CS 551/851Big Data in Computer

GraphicsGreg Humphreys

Big Data in Computer Graphics Fall 2002 Lecture 1

What does “big” mean?

• “Big” is a relative term• It happens whenever a resource is fully

consumed

“I cannot define it, but I know it when I see it”- Justice Potter Stewart

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Models

Pratt-Whitney 6000 turbine engine and rotor blade120 million cell calculation, 500,000 triangle surfaceStanford Center for Integrated Turbulence Simulations

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Models

Double Eagle Tanker Model: 83 million trianglesUNC Walkthrough Project

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Models

Scans of Saint Matthew (386 MPolys) and the David (2 GPolys) Stanford Digital Michelangelo Project

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Displays

Window system and large-screen interaction metaphorsFrançois Guimbretière, Stanford University HCI group

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Displays

Simulation of Compressible Turbulence (2K x 2K x 2K mesh)Sean Ahern and Randall Frank, LLNL

Big Data in Computer Graphics Fall 2002 Lecture 1

Big LCD Displays

Jet engine nacelle model courtesy Goodrich AerostructuresPeter Kirchner and Jim Klosowski, IBM T.J. Watson

3840

24

00

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Sloppy Displays

WireGL extensions for casually aligned displaysUNC PixelFlex team and Michael Brown, UKY

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Texture Maps153K x 153K =

73GB!!

Using Texture Mapping with Mipmapping to Render a VLSI Layout Solomon and Horowitz, DAC 2001

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Dynamic Range

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Dynamic Range

1/1000 1/500 1/250

1/125 1/60 1/30

1/15 1/8 1/4

Gradient Domain High Dynamic Range CompressionFattal, Lischinski and Werman, SIGGRAPH 2002

Big Data in Computer Graphics Fall 2002 Lecture 1

Big Chips• 63 MTransistors• 1.23 TOps/sec (!)• 10 GB/sec• 136 MTris/sec • 1.2 GPix/sec• 4 rendering pipes• 8 textures

GeForce4 die plot courtesy NVIDIA

Big Data in Computer Graphics Fall 2002 Lecture 1

Big… Everything

Realistic Modeling and Rendering of Plant EcosystemsDeussen, Hanrahan, Lintermann, Mech, Pharr and Prusinkiewicz, SIGGRAPH 1998

Big Data in Computer Graphics Fall 2002 Lecture 1

10 m

ips

What Once Was Big…Courtesy Frank Crow, Interval

0.01 s

1.0 s

100 s

104 s

106 s

1 min.

1 hr.

1 day

1 week

1 mo.log time

100 m

ips

1 g

ips

10 g

ips

100 g

ips

Fanatical

Possible

Practical

Interactive

Immersive

log performance

Teddy Bear 250 GI’s

Kitchen Table 10 GI’s

Stemware 100 MI’s

Slide courtesy Pat Hanrahan and Kurt Akeley

Big Data in Computer Graphics Fall 2002 Lecture 1

Course Information• Seminar-style: Read + discuss• Tuesday/Thursday 2:00-3:15 in Olsson 228E• Office hours MW 10:00-12:00 in Olsson 216• Discussions will be student-led• One assignment, one project• Course web page:

http://www.cs/~gfx/Courses/2002/BigData• This is an experiment. Feedback is crucial!

Big Data in Computer Graphics Fall 2002 Lecture 1

Discussions• Each student will lead at least one class• Prepared presentation for 30-45 minutes:

– Background information– Paper summaries– Key ideas– Interruptions encouraged

• Guide discussion• All students will submit 2-3 questions

about the reading before class, use those as a starting point

• Starting 9/10 (I’ll do the first three)

Big Data in Computer Graphics Fall 2002 Lecture 1

Assignment 0• Choose days to present• Submit your first three choices• Due evening of 9/3

Big Data in Computer Graphics Fall 2002 Lecture 1

Assignment: Benchmarking• Probe performance characteristics of

graphics hardware• Basics: triangle/fill rates, texture

download• Extras

– Triangle areas/shapes– Texture cache– Vertex cache– Interface bottleneck– Others?

• Due September 26th

Big Data in Computer Graphics Fall 2002 Lecture 1

Projects• Two months investigating something

cool• Need not be novel, but it helps

(especially for you graduate students)• Can work in groups no larger than 2• Writeup quality important: treat it as a

conference submission• Topic proposal due October 3rd

• Writeup/presentations due December 3rd

• Consider publishing your work…

Big Data in Computer Graphics Fall 2002 Lecture 1

About Greg• B.S.E. Princeton, 1997• Ph.D. Stanford, 2002• CTO, Ahpah Software

(Reverse-engineering technology)

• Research focus on scalable rendering using commodity technology: “Chromium”

• Writing textbook on Image Synthesis (class next semester)

• Looking for students who like serious hacking (hint)