03-28-2007MSIM 842 VISUALIZATION II INSTRUCTOR: JESSICA R. CROUCH 1 A Particle System for Interactive Visualization of 3D Flows Jens Krüger Peter Kipfer

03-28-2007 MSIM 842 VISUALIZATION II INSTRUCTOR: JESSICA R. CROUCH

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A Particle System for Interactive Visualizationof 3D Flows

Jens KrügerPeter KipferPolina KondratievaRüdiger Westermann

Authors:

Hector M. GarciaPresented By:


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Problem

In flow research and industrial practice vector field data is one of the key sources for the analysis of flow field dynamics

Visual exploration of complex fields imposes significant requirements on the visualization system and demands for approaches capable of dealing with large amounts of vector valued information at interactive rates.

Previous approaches to virtually explore high-resolution flow fields lack the ability to simultaneously advect and display large amounts of particles.


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Advection

Advection is transport in a fluid The fluid is described mathematically for such processes

as a vector field, and the material transported is described as a scalar concentration of substance, which is present in the fluid.

A good example of advection is the transport of pollutants or silt in a river: the motion of the water carries these impurities downstream.


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Motivation

Overcome current methods limitations by exploiting features of recent graphics accelerators to advect particles in the graphics processing unit (GPU).

Ability to achieve interactive streaming and rendering of millions of particles using higher order numerical integration schemes.

Enable the virtual exploration of large fields in a way similar to real-world experiments.


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Motivation


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Some background info…

GPU = Graphics Processing Unit. It is a dedicated graphics rendering

device. GPUs have a highly parallel structure

which makes them more effective than typical CPUs for a range of complex algorithms.


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More background info…

Recent developments in GPUs include support for programmable shaders

Because most of these computations involve matrix and vector operations, engineers and scientists have increasingly studied the use of GPUs for non-graphical calculations.

Applications requiring massive vector operations, can make use of the massive floating-point computational power of a GPU. This can yield several orders of magnitude higher performance than a conventional CPU.


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Related Work

Particle tracing techniques for flow viz have been studied intensively.

Core of these techniques use numerical integration schemes.

In flow viz context, analysis of such schemes with respect to stability,accuracy and performace.


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Related Work (cont’d…)

Particle-based techniques can visualize local features in the flow.

Global imaging techniques for 3D fields can illustrate global behavior.

LIC-methods allow for interactive 2D vector fields but no good in 3D flow.


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Related Work (cont’d…)


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Methods

Propose a method for overcoming both computation and bandwidth limitations using the GPU.

Use GPU for advection and rendering computations.

Use improvements to rendering pipeline.


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Methods (cont’d…)


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Using this functionality, particle tracing can be performed entirely on the GPU.

Their method computes intermediate results saves them in texture memory and uses them again as input to the geometry units to render images in the frame buffer.

Initial particle positions stored in RGB texture of size M x N.

User defines number of particles and appropriate texture is generated on the CPU and uploaded on the GPU.

Particle Integration Incarnation advection


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Particle Incarnation Transformation Birth Update

Particle Advection Texture access Death test

Advection Reincarnation


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GPU particle engine for flow viz is implemented in Cg


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Particle Rendering OpenGL SuperBuffer

Memory object is bound as the current texture render target and as a vertex array used to draw particle primitives.

Vertex Texture Fetch The key concept is to let the fragment units

generate textures and to use these textures as displacement maps for geometric primitives in subsequent rendering passes.


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Rendering Points

Maximum number of particles stored in video memory rendered as color primitives is 250 million per second

Oriented Point Sprites Used to reveal flow direction. Use a sprite texture atlas for arbitrary shaped

geometry


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Point Rendering


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Oriented Point Sprite Rendering


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Sorting

Authors implement a GPU sorting network into their particle engine. Based in the Bitonic merge sort

algorithm. Well suited for GPU architecture because

sequence of operations is fixed and not dependent in the data to be sorted.


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What is Bitonic merge sort?

Data independent sorting method based on the bitonic sequence

A 0-1-sequence is called bitonic, if it contains at most two changes between 0 and 1.

More generally, a sequence of numbers is bitonic sequence if it has at most one local maximum or one local minimum. Examples: 1,2,3,4,5 ; 10,6,5,3,1 ; 3,7,9,8,6,5,4,1

10,8,6,9,12,15,20


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How does it look like ?


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Derived Flow attributes

Velocity Divergence Enstrophy 2


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Derived Flow attributes (contd…)


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Visualization Geometry

Stream Lines Ping pong buffer (double buffer) Texture samples interpreted as control points Draw polylines of T control points

Stream Ribbons Show rotation about the flow axis Build a second atlas that contains the other rim

of each stream line rotating the initial normal vector according to the accumulated increment angles.


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Stream Lines


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Stream Ribbons


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Evaluation

Model runs at interactive rates on PC hardware

It outperforms CPU counterparts Show timing statistics to compare their

GPU implementation vs. CPU.


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Evaluation (cont’d…)

Lets take a test drive !


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Conclusion

Authors successfully demonstrates advantages of a GPU implementation of a particle flow simulation.

The possibility of integrating numerically and data intensive computations for flow analysis into the rendering process distinguishes the GPU engine from previous approaches.


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Conclusion (cont’d…)

Besides particle advection, the engine provides a variety of visualization options to visually convey relevant structures in 3D steady flow fields.

By using massive particle sets in combination with oriented sprites, LIC-like visualizations can be achieved at interactive rates. This includes higher order integration schemes, thus providing numerically accurate particle traces.


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Questions

Given the parallel architecture of GPUs would a GPU cluster method help for visualizing massive global 3D flow visualizations?


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Questions (cont’d…)

How would the performance of the visualization engine be impacted if the vector field is fed by a fully functional numerical model. i.e. ROMS


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Questions (cont’d…)

Could their implementation be easily extended to non-uniform grids ?

Documents

03-28-2007MSIM 842 VISUALIZATION II INSTRUCTOR: JESSICA R. CROUCH 1 A Particle System for Interactive Visualization of 3D Flows Jens Krüger Peter Kipfer