Parallel Methods for Real-Time Visualization of Snow

Abstract

In this thesis, we look at the underlying numerical fluid equations for simulation of snow and look at how to optimize and parallelize these routines as well as how to display the results efficiently through OpenGL.Multiprocessor platforms will be considered. The ultimate goal is to acheive near real-time realistic simulations of snow. AbstractUsing 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. i

Methodology for geographical data evolution: three-dimensional particle-based real-time snow simulation with remote-sensing data

“…In accordance with the observational and statistical data, 13 the diameter range of a snowflake is between 0.05 and 4.6 mm, indicating the difficulty of crystalline lens absorption of water vapor in the atmosphere. This follows the rule that the higher the temperature, the greater the water saturation, thus leading to larger snowflakes.…”

“…However, they still adopted image-filtering methods to simulate the statistical properties of snowfall spatial distributions, ignoring the influence of wind and airflow, both of which caused a low-quality simulation. In 2006, Saltvik et al 13 also conducted experiments on simulations of snow and snow scenes with the parallel method. This method supported much more snow particles in the scene than before, but it had an obvious defect in the dynamic deposit simulation of the snow, in which it did not adopt the particle system model and did not include any interaction between wind and snow.…”

Data acquisition and simulation of natural phenomena

“…But simulation results of this method seemed to be lacking some realistic features. There are also some studies on the dynamic phenomena of the snow, such as wind blown snow [80,81], snow [82], avalanche [83,84].…”

Diffusion‐Based Snow Cover Generation

“…Recently, a voxel‐based approach to large‐scale winter scenery synthesization was proposed [MGG*10]. There were also attempts to parallelize snow simulations [SEN08] or make the simulation run in real time [HAH02].…”