Distributed Physicallybased Art and Live
Animation on the GRID
Presented at Prof. Joe Kearney’s
lecture
Jun Ni, Ph.D. M.E
Research Services, ITS
Interactive Kites Flying
Shalini Venkataraman, Dept. of CS
EVL, University of Chicago
NCSA, University of Illinois
Outline
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Introduction to Grid Computing
State of the art of high performance
computing tele-immersive VR application
Motivation and background
Physically based model
Implementation with VR and no-grid simulation
Grid computing based simulation
Introduction to Grid Computing
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Geologically distributed “virtual supercomputer” in
virtual organization
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NSF Middleware
NSF and DOE supported globus project, TeriGrid (CalTech,
NPACI, ANL, NCSA) (ongoing $54 millions)
NSF ITR projects
Grids everywhere (next generation of computing)
Combination of grid computing together with teleimmersive VR application
State of the art of high performance
computing tele-immersive VR
application on internet
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Globally network-based
Physical model based scientific animation
Tele-immersive VR application
Art design
Motivation and background
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French sculpter and light artist, Jackie Matisse creates
teflon or crepe kites, with artistic tails as long as 15 feet,
that can soar through the air, ripple through water, or
undulate with the air currents in a room.
Randomly influenced by natural forces, the kitetails
move, and metamorphose in faint air currents and
dramatically changing natural light
Motivation and background
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The VR piece was
inspired by the threescreen collaborative
video Sea Tails created in
1983 by Matisse with
filmmaker Molly Davies.
The film follows ten
kitetails on their dancing
flight through the air and
into the water.
Physically Based Model
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To ensure stability, the simulation has to be performed
in very small time steps making them very
computationally intensive.
Implicit approaches to mass-spring systems in the
context of VR environments
using a grid computing system with its geographically
dispersed processors linked by high-speed networks
Physically Based Model
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Each kite is modeled as a
cloth object treated as a
cluster of masses and
springs
Using fundamental laws
of dynamics to calculate
various forces acting on
these masses and springs
in order to account for
the movement of each
kite
Physically Based Model
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Mesh Model is introduced to each grid point
P(i,j) and each point has its mass and linked to
neighboring points
Position x(i,j) obeys dynamic laws
dx(i,j)/dt = F(i,j)/m(i,j)
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Newton’s second law
Discretize dynamic law
x (i,j) t+dt = x(i,j) t + t v(I,j) t+dt
Physically Based Model
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Internal and external
forces acting on each
point of grids
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Internal forces: structural,
shearing and bending
forces
Fin(i,j) = k (Lt – Lo)[ P(i,j)-P(k,l) ]
Elasticity
Physically Based Model
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Internal and external forces acting on each
point of grids
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External gravitational force
Fg(i,j) = m(i,j) g
Gravitational acceleration
Physically Based Model
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Internal and external forces acting on each
point of grids
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Wind forces
Fw(i,j) = m n(i,j) [ w – v(i,j) ] n (i,j)
Air or fluid viscosity
Physically Based Model
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Internal and external forces acting on each
point of grids
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viscous forces
Fw(i,j) = - m n(i,j) v(i,j)
Damping coefficient
Implementation with VR
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CAVE VR environment
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EVL’s CAVE
CAVE Library
Implementation with VR
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Standalone kite
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Texture mapped onto the
kitetail mesh
User can use wand to
grab on the kite head and
move or change its
imagery
Wind direction is
controlled by wand
orientation (constant
wind speed)
Head controlled by wand in
CAVE system
Implementation with VR
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Standalone kite
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Other properties such as
stiffness, length, width
and visual attributes like
texture maps can be
specified at the rum-time
by user
Each kite dimension is 2
ft by 30 ft in virtual space
modled by 250 masspoints
Head controlled by wand in
CAVE system
Implementation with VR
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Standalone kite (no grid)
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Simulation rate for on kite
takes 125 iteration per second.
Each iteration takes 8 ms.
In 3-kite simulation, each kite
has 41 ms/s.
Small time step makes more
stable but more computer
intensive
SGI ONYX Inifite Reality with
8 198 MHz MPIS R10000
processors and 2G memory.
Head controlled by wand in
CAVE system
Grid computing based simulation
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Distributed simulation
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Small time steps
Grid enhanced
High-speed network
based
Architecture of gird
enhanced application to
kite simulation
Grid Computing Based
Simulation
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Distributed simulation
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Configure several
simulation nodes
globally distributed
QUANTA middleware
(collection of network
programming tools for
optimizing data sharing
over high-speed
networks)
Grid Computing Based
Simulation
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Distributed simulation
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kiteServer (database server for wind direction as a 3float array; any user interaction results will be received
and broadcast to other nodes)
kiteSim (simulation server for computing each kite’s
position and directly transmitted through UDP socket
to dispply client running in CAVE system)
kiteDisplay (client)
Implementation (displays the kitetails and userinteraction. The kite positions will read from kiteServer
and display texture mapped with images
Grid Computing Based
Simulation
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Results
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Distributed simulation rate (1000iterantions/s) is
significant higher than standalone simulation (125
iterations/s)
Simulation rate is dependent of the number of kites
due to network bandwidth. With increasing number of
kites, simulation rate approaches to constant.
Grid Computing Based
Simulation
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Discussion
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Network latency
Interactions among kites
Fluid models
Communication between kites
Virtual space for flying aircrafts (Jun Ni’s proposal)
using physically based mathematical models in CFD fro
fluid flow along each craft and deformable body model
for each object of craft
Interactive sound tracks
What about your suggestions?
Reference
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http://www.evl.uic.edu/research/template_res_
project.php3?indi=231