Multithreading for Visual Effects

Martin Watt, Erwin Coumans, George ElKoura, Ronald Henderson, Manuel Kraemer, Jeff Lait, James Reinders

July 29, 2014 by A K Peters/CRC Press
Reference - 255 Pages - 99 B/W Illustrations
ISBN 9781482243567 - CAT# K23439

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Features

  • Provides practical advice on multithreading techniques and visual effects
  • Gives first-hand insight into the architectures and software behind the proprietary animation systems from two major animation studios: DreamWorks Animation and Pixar
  • Presents a unique look into Houdini, the industry’s leading visual effects package
  • Covers hugely popular visual effects libraries, including Bullet, OpenVDB, and OpenSubdiv
  • Offers course notes and other material on the authors’ website
Watch co-author Ron Henderson accept his 2014 Academy of Motion Picture Arts and Sciences Technical Achievement Award for the development of the FLUX gas simulation system.

Summary

Tackle the Challenges of Parallel Programming in the Visual Effects Industry

In Multithreading for Visual Effects, developers from DreamWorks Animation, Pixar, Side Effects, Intel, and AMD share their successes and failures in the messy real-world application area of production software. They provide practical advice on multithreading techniques and visual effects used in popular visual effects libraries (such as Bullet, OpenVDB, and OpenSubdiv), one of the industry’s leading visual effects packages (Houdini), and proprietary animation systems. This information is valuable not just to those in the visual effects arena, but also to developers of high performance software looking to increase performance of their code.

Diverse Solutions to Solve Performance Problems

After an introductory chapter, each subsequent chapter presents a case study that illustrates how the authors used multithreading techniques to achieve better performance. The authors discuss the problems that occurred and explain how they solved them. The case studies encompass solutions for shaving milliseconds, solutions for optimizing longer running tasks, multithreading techniques for modern CPU architectures, and massive parallelism using GPUs. Some of the case studies include open source projects so you can try out these techniques for yourself and see how well they work.