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CS 551/851 Big Data in Computer Graphics

CS 551/851 Big Data in Computer Graphics. Greg Humphreys. 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 Models.

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CS 551/851 Big Data in Computer Graphics

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  1. CS 551/851Big Data in Computer Graphics Greg Humphreys

  2. 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

  3. Big Models Pratt-Whitney 6000 turbine engine and rotor blade 120 million cell calculation, 500,000 triangle surface Stanford Center for Integrated Turbulence Simulations

  4. Big Models Double Eagle Tanker Model: 83 million triangles UNC Walkthrough Project

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

  6. Big Displays Window system and large-screen interaction metaphors François Guimbretière, Stanford University HCI group

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

  8. Big LCD Displays 3840 2400 Jet engine nacelle model courtesy Goodrich Aerostructures Peter Kirchner and Jim Klosowski, IBM T.J. Watson

  9. Big Sloppy Displays WireGL extensions for casually aligned displays UNC PixelFlex team and Michael Brown, UKY

  10. 153K x 153K = 73GB!! Big Texture Maps Using Texture Mapping with Mipmapping to Render a VLSI Layout Solomon and Horowitz, DAC 2001

  11. Big Dynamic Range

  12. 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 Compression Fattal, Lischinski and Werman, SIGGRAPH 2002

  13. 63 MTransistors 1.23 TOps/sec (!) 10 GB/sec 136 MTris/sec  1.2 GPix/sec 4 rendering pipes 8 textures Big Chips GeForce4 die plot courtesy NVIDIA

  14. Big… Everything Realistic Modeling and Rendering of Plant Ecosystems Deussen, Hanrahan, Lintermann, Mech, Pharr and Prusinkiewicz, SIGGRAPH 1998

  15. What Once Was Big… Courtesy Frank Crow, Interval 1 mo. log time 106 s Fanatical 1 week 1 day 104 s Possible Teddy Bear 250 GI’s 1 hr. 100 s Practical 1 min. Kitchen Table 10 GI’s 1.0 s Interactive Immersive 0.01 s Stemware 100 MI’s 1 gips 10 gips 10 mips 100 gips 100 mips log performance Slide courtesy Pat Hanrahan and Kurt Akeley

  16. 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!

  17. 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)

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

  19. 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

  20. 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…

  21. 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)

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