CS 551/851 Big Data in Computer Graphics Greg 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 blade 120 million cell calculation, 500,000 triangle surface Stanford Center for Integrated Turbulence Simulations

Big Data in Computer Graphics Fall 2002 Lecture 1 Big Models Double Eagle Tanker Model: 83 million triangles UNC 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 metaphors François 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 Aerostructures Peter Kirchner and Jim Klosowski, IBM T.J. Watson

Big Data in Computer Graphics Fall 2002 Lecture 1 Big Sloppy Displays WireGL extensions for casually aligned displays UNC PixelFlex team and Michael Brown, UKY

Big Data in Computer Graphics Fall 2002 Lecture 1 Big Texture Maps 153K 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 1/10001/5001/250 1/1251/601/30 1/151/81/4 Gradient Domain High Dynamic Range Compression Fattal, 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 Ecosystems Deussen, Hanrahan, Lintermann, Mech, Pharr and Prusinkiewicz, SIGGRAPH 1998

Big Data in Computer Graphics Fall 2002 Lecture 1 10 mips What Once Was Big… Courtesy Frank Crow, Interval 0.01 s 1.0 s 100 s 10 4 s 10 6 s 1 min. 1 hr. 1 day 1 week 1 mo. log time 100 mips 1 gips 10 gips 100 gips 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: 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 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 3 rd Writeup/presentations due December 3 rd 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)