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1 AHM September 2005. 2 RAVE: Resource-Aware Visualization Environment Dr. Ian J. Grimstead Prof. Nick J. Avis Prof. David W. Walker Cardiff School of.

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Presentation on theme: "1 AHM September 2005. 2 RAVE: Resource-Aware Visualization Environment Dr. Ian J. Grimstead Prof. Nick J. Avis Prof. David W. Walker Cardiff School of."— Presentation transcript:

1 1 AHM September 2005

2 2 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

3 3 AHM September 2005 Presentation Structure ● Data Visualization: Pros and Cons ● A Solution: The RAVE project ● Demonstration of RAVE ● How RAVE works ● Latest results ● Conclusion

4 4 AHM 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... Too much info......or too little

5 5 AHM 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 interactive Image courtesy of IBM Research Generated with IBM Open Visualization Data Explorer

6 6 AHM 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!

7 7 AHM 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

8 8 AHM 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?

9 9 AHM 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

10 10 AHM 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...

11 11 AHM 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

12 12 AHM September 2005 Demonstration

13 13 AHM 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

14 14 AHM 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 Data Server Internet or remote machine Visualization Data RAVE Client RAVE Client RAVE Client

15 15 AHM 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 Visualization Data Server 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 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

16 16 AHM 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 Data Server Thin Client Visualization Data Render Server Visual drawn off-screen (hidden) Isosurface of MRI scan Large Geometric Models Archive (~850kpoly, 3 nodes, 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) 400x400 (11Mbit shared wireless)

17 17 AHM 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

18 18 AHM 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

19 19 AHM 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 Data Server Drawn Visual Render Server Drawn Visual Render Server Active Client UDDI Server Available RS Search for RS

20 20 AHM September 2005 Tiled Rendering: Latest Results Tiling 600kv? Perfectly tri- stripped ~29,000 nodes; ~2.2 v:p ~1.3 v:p

21 21 AHM 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

22 22 AHM 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

23 23 AHM 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: ●

24 24 AHM 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)


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