From Simulation to Visualization: Astrophysics Goes Hollywood Frank Summers January 17, 2002

Simulation Done for research purposes Visualization Presentation to wide audience

Simulations Attempts to replicate & explore aspects of nature on a computer Attempts to replicate & explore aspects of nature on a computer Mathematical abstraction of a physical process (equations) Mathematical abstraction of a physical process (equations) Time sequence Time sequence

Example: Earth orbiting the Sun

Earth Sun

Earth Sun

Earth Sun

Earth Sun

Planet Star

Planet Star

Object 1 Object 2

Simulation is just numbers … Position, velocity, and mass Position, velocity, and mass Object Position (AU) Velocity (km/s) Mass (g) Sun (0, 0, 0) 2 x 10 2 x Earth (1, 0, 0) (0, 30, 0) 6 x 10 6 x 10 27

… numbers changing over time Earth position changes Earth position changes DayXYZ …………

Simulation Details Initial Conditions Initial Conditions  position, velocity, density, temperature, etc. for all objects at starting time Equations Equations  gravity, hydrodynamics, radiation, magnetic fields, expansion of the universe

Simulation Details Time Evolution Time Evolution  Calculate forces, heating, other changes  Update position, velocity, etc. with new values  Repeat Data Output Data Output  Write file of positions, velocities, etc  Series of files covering simulation time

Scientific Accuracy Simulations expensive, but necessary Simulations expensive, but necessary Artist’s conceptions difficult Artist’s conceptions difficult  Well removed from normal experience  Complex 3D behaviors  Coupled feedback between physics  Scientists can’t describe it sufficiently Scientific simulations Scientific simulations  physics equations programmed in  3D, complexity, and feedbacks included

Visualization Turn those numbers into pictures Turn those numbers into pictures

Visualizations Data Transformation Data Transformation Representation Representation Choreography Choreography Rendering Rendering Compositing Compositing

Graphics vs Visualization Science Graphics Science Graphics  Pictures, plots, charts  Illustrations to scientific argument  Requires background knowledge to interpret  Representational  Content more important than form Scientific Visualization  Images and movies  Tells its own story  Must play off of audience’s knowledge  More literal  Visual message is the strongest

Data Transformation Comprehend the dataset Comprehend the dataset  What quantities?  What time period?  What are the assumptions? Convert from research quantities to more generally meaningful quantities Convert from research quantities to more generally meaningful quantities

Representation How literal? How literal? How artistic? How artistic? How best to promote message? How best to promote message? How to be least misleading? How to be least misleading?

Shading Geometry can get very complex Geometry can get very complex  ex., surface of an orange Shading solution - Use simple shapes Shading solution - Use simple shapes  ex., sphere Add complexity when drawing the surface Add complexity when drawing the surface  Texture - color, pattern  Bumps - small shape distortions  Light – reflection, transparency Programmability = Flexibility Programmability = Flexibility

Shading Example: 3 Balls

Shading Example: Teapot

Shading Stars Simple Geometry - DisksDisks w/ Star Shader

Shading Stars GaussianExponentialCombination Stars by Magnitude

Calibration using local starfield

Globular Star Cluster 47 Tucanae

Globular Star Cluster Viz Data - N-body simulation, 6144 stars (Zwart) Data - N-body simulation, 6144 stars (Zwart)  3D position, absolute brightness, mass, & type  color derived from mass, type, and spectra* Stars as point objects Stars as point objects  size depends on apparent brightness  size calculated in pixels, not 3D space Star shader in renderman Star shader in renderman  calculate app. brightness - each star, each frame  combination of gaussian and exponential glows Calibrated by reproducing constellations Calibrated by reproducing constellations

Shading Gas Clouds

Choreography Camera motion Camera motion Invaluable for giving 3D feel Invaluable for giving 3D feel Missing from most science animations Missing from most science animations

YZ Projection XY Projection

YZ Projection XY Projection

Orion Nebula

Rendering Lots of computer time Lots of computer time  Over 2 hours per frame for NASM  1500 frames – 125 CPU days Renderfarms Renderfarms  Clusters of computers dedicated to rendering

Renderfarm in 331B Gathered unused OPO machines Gathered unused OPO machines Installed Red Hat Linux Installed Red Hat Linux Connected to a switch and created an isolated private network of five machines Connected to a switch and created an isolated private network of five machines Total out-of-pocket cost $80 Total out-of-pocket cost $80 private network switch Computer 5 Red Hat Linux 7.2 P3 850 MHz Computer 4 Red Hat Linux 7.2 P2 400 MHz Computer 3 Red Hat Linux 7.2 P2 400 MHz Computer 2 Red Hat Linux 7.2 Dual P3 800 MHz Computer 1 Red Hat Linux 7.2 Dual P3 933 MHz

Master Computer Node 7 Node 9 Node 8 Node 4 Node 6 Node 2 Node 5 Node 3 Node 1 Node 11 Node 10 Node 13 Node 12 Node 14 Node 15 Node Computing Cluster Display Visualization Wall Schematic

Compositing Add multiple elements together Add multiple elements together Provide context Provide context

Data - N-body + Hydro simulation Data - N-body + Hydro simulation  262,144 particles (Mihos & Hernquist)  young stars, old stars, gas, dark matter Galaxy Collision Viz