In computer graphics, rendering is the process by which an abstract description of a scene is converted to an image. When the scene is complex, or when high-quality images or high frame rates are required, the rendering process becomes computationally demanding. To provide the necessary levels of performance, parallel computing techniques must be brought to bear. Although parallelism has been exploited in computer graphics since the early days of the field, its initial use was primarily in specialized applications. The VLSI revolution of the late 1970Õs and the advent of scalable parallel computers during the late 1980Õs changed this situation. Today, parallel hardware is routinely used in graphics workstations, and numerous software-based rendering systems have been developed for general-purpose parallel architectures. This article provides a broad introduction to the subject of parallel rendering, encompassing both hardware and software systems. The focus is on the underlying concepts and the issues which arise in the design of parallel rendering algorithms and systems. We examine the different types of parallelism and how they can be applied in rendering applications. Concepts from parallel computing, such as data decomposition, task granularity, scalability, and load balancing, are considered in relation to the rendering

The radiosity method models the interaction of light between diffuse surfaces, thereby accurately predicting global illumination effects. Due to the high computational effort to calculate the transfer of light between surfaces and the memory requirements for the scene description, a distributed, parallelized version of the algorithm is needed for scenes consisting of thousands of surfaces. We present

. Photo-realistic computer graphics is an area of research which

Ray tracing is a powerful technique to generate realistic images of 3D scenes. A

This thesis presents and analyzes scalable algorithms for dynamic load balancing and mapping in distributed computer systems. The algorithms are distributed and concurrent, have no central thread of control, and require no centralized communication. They are derived using spectral properties of graphs: graphs of physical network links among computers in the load balancing problem, and graphs of logical communication channels among processes in the mapping problem. A distinguishing characteristic of these algorithms is that they are scalable: the expected cost of execution does not increase with problem scale. This is proven in a scalability theorem which shows that, for several simple disturbance models, the rate of convergence to a solution is independent of scale. This property is extended through simulated examples and informal argument to general and random disturbances. A worst case disturbance is presented and shown to occur with vanishing probability as the problem scale increas...

Ray tracing is a powerful technique to generate realistic images of 3D scenes. A drawback is its high demand for processing power. Multiprocessing is one way to meet this demand. However, when the models are very large, special attention must be paid to the way the algorithm is parallelised. Combining demand driven and data parallel techniques provides good opportunities to arrive at an efficient scalable algorithm. Which tasks to process demand driven and which data driven, is decided by the data intensity of the task and the amount of data locality (coherence) that will be present in the task. Rays with the same origin and similar directions, such as primary rays and light rays, exhibit much coherence. These rays are therefore traced in demand driven fashion, a bundle at a time. Non-coherent rays are traced data parallel. By combining demand driven and data driven tasks, a good load balance may be achieved, while at the same time spreading the communication evenly across the network. This leads to a scalable and efficient parallel implementation of the ray tracing algorithm.