Physical Simulation for Animation and Visual Effects: Parallelization and Characterization for Chip Multiprocessors.

Christopher J. Hughes, Radek Grzeszczuk, Eftychios Sifakis, Daehyun Kim, Sanjeev Kumar, Andrew P. Selle, Jatin Chhugani, Matthew Holliman, Yen-Kuang Chen.

In the Proceedings of IEEE/ACM International Symposium on Computer Architecture (ISCA), San Diego, California, June 2007.

Paper: pdf [0.35MB ], ps [0.29MB ]

We explore the emerging application area of physics-based simulation for computer animation and visual special effects. In particular, we examine its parallelization potential and characterize its behavior on a chip multiprocessor (CMP). Applications in this domain model and simulate natural phenomena, and often direct visual components of motion pictures. We study a set of three workloads that exemplify the span and complexity of physical simulation applications used in a production environment: fluid dynamics, facial animation, and cloth simulation. They are computationally demanding, requiring from a few seconds to several minutes to simulate a single frame; therefore, they can benefit greatly from the acceleration possible with large scale CMPs.

Starting with serial versions of these applications, we parallelize code accounting for at least 96% of the serial execution time, targeting a large number of threads. We then study the most expensive modules using a simulated 64-core CMP.

For the code in key modules, we achieve parallel scaling of 45x, 50x, and 30x for fluid, face, and cloth simulations, respectively. The modules have a spectrum of task granularity and locking behavior, and all but one are dominated by loop-level parallelism. Many modules operate on streams of data. In some cases, modules iterate over their data, leading to significant temporal locality. This streaming behavior leads to very high on-die and main memory bandwidth requirements. Finally, most modules have little inter-thread communication since they are data-parallel, but a few require heavy communication between data-parallel operations.