Multiresolution Visualization and Analysis of Turbulence using VAPOR

1
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

This work is funded in part through a U.S.
National Science Foundation, Information
Technology Research program grant
2
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

3
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 Davis Institute for Data Analysis and
  Visualization, and Ohio State Universitys Dept.
  of Computer and Information Sciences

4
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

Combination of visualization with multiresolution
data representation enables interactive discovery
5
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

6
VAPOR data exploration examples
Interactive visualization facilitates scientific
discovery

• Combining visualization with analysis of a
  vortex, in a solar hydrodynamic simulation (Mark
  Rast)
• A current roll 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)

7
VAPORs 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

8
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

9
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

10
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

11
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

12
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

13
Interaction Technique 6 Use the GPU for
interactive data rendering

• Modern GPUs are cheap, fast, effective
• GPUs are SIMD clusters, efficiently traverse
  data arrays
• Support for cartesian, spherical,
  terrain-following grids

B. Brown, Solar MHD simulation
T. Prabhakaran, April 2007 cold event in WRF
14
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
  GPUs
• Largest obstacles
• Wide diversity of data representations used in
  research
• Data conversion effort

15
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_at_ucar.edu

16
VAPOR Availability

• Version 1.2.2 software released in January 2008
• Runs on Linux, Irix, Windows, Mac
• System requirements
• a modern (nVidia or ATI) graphics card (available
  for about 200)
• 1GB of memory
• Supported in NCAR visualization/analysis systems
• Software dependencies
• IDL http//www.ittvis.com/ (only for
  interactive analysis)
• Executables, documentation available (free!) at
  http//www.vapor.ucar.edu/
• Source code, feature requests, etc. at
  http//sourceforge.net/projects/vapor

17
Acknowledgements

• Steering Committee
• Nic Brummell - CU
• Yuhong Fan - NCAR, HAO
• Aimé 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