Toward Effective Visualization of Ultrascale TimeVarying Data - PowerPoint PPT Presentation

About This Presentation
Title:

Toward Effective Visualization of Ultrascale TimeVarying Data

Description:

SC05 Time-Varying Visualization Workshop. Toward Effective Visualization of Ultra ... Saddle Amalgamation/Bifurcation. Regular vertex Continuation ... – PowerPoint PPT presentation

Number of Views:40
Avg rating:3.0/5.0
Slides: 58
Provided by: csUcd
Category:

less

Transcript and Presenter's Notes

Title: Toward Effective Visualization of Ultrascale TimeVarying Data


1
Toward Effective Visualization of Ultra-scale
Time-Varying Data
  • Han-Wei Shen
  • Associate Professor
  • The Ohio State University

2
Applications
  • 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
3
Applications
  • 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
4
Research 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

5
Research 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

6
Bricking and Multi-resolution
  • Bricking subdivide the volume into mutiple
    blocks

7
Bricking and Multi-resolution
  • Create a multi-resolution representation for each
    block

8
Spatial Data Hierarchy
  • Combining octree with multi-res transform

bricks
9
Temporal Data Hierarchy?
  • Option1 - Multiple Octrees

t 0 t 1
t 2
10
Temporal Data Hierarchy?
  • Option 2 Treat time as another dimension a
    single 4D tree (16 tree)


11
Time-Space Partition (TSP) Tree(Two Level
Hierarchical Subdivision)
  • First level spatial subdivision

bricks
Shallow Complete Octree
12
Time-Space Partition (TSP) Tree(Two Level
Hierarchical Subdivision)
  • Second level temporal subdivision

4 time steps
13
Spatio-Temporal Data Encoding
  • Wavelet Transform (DWT)

3D wavelet transform
1D WT
14
Spatio-Temporal Data Indexing
  • Time-Space Partitioning (TSP) Trees

15
Tree Traversal and Rendering
16
Image Compositing
Front-to-back
17
Rendering Performance
  • The cached partial images can be re-used
    for the nodes that have high temporal coherence

18
Time-Varying Volume Rendering
Error 0
11.2 speedup
19
I/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
20
Time-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

21
Analyzing Time-varying Features
  • Animation might not be sufficient

22
Strategy 1 Tracking individual components
23
Strategy 2 High Dimensional Visualization
  • Chronovolumes

24
Tracking Time-Varying Isosurface
  • Two main goals
  • Identify correspondence
  • Detect important evolution events and critical
    time steps

?
25
Evolutionary Events
26
Tracking Correspondence
  • Wang and Silvers assumption - Corresponding
    features in adjacent time steps overlap with each
    other

27
Tracking Correspondence
  • A common assumption - Corresponding features in
    adjacent time steps overlap with each other

t 0 t 1
28
Previous 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.

29
Challenges
  • Exhaust search is expensive
  • Solution A local tracking
  • The user selects a local
  • feature of interest and start
  • tracking
  • Extract high dimensional (4D) isosurfaces

30
2D Example
  • 2D time-varying isocontours

T 2
T 1
T 0
31
2D Example
  • Extract 3D isosurface and then slice back

T 2
T 1
T 0
32
2D Example
  • Extract 3D isosurface and then slice back

T 2
T 1
T 0
33
4D 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)
34
Algorithm
  • 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

35
Detect 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

36
Color the components
37
Color the components
38
Critical Time Steps
39
Chronovolumes
  • A Direct Rendering Technique for Visualizing
    Time-Varying Data

(Jonathan Woodring and Han-Wei Shen 2003)
40
Main 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

41
Early Work
Chronophtography (Marey, 1830-1904)
Nude descending a staircase Duchamp, 1912
42
Chronovolumes
  • 4D rendering idea
  • Integration through time
  • Integration functions

43
4D Rendering
  • Direct visualization of 4D data
  • Project the 4D data into a visualizable lower
    dimensional space (2D images)

2D -gt 1D
3D -gt 2D
44
4D Rendering
  • 4D to 2D projection?
  • Need to preserve the relationships between
    different objects in (3D) space and also reveal
    their relationship in time

45
Integration Through Time
  • 4D to 3D projection (chronovolume)
  • Regular volume rendering to visualize
    chronovolumes

chronovolume
46
Integration 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

???
47
Alpha Compositing
  • Commonly used in 3D volume rendering

D
0
C
2D Image
48
Alpha Compositing (2)
  • Adopt the model to time integration

post-classified (color) volume
49
Transfer 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
50
Alpha Compositing Example
10 time steps
3 time steps
51
Additive Colors
  • Show how features overlap

T
T


C c(s(x(t)) dt
t4
0
t3
t2
t1
t
52
Additive Color Example
Alpha Compositing Additive Color
53
Additive Color Example
Alpha Compositing
Additive Colors
54
Additive Color Example
Alpha Compositing
Additive Colors
55
Min/Max Intensity
  • Detect the hot spot

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.

56
Maximum Intensity Example
Additive Colors Maximum
Intensity
57
Maximum Intensity Examples
Alpha Compositing Maximum
Intensity
Write a Comment
User Comments (0)
About PowerShow.com