*Partially funded by the Austrian Grid Project (BMBWK GZ 4003/2-VI/4c/2004)

Making the Best of Your Data -Making the Best of Your Data -Offloading Visualization Tasks Offloading Visualization Tasks

onto the Gridonto the GridSemiautomatic Generation of Transfer Semiautomatic Generation of Transfer

Functions through Grid-based Parameter Functions through Grid-based Parameter Studies*Studies*

Peter Praxmarer

GUP, Joh. Kepler University Linzpraxmarer@gup.jku.at

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Agenda

• What are Transfer functions?

• Goal

• Approach

• Characteristics

• Use of the Grid

• Conclusion and Future Work

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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->(R,G,B,A)

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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What are Transfer Functions?

• Example:– Given: Some volume data, a transfer function for

transparency only.

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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What are Transfer Functions?

• Example:

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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What are Transfer Functions?

• Example:

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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What are Transfer Functions?

• Example: Adding a color map

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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What are Transfer Functions?

• Example: Adding a color map

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Task

• Find transfer functions for visualizing volume data

• Application:Offline-Rendering (Raytracing) of gas distributions in galaxy clusters

• Given:– Voxel data (density, temperature)

• Wanted:– Mapping: D->(R,G,B,A)

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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 -> we can use the

gradient information for detecting boundaries– Galaxies often consist of a lot of gas which gradually gets

denser -> 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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Approach

• Apply a genetic algorithm:1. Generate an initial population of transfer

functions2. Evaluate the fitness of each chromosome3. Select the best chromosomes (transfer

functions) for the next population pi+1

4. Recombine the chromosomes5. Mutate some chromosomes6. Perform steps 2 to 6 until a good-enough

transfer function has been found.

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Encoding (1)

• A chromosome represents one transfer function

• Each chromosome has N<50 genes

• Each gene stores– Density value– Color

• Thus: Mapping D -> (R,G,B,A)

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Encoding (2)

Density valueHue,

Saturation,Value

Density valueHue,

Saturation,Value

Density valueHue,

Saturation,Value

Density valueHue,

Saturation,Value

…

Gene 0 Gene 1 Gene N-2 Gene N-1

Chromosome (transfer function)

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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/don’t like scheme

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Selecting the next population

• Select transfer functions for popi+1 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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Recombination (2)

For example:

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Mutation

• Applied with probability pmutation (usually pmutation=0,001) per gene

• Randomly change the gene (color or density value)

• Introduces new solutions into the search space

• Prevents the premature convergence to local optima

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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Rendering on the Grid

• Use today’s grid technology to distribute the load on various resources

• Prerequisites:– POVRay– Grid infrastructure

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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)

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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

Talk at CGW05 Peter Praxmarer, GUP, Universität Linz

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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• …