Jun Ni, Ph.D. M.E Research Services, ITS

Distributed Physically-based Art and Live Animation on the GRIDPresented at Prof. Joe Kearney’s lecture Jun Ni, Ph.D. M.E Research Services, ITS

Interactive Kites FlyingShalini Venkataraman, Dept. of CS EVL, University of Chicago NCSA, University of Illinois

Outline • 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 • Geologically distributed “virtual supercomputer” in virtual organization • 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 tele-immersive VR application

State of the art of high performance computing tele-immersive VR application on internet • Globally network-based • Physical model based scientific animation • Tele-immersive VR application • Art design

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 • 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 • 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 • 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 • 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 • 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 • 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 • Internal and external forces acting on each point of grids • Wind forces Fw(i,j) = m n(i,j) [ w – v(i,j) ] n (i,j) Air or fluid viscosity

Physically Based Model • Internal and external forces acting on each point of grids • viscous forces Fw(i,j) = - m n(i,j) v(i,j) Damping coefficient

Implementation with VR • CAVE VR environment • EVL’s CAVE • CAVE Library

Implementation with VR Head controlled by wand in CAVE system • 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)

Implementation with VR Head controlled by wand in CAVE system • 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

Implementation with VR Head controlled by wand in CAVE system • 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.

Grid computing based simulation • Distributed simulation • Small time steps • Grid enhanced • High-speed network based • Architecture of gird enhanced application to kite simulation

Grid Computing Based Simulation • 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 Simulation • 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 Simulation • 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 Simulation • 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?

Reference • http://www.evl.uic.edu/research/template_res_project.php3?indi=231

Jun Ni, Ph.D. M.E Research Services, ITS