Aggarwal, VibhorDebattista, KurtDubla, PiotrBashford-Rogers, ThomasChalmers, AlanKurt Debattista and Daniel Weiskopf and Joao Comba2014-01-262014-01-262009978-3-905674-15-61727-348Xhttps://doi.org/10.2312/EGPGV/EGPGV09/103-110Parallel computing has been frequently used for reducing the rendering time of high-fidelity images, since the generation of such images has a high computational cost. Numerous algorithms have been proposed for parallel rendering but they primarily focus on utilising shared memory machines or dedicated distributed clusters. A local desktop grid, composed of arbitrary computational resources connected to a network such as those in a lab or an enterprise, provides an inexpensive alternative to dedicated clusters. The computational power offered by such a desktop grid is time-variant as the resources are not dedicated. This paper presents fault-tolerant algorithms for rendering high-fidelity images on a desktop grid within a given time-constraint. Due to the dynamic nature of resources, the task assignment does not rely on subdividing the image into tiles. Instead, a progressive approach is used that encompasses aspects of the entire image for each task and ensures that the time-constraints are met. Traditional reconstruction techniques are used to calculate the missing data. This approach is designed to avoid redundancy to maintain time-constraints. As a further enhancement, the algorithm decomposes the computation into components representing different tasks to achieve better visual quality considering the time-constraint and variable resources. This paper illustrates how the component-based approach maintains a better visual fidelity considering a given time-constraint while making use of volatile computational resources.Categories and Subject Descriptors (according to ACM CCS): Computer Graphics [I.3.1]: Hardware Architecture- Parallel processing; Computer Graphics [I.3.7]: Three-Dimensional Graphics and Realism-Ray tracing; Computer Graphics [I.3.2]: Graphics Systems-Distributed/network graphics