Multiresolution Visualization and Analysis of Turbulence using VAPOR
Alan Norton
NCAR/CISL
Boulder, CO USA
Turbulent Theory and Modeling:
 GTP Theme-of-Year Workshop February 28, 2008

Outline
VAPOR project overview
VAPOR technical capabilities (new 1.2 release)
Interaction techniques for understanding massive turbulence datasets
Six techniques that have been developed through scientific use of VAPOR
Visualization is a data exploration process
Lessons and future work

VAPOR project overview
VAPOR is the Visualization and Analysis Platform for Oceanic, atmospheric and solar Research
Problem:  Because of the recent growth in supercomputing performance, scientific datasets are becoming too large to interactively apply analysis and visualization resources.
Goal:  Make it easier to analyze and visualize massive (Terabyte and greater) datasets
Provide interactive data access
Develop user interface customized for scientists
VAPOR is funded by NSF ITR: a collaboration with NCAR, UC DavisÕ Institute for Data Analysis and Visualization, and Ohio State UniversityÕs Dept. of Computer and Information Sciences

VAPOR Technical Approach
Key components
Multiresolution data representation, enables interactive access:
Entire dataset available at lowered resolution
Regions of interest available at full resolution
Prioritize ease-of-use for scientific research
Integrate visualization and analysis, interactively steering analysis while reducing data handling
Exploit power of GPU

Principal Capabilities of VAPOR 1.2
New features in version 1.2 (Oct 2007)
Isosurfaces
Interactively generated using GPU
Spherical grid rendering (prototype)
Support for WRF (and terrain-following grids)
Existing features:
Flow integration
Both steady and time-varying flow integration
Field line advection
Volume rendering
Interactive color/transparency editor
Interactive control of region size and data resolution
Bidirectional integration with IDL¨ for analysis
Data probing and contour planes
Interactive flow seed placement
Animation of time-varying data

VAPOR data exploration examples
Combining visualization with analysis of a vortex, in a solar hydrodynamic simulation (Mark Rast)
A Ôcurrent rollÕ in a multi-terabyte MHD dataset (Pablo Mininni)
Advection of magnetic field lines in a velocity field (Pablo Mininni)
Advance of cold air mass in Georgia, April 2007 (Thara Prabhakaran)

VAPORÕs Interaction Techniques for
Understanding Massive Turbulence Datasets
Interactive feedback is key to visual data understanding
Multiresolution data browsing
Enables interactive access to terabyte datasets
Visual color and opacity editing with histograms
Identify features of interest by color and opacity
Export/import data to/from analysis toolkit
Currently supporting IDL¨
Use planar probe for visual flow seed placement
Local data values guide seed placement
Track structure evolution with field line advection
Time-evolution of structures shown by field line motion
Use the GPU for interactive rendering
Cartesian, Spherical, Terrain-following (WRF) grids

Interaction Technique 1:
Multiresolution data browsing
Enabled by wavelet data representation
Interactively visualize full data at low resolution
Zoom in, increase resolution for detailed understanding

Interaction Technique 2:
Visual color/transparency editing
Design developed with Mark Rast
Drag control points to define opacity and color mapping
Histogram used to guide placement
Continuous visual feedback in 3D scene

Interaction Technique 3:
Export/import data to/from analysis toolkit
Currently using IDL¨
User specifies region to export to IDL session
IDL performs operations on specified region
Results imported as new variables in VAPOR

Interaction Technique 4:
Use planar probe for visual flow seed placement
Useful to place flow seeds based on local data values
Planar probe provides cursor for precise placement in 3D
Field lines are immediately reconstructed as seeds are specified

Interaction Technique 5:
Track structure evolution with field line advection
Animates field lines in velocity field
Useful in tracking evolution of geometric structures (e.g. current sheets, flux tubes)
Based on algorithm proposed by Aake Nordlund

Interaction Technique 6:
Use the GPU for interactive data rendering
Modern GPUÕs are cheap, fast, effective
GPUÕs are SIMD clusters, efficiently traverse data arrays
Support for cartesian, spherical, terrain-following grids

VAPOR Lessons
Multiresolution methods are essential for understanding massive data sets.
Interactive analysis and visualization can indeed enable or accelerate scientific discovery
One-on-one interaction between scientists and software developers results in valuable interaction techniques
We are only beginning to exploit the power of GPUÕs
Largest obstacles:
Wide diversity of data representations used in research
Data conversion effort

VAPOR Plans
New features prioritized by the VAPOR steering committee and user input
Features under consideration include:
Mapping of variables to isosurface color/opacity
Support for 2D data
Image-based flow visualization
Perform math operations on data
Keyframing and spin animation
Parallel data conversion on supercomputers
Wavelet data compression
Send suggestions to  vapor@ucar.edu

Slide 16
Acknowledgements
Steering Committee
Nic Brummell - CU
Yuhong Fan - NCAR, HAO
AimŽ Fournier – NCAR, IMAGe
Pablo Mininni, NCAR, IMAGe
Aake Nordlund, University of Copenhagen
Helene Politano - Observatoire de la Cote d'Azur
Yannick Ponty - Observatoire de la Cote d'Azur
Annick Pouquet - NCAR, ESSL
Mark Rast - CU
Duane Rosenberg - NCAR, IMAGe
Matthias Rempel - NCAR, HAO
Geoff Vasil, CU
Developers
John Clyne – NCAR, CISL
Alan Norton – NCAR, CISL
Kenny Gruchalla – CU
Victor Snyder - CSM
Research Collaborators
Kwan-Liu Ma, U.C. Davis
Hiroshi Akiba, U.C. Davis
Han-Wei Shen, Ohio State
Liya Li, Ohio State
Systems Support
Joey Mendoza, NCAR, CISL