jun ni, ph.d. m.e research services, its
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Distributed Physically-based
Art and Live Animation on the
GRID
Presented at Prof. Joe Kearney’s lectureJun Ni, Ph.D. M.EJun Ni, Ph.D. M.E
Research Services, ITSResearch Services, ITS
Interactive Kites Flying
Shalini Venkataraman, Dept. of CS
EVL, University of ChicagoEVL, University of Chicago
NCSA, University of IllinoisNCSA, University of Illinois
OutlineOutline
Introduction to Grid ComputingIntroduction to Grid Computing State of the art of high performance State of the art of high performance
computing tele-immersive VR computing tele-immersive VR applicationapplication
Motivation and backgroundMotivation and background Physically based modelPhysically based model Implementation with VR and no-grid Implementation with VR and no-grid
simulationsimulation Grid computing based simulationGrid computing based simulation
Introduction to Grid Introduction to Grid ComputingComputing
Geologically distributed “virtual Geologically distributed “virtual supercomputer” in virtual organizationsupercomputer” in virtual organization NSF MiddlewareNSF Middleware NSF and DOE supported globus project, NSF and DOE supported globus project,
TeriGrid (CalTech, NPACI, ANL, NCSA) TeriGrid (CalTech, NPACI, ANL, NCSA) (ongoing $54 millions)(ongoing $54 millions)
NSF ITR projects NSF ITR projects Grids everywhere (next generation of Grids everywhere (next generation of
computing)computing) Combination of grid computing together Combination of grid computing together
with tele-immersive VR applicationwith tele-immersive VR application
State of the art of high State of the art of high performance computing performance computing
tele-immersive VR tele-immersive VR application on internetapplication on internet
Globally network-basedGlobally network-based Physical model based scientific Physical model based scientific
animationanimation Tele-immersive VR applicationTele-immersive VR application Art designArt design
Motivation and background Motivation and background
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 Motivation and background
The VR piece was inspired by the three-screen 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 Physically Based Model
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 Physically Based Model
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 Physically Based Model
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
Discretize dynamic law
dx(i,j)/dt = F(i,j)/m(i,j) Newton’s second law
x (i,j) t+dt = x(i,j) t + t v(I,j) t+dt
Physically Based Model Physically Based Model
Internal and external forces acting on each point of grids Internal forces:
structural, shearing and bending forces
Fin(i,j) = k (Lt – Lo)[ P(i,j)-P(k,l) ]
Elasticity
Physically Based Model Physically Based Model
Internal and external forces acting on each point of grids External gravitational force
Fg(i,j) = m(i,j) g
Gravitational acceleration
Physically Based Model Physically Based Model
Internal and external forces acting on each point of grids Wind forces
Fw(i,j) = n(i,j) [ w – v(i,j) ] n (i,j)
Air or fluid viscosity
Physically Based Model Physically Based Model
Internal and external forces acting on each point of grids viscous forces
Fw(i,j) = - n(i,j) v(i,j)
Damping coefficient
Implementation with VRImplementation with VR
CAVE VR environment EVL’s CAVE CAVE Library
Implementation with VRImplementation with VR
Standalone kite 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 VRImplementation with VR
Standalone kite 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 mass-points
Head controlled by wand in CAVE system
Implementation with VRImplementation with VR
Standalone kite (no grid) 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 Grid computing based simulationsimulation
Distributed simulation Small time steps Grid enhanced High-speed
network based Architecture of gird
enhanced application to kite simulation
Grid Computing Based Grid Computing Based SimulationSimulation
Distributed simulation Configure several
simulation nodes globally distributed
QUANTA middleware (collection of network programming tools for optimizing data sharing over high-speed networks)
Grid Computing Based Grid Computing Based SimulationSimulation
Distributed simulation kiteServer (database server for wind direction
as a 3-float 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 user-
interaction. The kite positions will read from kiteServer and display texture mapped with images
Grid Computing Based Grid Computing Based SimulationSimulation
Results 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 Grid Computing Based SimulationSimulation
Discussion 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?
ReferenceReference
http://www.evl.uic.edu/research/http://www.evl.uic.edu/research/template_res_project.php3?indi=231template_res_project.php3?indi=231
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