Workload Prediction of Rendering Algorithms in GRID Computing

Computational Grids are a promising platform for solving large-scale resource intensive problems [1]. The concept of grid computing is gaining popularity in the last decade due to the rapid growth of the Internet as a medium for global communication and the development of faster hardware and more sophisticated software. Grid computing clusters wide variety of geographically distributed computational resources, including supercomputers, PC's, PDA's and workstations, and presents them as a single unified integrated resource. Many computationally intensive problems can be solved within a more feasible/ reasonable time and cost based on a grid infrastructure than using a single resource supercomputing scheme [2]. A computationally intensive application, which can benefit from grid infrastructure, is rendering. Rendering is the process of creating realistic images from synthetic three-dimensional (3D) geometrical models and is an essential tool for many fields, ranging from simulation and design to education, entertainment and advertisement [3]. In order to implement a rendering algorithm to a grid infrastructure, two main issues should be addressed. The first refers to the parallelization of the rendering process, while the second to the way of allocating different tasks to the available resources. Parallelization of a rendering process can be performed in frame domain. In this case, each frame of the generated video sequence is handled independently from the previous frames and thus can be assigned to a different resource for processing. The second issue is solved by applying an appropriate scheduling scheme such as the Earliest Deadline First (EDF) and requires prediction of the workload of the rendering algorithm to be implemented. A block diagram of the proposed architecture is shown in Figure 1. Workload prediction of a rendering process is a difficult task, since many parameters are involved, which affect the computational complexity in a non-linear and complex way. For this reason, in our case, the rendering workload is modeled using a continuous non-linear function ) (x c g y = , where vector x includes the rendering parameters and y is the respective computational cost. Index c of function )