Post on 23-Jan-2016
description
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Fast Isosurface Visualization on aHigh-Resolution Scalable Display Wall
Adam Finkelstein
Allison Klein
Kai Li
Princeton University
Sponsors: DOE, Intel, NSF
Overview
The display wall environmentMotivationChallenges
Isosurfaces on the display wallExtractionRenderingFuture directions
Some snapshots
Some snapshots
Some snapshots
Some snapshots
Scalable low-cost display wall
32-node I/O cluster PCs w/3-D accelerators
...
64-CPU cluster
Commodityprojectors
128-CPUProduction cluster
ExtensibleRouter
T1PPPL
Wireless links
AT&T LabLAN vBNS
Technology trends
display resolution (< 5%/year)
Time
CPU and graphics hardware perfo
rmance
memory density, disk
density (5
0-60%/year)
Scalable low-cost display wall
Now:8’ × 18’ rear-projection screen8 polysilicon LCD projectors deliver
6 million pixels per frame (4096 x 1536)A network (Myrinet) of 15 Pentium-II 450Mhz
(8 have Intergraph graphics accelerators)
Soon:15 new-generation projectors will deliver
20 million pixels per frame (6400 x 3072)A network of new-generation PCs with
new-generation 3D graphics accelerators
Multi-projector displays
SGI-based displaysGovernment labs:
ANL, LANL, LLNL, Sandia Industry:
AT&T, Panoram Tech, Trimension, ...Universities:
Minnesota, Stanford, UI Chicago, UNC
PC-based displaysPrinceton, Intel, ANLNext: Illinois, LLNL, Sandia, Lucent, ...
First Video
Research challenges
Parallel rendering Fast communication Seamless imaging Interaction techniques Spatialized sound Virtual environments Visualization systems
Visualization of isosurfaces
Goals
Large data setsVisible womanAstrophysical simulations
Large display InexpensiveHigh resolution
Interactive ratesExtractionRendering
Runtime components
Extraction
Find voxels containing the isosurface.
Communication
Send surface information to display.
Rendering
Draw the surface.
Runtime architecture
database display
network
Extraction Communication Rendering
Extraction on one processor
Acceleration methods [Cignoni97]:
Spatial -- e.g. octree [Parker,Shen]Seed -- e.g. seed and traverse [Bajaj]Value -- e.g. interval tree [Cignoni]
Extraction on one processor
Acceleration methods [Cignoni97]:
Spatial -- e.g. octree [Shen]Seed -- e.g. seed and traverse [Bajaj]Value -- e.g. interval tree [Cignoni]
–We use filtering search [Chazelle86]
Filtering search
0.00
0.12
0.38
0.57
0.61
0.78
0.93
Benefits of filtering search
Nice space / time tradeoff
Better asymptotic worst case
Very easy to code
Trivially parallelizeable
Runtime architecture
database display
network
Extraction Communication Rendering
Runtime architecture
database display
network
Extraction Communication Rendering
Communication
Gigabit network (Myrinet)
Scalable
Virtual memory mapped communication
Currently we ship voxels:voxel IDmarching cube caseedge interpolants
Runtime architecture
database display
network
Extraction Communication Rendering
Rendering
Rely on PC graphics cards
Static screen-space partitioning
Current bottleneck
Edge blending
Second Video
How do we make it faster?
Rendering:Next generation of graphics cardsLoad balancing
General:Surface simplificationMultiresolution representations
Broader directions
Other vis techniques
Remote visualizationCompressionNetworking: PPPL, AT&T, CorridorOne
Scalable storage server3 TB storage1.5 GB / sec Intelligent caching$150K