Title: Toward Effective Visualization of Ultrascale TimeVarying Data
1Toward Effective Visualization of Ultra-scale
Time-Varying Data
- Han-Wei Shen
- Associate Professor
- The Ohio State University
2Applications
- Large Scale Time-Dependent Simulations
- Richtmyer-Meshkov Turbulent Simulation (LLNL)
- 2048x2048x1920 grid per time step (7.7 GB)
- Run 27,000 time steps
- Multi-terabytes output
LLNL IBM ASCI system
3Applications
- Oak Ridge Terascale Supernova Initiative (TSI)
- 640x640x640 floats
- gt 1000 time steps
- Total size gt 1 TB
- NASAs turbo pump simulation
- Multi-zones
- Moving meshes
- 300 time steps
- Total size gt 100GB
ORNL TSI data
NASA turbo pump
4Research Goals and Challenges
- Interactive data exploration
- Quick overview, detail on demand
- Feature enhancement and tracking
- Display the invisible
- Understand the evolution of salient features over
time - Challenges
- managing, indexing, and processing of data
5Research Focuses
- Multi-resolution data management schemes
- Acceleration Techniques
- Efficient data indexing
- Coherence exploitation
- Effective data culling
- Parallel and distributed processing
- Feature tracking and enhancement
- Visual representation
- Geometric tracking
6Bricking and Multi-resolution
- Bricking subdivide the volume into mutiple
blocks
7Bricking and Multi-resolution
- Create a multi-resolution representation for each
block
8Spatial Data Hierarchy
- Combining octree with multi-res transform
bricks
9Temporal Data Hierarchy?
- Option1 - Multiple Octrees
t 0 t 1
t 2
10Temporal Data Hierarchy?
- Option 2 Treat time as another dimension a
single 4D tree (16 tree)
11Time-Space Partition (TSP) Tree(Two Level
Hierarchical Subdivision)
- First level spatial subdivision
bricks
Shallow Complete Octree
12Time-Space Partition (TSP) Tree(Two Level
Hierarchical Subdivision)
- Second level temporal subdivision
4 time steps
13Spatio-Temporal Data Encoding
3D wavelet transform
1D WT
14Spatio-Temporal Data Indexing
- Time-Space Partitioning (TSP) Trees
15Tree Traversal and Rendering
16Image Compositing
Front-to-back
17Rendering Performance
- The cached partial images can be re-used
for the nodes that have high temporal coherence
18Time-Varying Volume Rendering
Error 0
11.2 speedup
19I/O Efficiency
Shock wave 1024 x 128 x 128 , 40 time
steps Minimum brick size 32 x 32 x 32 Temporal
error tolerance 0.02
20Time-Space Partition (TSP) Tree
- More cohesively integrate the temporal and
spatial information into a single hierarchical
data structure - Exploit both temporal and spatial coherence -
Octree becomes a special case of the TSP tree
21Analyzing Time-varying Features
- Animation might not be sufficient
22Strategy 1 Tracking individual components
23Strategy 2 High Dimensional Visualization
24Tracking Time-Varying Isosurface
- Two main goals
- Identify correspondence
- Detect important evolution events and critical
time steps
?
25Evolutionary Events
26Tracking Correspondence
- Wang and Silvers assumption - Corresponding
features in adjacent time steps overlap with each
other
27Tracking Correspondence
- A common assumption - Corresponding features in
adjacent time steps overlap with each other
t 0 t 1
28Previous Approach
- Algorithm
- Extract the complete set of isosurfaces
- Overlap test
- Overlapping features are identified and the
number of intersecting nodes is calculated. - Best matching test
- Find the best match among features.
29Challenges
- Exhaust search is expensive
- Solution A local tracking
- The user selects a local
- feature of interest and start
- tracking
- Extract high dimensional (4D) isosurfaces
302D Example
- 2D time-varying isocontours
T 2
T 1
T 0
312D Example
- Extract 3D isosurface and then slice back
T 2
T 1
T 0
322D Example
- Extract 3D isosurface and then slice back
T 2
T 1
T 0
334D Isosurface
- 3D time-varying 4D
- Extract isosurfaces from 4D hypercubes
- Use 4D maching cubes table (Bhaniramka02)
- Slice the tetrahedra to get the surface at the
desired time step
(x,y,z,t)
34Algorithm
- To track an isosurface component
- User chooses a local component at t
- Propagate 4D isosurface from the seed
- Slice the 4D isosurface at t1
- Continue to t2 if desired
35Detect critical time steps for isosurface tracking
- A 4D isocontour component is a tetrahedral mesh
embedded in four dimensional space. We can treat
the 4D mesh as a normal 3D mesh, with the time
values as the scalar values defined over the
tetrahedron vertices. - The critical points of this mesh indicate when
and where the topology of the isosurface will
change. - Local minimum Creation
- Local maximum Dissipation
- Saddle Amalgamation/Bifurcati
on - Regular vertex Continuation
36Color the components
37Color the components
38Critical Time Steps
39Chronovolumes
- A Direct Rendering Technique for Visualizing
Time-Varying Data
(Jonathan Woodring and Han-Wei Shen 2003)
40Main Idea
- Render data at different time steps to a single
image - Establish correspondences between features
- Compare shapes and sizes of features in time
- Reason about the positions of the features
- Reveal temporal trend
41Early Work
Chronophtography (Marey, 1830-1904)
Nude descending a staircase Duchamp, 1912
42Chronovolumes
- 4D rendering idea
- Integration through time
- Integration functions
434D Rendering
- Direct visualization of 4D data
- Project the 4D data into a visualizable lower
dimensional space (2D images)
2D -gt 1D
3D -gt 2D
444D Rendering
- 4D to 2D projection?
- Need to preserve the relationships between
different objects in (3D) space and also reveal
their relationship in time
45Integration Through Time
- 4D to 3D projection (chronovolume)
- Regular volume rendering to visualize
chronovolumes
chronovolume
46Integration Function
- Vc F (Vt, V t1, V t2, V t3, , V tn-1)
- No so called correct integration the design
of F depends on the visualization need
???
47Alpha Compositing
- Commonly used in 3D volume rendering
D
0
C
2D Image
48Alpha Compositing (2)
- Adopt the model to time integration
post-classified (color) volume
49Transfer Function
- Color and opacity function
- Modulate by time stamp and data
Alpha function example
a
a
0.2
0.7
t
v
3 8
6
50Alpha Compositing Example
10 time steps
3 time steps
51Additive Colors
- Show how features overlap
T
T
C c(s(x(t)) dt
t4
0
t3
t2
t1
t
52Additive Color Example
Alpha Compositing Additive Color
53Additive Color Example
Alpha Compositing
Additive Colors
54Additive Color Example
Alpha Compositing
Additive Colors
55Min/Max Intensity
F(V i) t such that V t gt Vi for any i
lt
- Show which time step has the highest
- (lowest) value, and also what that
- value is.
56Maximum Intensity Example
Additive Colors Maximum
Intensity
57Maximum Intensity Examples
Alpha Compositing Maximum
Intensity