david w. walker ian j. grimstead cardiff school of computer science [email protected]

1Singapore IHPC, January 2006


David W. WalkerIan J. Grimstead

Cardiff School of Computer [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


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


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


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!


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


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?


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


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...


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


Run UDDI Manager

Interrogating UDDI server,populating tableMachines responding / time-outSort by availability


Create Data Service

Select service

Enter:1/ Instance name,

2/ Instance description,3/ Data bootstrap URL

New service listed

Ready to createActive Client


Active Client

Select interaction

Drag mouse/stylus to activate interaction

(move/rotate/etc)

Can now interact with scene


Create Render Service

Select render serviceConnects to selected

Data ServiceNew instance listed

Ready to create Thin Client


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


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


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


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


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


● 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)


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)


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


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


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


Connection to AccessGrid

RAVE can supply AccessGrid

● Render Server supplies H.261 video feed

● Wide-area distribution of visualization

● Interact with existing clients.


AccessGrid and 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


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


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/


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)

david w. walker ian j. grimstead cardiff school of computer science [email protected]

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