A Utility-Maximizing Tasks Assignment Method for Rendering Cluster System

A utility-maximizing tasks assignment method for rendering cluster system based on feedback is proposed to solve the problem that traditional task-centered assignment method and naïve load balancing strategy cannot make full use of resources. The method uses feedback on resource usage to choose an appropriate number of threads for the renderer and then divides computing nodes of the rendering cluster system into fine grain computing units. After that, the method takes advantage of frame-to-frame coherence to assign tasks to computing units with a new static load balancing strategy. Experiments on two scene models with different complexity and comparisons with the naïve method and the fixed-threads method show that the utility-maximizing method not only reduces the rendering time of a rendering job, but also balances the load between computing units. The scalability of the proposed method is also verified on different number of computing nodes.