Making%20the%20Best%20of%20Your%20Data%20-Offloading%20Visualization%20Tasks%20onto%20the%20Grid%20Semiautomatic%20Generation%20of%20Transfer%20Functions%20through%20Grid-based%20Parameter%20Studies*

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Making the Best of Your Data -Offloading
Visualization Tasks onto the GridSemiautomatic
Generation of Transfer Functions through
Grid-based Parameter Studies

• Peter Praxmarer
• GUP, Joh. Kepler University Linz
• praxmarer_at_gup.jku.at

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Agenda

• What are Transfer functions?
• Goal
• Approach
• Characteristics
• Use of the Grid
• Conclusion and Future Work

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What are Transfer Functions?

• Volumetric data represents
• A volume with some scalar property at every point
• Properties can be density, temperature,
  chemistry,
• Rendering methods
• Isosurface A 3d contour is created at a selected
  density, the resulting surface shows all the
  regions that are more (or less) dense than the
  chosen contour level.
• Raytracing/Raycasting A transfer function
  provides a mapping of the density values to color
  and transparency.
• Thus D-gt(R,G,B,A)

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What are Transfer Functions?

• Example
• Given Some volume data, a transfer function for
  transparency only.

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What are Transfer Functions?

• Example

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What are Transfer Functions?

• Example

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What are Transfer Functions?

• Example Adding a color map

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What are Transfer Functions?

• Example Adding a color map

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Task

• Find transfer functions for visualizing volume
  data
• ApplicationOffline-Rendering (Raytracing) of
  gas distributions in galaxy clusters
• Given
• Voxel data (density, temperature)
• Wanted
• Mapping D-gt(R,G,B,A)

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Properties of the data

• Astrophysical volume data generated by N-body SPH
  simulations
• Unlike MRI data the astrophysical data is highly
  amorphic
• In MRI data there are sharp boundaries -gt we can
  use the gradient information for detecting
  boundaries
• Galaxies often consist of a lot of gas which
  gradually gets denser -gt the gradient alone is
  not sufficient to generate good visual
  representations (especially with isosurface
  rendering)
• Finding a transfer function that reveals the
  interesting parts of the simulated data is
  difficult

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Approach

• Apply a genetic algorithm
• Generate an initial population of transfer
  functions
• Evaluate the fitness of each chromosome
• Select the best chromosomes (transfer functions)
  for the next population pi1
• Recombine the chromosomes
• Mutate some chromosomes
• Perform steps 2 to 6 until a good-enough transfer
  function has been found.

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Encoding (1)

• A chromosome represents one transfer function
• Each chromosome has Nlt50 genes
• Each gene stores
• Density value
• Color
• Thus Mapping D -gt (R,G,B,A)

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Encoding (2)
Chromosome (transfer function)
Gene 0
Gene 1
Gene N-2
Gene N-1
Density value Hue, Saturation, Value
Density value Hue, Saturation, Value
Density value Hue, Saturation, Value
Density value Hue, Saturation, Value

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Generating an initial population

• Collecting statistical measures
• Histogram of the voxel data
• Mean, average, mode
• Used to generate the initial population using a
  heuristics.
• Selection based on
• Density interval
• Frequency

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Evaluation of the fitness

1. Render the population on the grid
2. Present the resulting images to the user
3. The user judges the transfer functions according
   to a like/dont like scheme

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Selecting the next population

• Select transfer functions for popi1 proportional
  to their fitness (the better a transfer function
  is, the more often it is selected)
• Introduces a bias towards better transfer
  functions
• Is not sufficient to generate new transfer
  functions

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Recombination (1)

• Allows a transfer function to move towards
  interesting places
• Applied with a probablity pcrossover (usually
  pcrossover 0.7)
• Generates two offsprings from two parent
  chromosomes
• The parent chromosomes are chosen by random from
  the previously selected chromosomes

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Recombination (2)
For example
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Mutation

• Applied with probability pmutation (usually
  pmutation0,001) per gene
• Randomly change the gene (color or density value)
• Introduces new solutions into the search space
• Prevents the premature convergence to local optima

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Application characteristics

• The user directs the search
• Only the domain expert knows what he wants to see
  
• Allows finding transfer functions for volume data
  with an amorphous structure (galaxy data vs. MRT
  data)
• Requires large computational power to render the
  images of a population

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Rendering on the Grid

• Use todays grid technology to distribute the
  load on various resources
• Prerequisites
• POVRay
• Grid infrastructure

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Components

• Consists of
• GUI Presents the rendered images
• Master Server that maintains the connection to
  clients Runs on the same machine as the GUI.
• Clients Are running on the Grid. Connect back to
  the Master and receive and execute commands from
  the master
• Retrieve Density data
• Retrieve Scene description
• Execute Rendering
• Send Output back to GUI

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Parameter study

• The transfer function is saved as a colormap in
  the POVray scene description file
• Clients receive commands to execute the Rendering
  and transfer back the results
• Rendering is parallelized across multiple Grid
  nodes
• Data transfer using GridFTP

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Results (1)
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Results (2)
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Results (3)

• Rendering time 60 sec / transfer function with
  POVRay resolution 600x600
• By Parallelizing the POVRay rendering the
  rendering time can be significantly reduced
  (depends of number of available nodes)
• Population size typically 16, up to 64

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Conclusion

• Supports astrophysicists in finding useful
  transfer functions for visualizing their
  simulated data
• The astrophysicists directs the search to what he
  wants to see in his data
• Due to the use of Grid Technology he is able to
  explore many different settings at once in a
  considerably short time
• Due to ray-tracing he gets high-quality
  representations of the volume data

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Future Work

• Improve heuristics for generating the initial
  population
• Use good transfer functions from astrophysicists
  as a starting point
• Improve the GUI to allow manually changing the
  presented transfer function. This should be done
  locally on the workstation to provide
  interactivity.
• Reduce the response time