Parallel Methods for Real-Time Visualization of Snow

Abstract

Using computer generated imaging is becoming more and more popular in areas such as computer gaming, movie industry and simulation. A familiar scene in the winter months for most us in the Nordic countries is snow. This thesis discusses some of the complex numerical algorithms behind snow simulations. Previous methods for snow simulation have either covered only a very limited aspect of snow, or have been unsuitable for real-time performance. In this thesis, some of these methods are combined into a model for real-time snow simulation that handles both snowflake motion through the air, wind simulation, and accumulation of snow on objects and the ground. With a goal towards achieving real-time performance with more than 25 frames per second, some new parallel methods for the snow model are introduced. Focus is set on efficient parallelization on new SMP and multi-core computer systems. The algorithms are first parallelized in a pure data-parallel manner by dividing the data structures among threads. This scheme is then improved by overlapping inherently sequential algorithms with computations for the following frame, to eliminate processor idle time. A speedup of 1.9 on modern dual CPU workstations is achieved, while displaying a visually satisfying result in real-time. By utilizing Hyper-Threading enabled dual CPU systems, the speedup is further improved to 2.0.