s t o select ed
D a t a Se r vice
Ready t o creat e Th in Clie n t
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Thin Client
Sam e GUI as
Act ive Clie n t
(Uses WS t o
populat e m enu)
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Navigat e by
dragging in
window
(akin t o VRML
st eer m ode)
We can see t he
avat ar of t he
Act ive Clie n t
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Tiled Rendering
1/ UDDI server int errogat ed
2/ Re nde r Se r vice wit h
sam e dat a set discovered
3/ Re nde r Se rvice asked
t o render a t ile
4/ Act ive Clie nt cont inues
t o render unt il t ile arrives
Rem ot e assist ant
list ed wit h FPS
Add a t ile
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The RAVE Project:
How it Works
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Each RAVE component now examined:
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Data Distribution – Data Server
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Displaying the Data – Active Client
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Lightweight clients – Render Server, Thin Client
Service Discovery
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Tiled rendering with Active Client
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Remote (dynamic) data feed
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Data Distribution
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First component: Data Server
Acts as a distribution point & interpreter
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Understands many types of data
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Uses Java3D+Xj3D as importer
RAVE
Client
Internet
or remote
machine
Data to be
visualised
Visualization
Data
Data
Server
Singapore IHPC, January 2006
RAVE
Client
RAVE
Client
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Isosurface
Geology
Note:dataset
of
Windows
MRI (10
from
XP
minute
Large
Geometric
Diffusion
ETOPOTensor
from
Models
National
Imaging,
Archive
(~850kpoly,
Geophysical
SHEFC Brain
3 nodes,
Data
Imaging
Center
19.8Mb
(~4.6Mpoly,
Research Centre
raw
3 nodes,
data)
for Scotland,
109.6Mb
Second component: Active
RAVE
Client
Bootstrap
DS→AC:
12.4s
Martin
Connell
raw
data)
and Mark
● “Active” – facilities to draw on
its own(~950kpoly,
Bootstrap
DS→AC: 2200
48.3s
Bastin
nodes, 29.8Mb raw data)
Accepts feed from Data Server
Bootstrap DS→AC: 20.9s
Displaying the Data
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Presents images of data to user
Visual drawn on
local machine
Visualization
Data
Data
Server
Singapore IHPC, January 2006
Active RAVE
Client
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MolScriptofVRML
of 1PRC
Isosurface
MRI scan
Large
molecule
(Research
Geometric
Models
Archive
Collaboratory
for Structural
(~850kpoly,
3 nodes,
3.2fps @
Bioinformatics
– Protein
Data
400x400 11Mbit
wireless)
Bank)
Third component: the Render
Server
(~546kpoly,
29,000 nodes,
23.2MbClients
raw data)
Drawn visual sent to Thin RAVE
96.5s DS→RS (# nodes)
● “Thin”-insufficient power/resources to draw data
3.2fps @ 400x400
drawn
(11MbitVisual
shared
wireless)
Lightweight Clients
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off-screen (hidden)
Visual
Visualization
Data
Interaction
Data
Server
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Render
Server
Thin
Client
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Performance / Issues
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Performance with Java3D
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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
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Tricky to release memory
Had to be brave and produce IA64 build
Off-screen rendering requires on-screen window
(IRIX)
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Service Discovery
Machine
At t ribut es/Usage
Known
● Servers are “advertised” on
Machines
● Using standardised methods
● UDDI, Grid/Web Services
the network
We can reuse the work of other people
Inst ances on
● UDDI4J, Apache Axis, Globus
Select ed Machine
Creat e new Inst ance
● Human user can see list of servers
on Select ed Machine
● Select most appropriate one
Creat e Act iv e
Consider
speed, memory, bandwidth...
or Th in ●Clie
nt
● May already have your required data on it
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d e r Se r v ice s
Or automatically select withRe
a nheuristic
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(sim ilar t o D a t a Se r v ice 23
s)
Tiled Rendering
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If your machine can nearly cope:
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Request assistance from a Render Service
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Automatically select RS with heuristic
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Locally render subset (tile) of data
UDDI
Remainder rendered by
Render Server
Search
Server
Visualization
Data
Data
Server
Singapore IHPC, January 2006
for RS
Drawn
Visual
Available
RS
Render Server
Drawn
Visual
Render Server
Active
Client
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Remote, Dynamic Data
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Independent simulation can
supply Data Server
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Simulation code instrumented
Transmits scene creation to Data
Server
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Subsequent updates also sent
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Data Server reflects updates
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Multiple clients can view live simulation
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Connection
to AccessGrid
RAVE can supply AccessGrid
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Render Server supplies H.261
video feed
Wide-area distribution of
visualization
Interact with existing clients.
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AccessGrid and RAVE
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Summary
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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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Current & Future Work
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Data Server stream actions to disk (done)
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Automated migration of services
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Gesticulation, data mark-up
Further resource-awareness
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Implementation of failsafe
Collaboration support
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Asynchronous collaboration through playback
Image compression, data down-sampling
Further investigation of work distribution
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Scene graph distribution
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Conclusion
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Visualization – great!
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But requires specialist hardware or software
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Often not designed for multiple users
Solution - “RAVE”
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Utilise any available machines/resources
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Collaborative – work from your desk
Further information:
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http://www.wesc.ac.uk/projectsite/rave/
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Acknowledgements
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Project funding: UK DTI & SGI
Diffuse Tensor Imaging dataset:
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Molecule geometry:
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Research Collaboratory for Structural Bioinformatics
Protein Data Bank, using MolScript
Skeletal hand:
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Martin Connell and Mark Bastin, SHEFC Brain Imaging
Research Centre for Scotland
Large Geometric Models Archive, Georgia Institute of
Technology
ETOPO dataset:
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National Geophysical Data Center (NGDC)
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