A Multiresolution Volume Rendering Framework for LargeScale TimeVarying Data Visualization

1
A Multiresolution Volume Rendering Framework for
Large-Scale Time-Varying Data Visualization

• Chaoli Wang1, Jinzhu Gao2,
• Liya Li1, Han-Wei Shen1
• 1The Ohio State University
• 2Oak Ridge National Laboratory

2
Introduction

• Large-scale numerical simulation
• Richtmyer-Meshkov Instability (RMI) data _at_ LLNL
• 2,048 2,048 1,920 grid
• 960 (8 8 15) nodes of the IBM-SP system
• 7.5 GB per time step, output 274 time steps
• Goal
• Data exploration
• Quick overview, detail on demand
• Approach
• Multiresolution data representation
• Error-controlled parallel rendering

3
Challenge

• Compact hierarchical data representation
• Allow specifying different spatial and temporal
  resolutions for rendering
• Long chains of parent-child node dependency
• Data dependency among processors
• Balance the workload for parallel rendering

4
Algorithm Overview
The algorithm flow for large-scale time-varying
data visualization
5
Wavelet-Based Time Space Partitioning Tree

• The WTSP tree
• Space-time hierarchical data structure to
  organize time-varying data
• An octree (spatial hierarchy) of binary trees
  (temporal hierarchy)
• Originate from the TSP tree Shen et al. 1999
• Borrow the idea of the wavelet tree Guthe et al.
  2002

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Wavelet-Based Time Space Partitioning Tree

• WTSP tree construction
• Two-stage block-wise wavelet transform and
  compression process
• Build a spatial hierarchy in the form of an
  octree for each time step
• Merge the same octree nodes across time into
  binary time trees

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Hierarchical Spatial and Temporal Error Metric
se(T) Si0..7MSE(T, Ti) MAXse(Ti)i0..7

• Based on MSE calculation
• Compare the error of each block with its children

te(T) MSE(T, Tl) MSE(T, Tr) MAXte(Tl),
te(Tr)
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Storing Reconstructed Data for Space-Time Tradeoff

• Alleviate data dependency
• EVERY-K scheme

ho 6, ht 4 ko 2, kt 2
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WTSP Tree Partition and Data Distribution

• Eliminate dependency among processors
• Distribution units

ho 6, ht 4 ko 2, kt 2
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WTSP Tree Partition and Data Distribution

• Space-filling curve traversal
• Neighboring blocks of similar spatial-temporal
  resolution should be evenly distributed to
  different processors
• Space-filling curve preserves locality, always
  visits neighboring blocks first
• Traverse the volume to create a one-dimensional
  ordering of the blocks

11
WTSP Tree Partition and Data Distribution

• Error-guided bucketization
• Data blocks with similar spatial and temporal
  errors should be distributed to different
  processors
• Create buckets with different spatial-temporal
  error intervals

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WTSP Tree Partition and Data Distribution

• Error-guided bucketization
• Bucketize the distribution units when performing
  hierarchical space-filling curve traversals
• Distribute units in each bucket in a round-robin
  fashion

13
Run-Time Rendering

• WTSP tree traversal
• User specifies time step and tolerances of both
  spatial and temporal errors
• Traverse octree skeleton and the binary time
  trees for each encountered octree node
• A sequence of data blocks is identified in
  back-to-front order for rendering

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Run-Time Rendering

• Data block reconstruction
• Get low-pass filtered subblock from its parent
  node
• Decode high-pass filtered wavelet coefficients
• Perform inverse 3D wavelet transform
• Reduce reconstruction time from O(c1ho c2hoht)
  to O(c1ko c2kokt), where
• c1 time to perform an inverse 3D wavelet
  transform
• c2 time to perform an inverse 1D wavelet
  transform
• ho the height of the octree
• ht the height of the time tree
• ko of levels in an octree node group
• kt of levels in a time tree node group

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Run-Time Rendering

• Parallel Volume Rendering
• Each processor renders the data blocks identified
  by the WTSP tree traversal and assigned to it
  during the data distribution stage
• Cache reconstructed data for subsequent frames
• Screen tiles partition
• Image composition

16
Results

• Data sets and wavelet transforms

17
Results

• Testing environment
• A PC cluster consisting of 32 2.4 GHz Pentium 4
  processors connected by Dolphin networks
• Performance
• Software raycasting
• 96.53 parallel CPU utilization, or a speedup of
  30.89 times for 32 processors

18
Results

• Data distribution with EVERY-K scheme (ko 2, kt
  2)

RMI data set
SPOT data set
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Results

• Rendering balance result

RMI data set
SPOT data set
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Results

• The timing result with 5122 output image
  resolution

21
Results

• Rendering of RMI data set at selected time steps

1st 536
8th 743
15th 1,317
32th 1,625
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Results

• Rendering of SPOT data set at selected time steps

1st 2,558
12th 2,743
21th 2,392
30th 2,461
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Results

• Multiresolution volume rendering

RMI data set, 11th time step
SPOT data set, 5th time step
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Conclusion Future Work

• Multiresolution volume rendering framework for
  large-scale time-varying data visualization
• Hierarchical WTSP tree data representation
• Data partition and distribution scheme
• Parallel volume rendering algorithm
• Future work
• Utilize graphics hardware for wavelet
  reconstruction and rendering speedup
• Incorporate optimal feature-preserving wavelet
  transforms for feature detection

25
Acknowledgements

• Funding agencies
• NSF ITR grant ACI-0325934
• NSF Career Award CCF-0346883
• DOE Early Career Principal Investigator Award
  DE-FG02-03ER25572
• Data sets
• Mark Duchaineau _at_ LLNL
• John Clyne _at_ NCAR
• Testing environment
• Jack Dongarra and Clay England _at_ UTK
• Don Stredney and Dennis Sessanna _at_ OSC