interactive gpu-based particle tracing for cumulus cloud...
TRANSCRIPT
November 20, 2007
1
Interactive GPU-based Particle Tracing for
Cumulus Cloud Research
M.Sc. Final Presentation
Delft University of TechnologyFaculty EEMCSComputer Graphics and CAD/CAM unitData Visualization Group
Graduation Committee:
prof.dr.ir. F.W. Jansen
ir. F. H. Post
E. J. Griffith, M.Sc.
dr. H. J. J. Jonker (TNW)
Dylan Dussel
November 20, 2007 2
Overview
• Introduction
• Cumulus Cloud Research
• GPU-based Particle Tracing
• Data Handling Techniques
• Particle Visualization & Interaction
• Validation
• Conclusions and Future Work
November 20, 2007 3
Introduction
• Cumulus Cloud Research
• Particle Tracing
• Graphics Processing Unit (GPU)
November 20, 2007 4
Cumulus Cloud Research
• Atmospheric Boundary Layer (ABL)
• Turbulent, thermals
• Dispersion of Pollution
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Cumulus Cloud Research
• The presence of a descending shell
Classical view Descending shell
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Cumulus Cloud Research
• Data aquisition by measurements
• Large-eddy Simulation (LES)• Sub-grid model
• Large time-varying data• Example:
velocity data: 3 16-bit vector components
256x256x160 resolution
60 MB for each time step!
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GPU-based Particle Tracing
• Graphics Processing Unit
• Driven by games industry
• Powerfull, parallel architecture
• Designed to work on large data concurrently
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GPU-based Particle Tracing
• GPU algorithms require a different approach
• Arrays->Textures
• Shader Program (SIMD)
• Render Target, Multipass Algorithms
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GPU-based Particle Tracing AlgorithmFirst pass: advection
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GPU-based Particle Tracing AlgorithmSecond pass: visualization
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GPU-based Particle Tracing Algorithm
• Calculating Curves
• Path lines particles history
• Stream lines multiple passes
• Streak lines particle life time
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• Large time-varying data
• Data loading can become bottleneck of the visualization system
• Data handling techniques to minimize bottleneck
Large Data Handling
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Large Data Handling
• Data format
• Subsampling
• Example: 64x64x40, 16-bit vector component = 960 KB
• Data transfer
• Optimal texture format
U V W ?
4 x 16-bit
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Large Data Handling
• Feature Extraction
• Multiresolution
• High resolution for region of interest (ROI)
• Low resolution for complete domain
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Large Data Handling
• Velocity Field Compression• Split into normal and length
• Quantize length into 16-bit
• Normal lookup table (256 x 256 normals)
• 16-bit (2 Bytes) normal indices
• 3:2 reduction in data
• Half the texture size
L L N N
4 x 8-bit
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Large Data Handling
• GPU data decompression
• “Flat 3D” texture• Factoring V-dimension in V’ and V’’
• Decompression algorithm in fragment shader
• More complex texture lookup and interpolation in particle advection pass
8 x 8 x 6 32 (8x4) x 12 (6x2)
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Large Data Handling
• Particle Tracing System
• 3-pass GPU algorithm
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Particle Visualization
• Visualize particles as points
• Color coding
• Blending
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Particle Visualization
• Pointsize
• Depth fogging
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Particle Visualization
• Glyph-based texture mapped point sprites
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Particle Visualization
• Glyph-based texture mapped point sprites
• View-oriented ellipsoids
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Curve Visualization
Stream lines
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Curve Visualization
Streak lines Path lines
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Curve Visualization
• Path lines
• Particle on path line
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Interaction
• Particle Emitter
• Freely positioning of a source of particles
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Interaction
• Different applications
• Virtual Workbench (VR)
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System validation
• Particle distribution• Well-mixed condition (Thomson, 1987)
• Piling up of particles due to lacking sub-grid model
• Quite uniform
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System validation
• Particle trajectory accuracy
• LES particles vs.
GPU particles
• Uncompressed vs.
Compressed data
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Conclusions
• Interactive visualization of a large number of particles
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Conclusions
• Smart data handling techniques
• Data format
• Multiresolution, ROIs
• GPU-based decompression
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Conclusions
• Validation
• Glyph-based point sprites
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Conclusions
• Cumulus cloud research
• Descending Shell
• Dispersion of particles
• Lifetime cycle
November 20, 2007 33
Future Work
• Optimization
• Exploiting features of modern and future GPUs
• Including the sub-grid model
• Improving compression techniques
• Quantitative information
• Improving the VR application
November 20, 2007 34
Aknowledgements
• Frits, Harm
• Eric
• René, Michal, Gerwin, Jorik, Thijs, Remco
• CG&CC unit
November 20, 2007 35
Questions?