Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information...
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![Page 1: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/1.jpg)
Large Scale Time-Varying
Data Visualization
Han-Wei ShenDepartment of Computer and Information Science
The Ohio State University
![Page 2: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/2.jpg)
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
![Page 3: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/3.jpg)
Applications
Oak Ridge Terascale Supernova Initiative (TSI) 640x640x640 floats > 1000 time steps Total size > 1 TB
NASA’s turbo pump simulation Multi-zones Moving meshes 300+ time steps Total size > 100GB
ORNL TSI data
NASA turbo pump
![Page 4: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/4.jpg)
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
Challengesmanaging, indexing, and processing of data
![Page 5: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/5.jpg)
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
![Page 6: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/6.jpg)
Multiresolution Data Hierarchy
Goal: allow interactive spatial-temporal data browsing at arbitrary scales
Based on wavelet transform and Huffman encoding Create a multiresolution data hierarchy called
wavelet based time-space partitioning tree (WTSP tree)
t=0 t=1 t=2 t=3 t=4
… … … … … … … … … …
![Page 7: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/7.jpg)
Multiresolution Data Hierarchy
Goal: allow interactive spatial-temporal data browsing at arbitrary scales
Based on wavelet transform and Huffman encoding Create a multiresolution data hierarchy called
wavelet based time-space partitioning tree (WTSP tree)
t=0 t=1 t=2 t=3 t=4
+ + + +… … … … … … … … … …
![Page 8: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/8.jpg)
Multiresolution Data Hierarchy
Goal: allow interactive spatial-temporal data browsing at arbitrary scales
Based on wavelet transform and Huffman encoding Create a multiresolution data hierarchy called
wavelet based time-space partitioning tree (WTSP tree)
… …
1D Temporal hierarchy3D Spatial hierarchyWTSP = 4D hierarchy
![Page 9: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/9.jpg)
Accelerated Techniques
Utilize temporal coherence Coherence in image Coherence in visibility Coherence in indices
1D Temporal hierarchy3D Spatial hierarchy
11.2 speedup, 3.4% image diff. 75% invisible blocks removed 80% space saving for indices
![Page 10: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/10.jpg)
Feature Tracking and Enhancement
Two main goals: Identify correspondence
Detect important evolution events and critical time steps
![Page 11: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/11.jpg)
Strategy 1: Computing 4D Geometry
3D time-varying = 4D
Extract “isosurfaces” from 4D hypercubes
Slice the 4D geometry to get the surface at the desired time step
Analyze the 4D geometry to discovery important evolution events
![Page 12: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/12.jpg)
Strategy 2: High Dimensional Visualization
• Hyper-Projection to 3D image plane and then use graphics hardware to perform real time rendering
![Page 13: Large Scale Time-Varying Data Visualization Han-Wei Shen Department of Computer and Information Science The Ohio State University.](https://reader031.fdocuments.in/reader031/viewer/2022012922/5697bff61a28abf838cbdda4/html5/thumbnails/13.jpg)
Parallel Computation
To utilize the parallel computation power, we need to
partitioning and distribute the hierarchy Effective data management is a must!
Eliminate data dependency while minimizing data replication Ensure run time load balancing Efficient streaming and caching of raw or reconstructed data
from the data repository
1D Temporal hierarchy3D Spatial hierarchy
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Summary
We are now able to efficiently visualize large scale time-varying data generated by DOE scientists
Most of the algorithms are run on either local workstations or clusters One of the major challenges remained is actually
how to move data Inter-operability among different algoirthms
and software components will be a key for practical uses