Non-linear prediction of rendering workload for grid infrastructure

Grid computering clusters a wide variety of geographically distributed resoures. As a result it can be considered as a promising platform for solving large scale intensive problems. For this reason, it can be viewed as one of the hotters issues in the computer society. A computational intensive application which can be gained from such a Grid infrastructure, is rendering, a process dealing with creating realistic computer-generated image and with many applications ranging from simulation to design and entertainment. To implement, however, a rendering process in a Grid infrastructure prediction of this computational complexity is required. In this paper, this is addressed by using several neural network modules, each of which is appropriate for a given rendering process. For this reason, a feature vector is constructed initially, to describe with high efficiency the parameters affecting the complexity of a rendering algorithm. The feature vector is estimated by parsing a file in a RIB format. Then, prediction is performed using a neural network model. predictions for three types of rendering algorithms are examined; the ray tracing, the radiosity and the Monte Carlo irradiance analysis.