Time-constrained high-fidelity rendering on local desktop grids

  • Authors:

  • Vibhor Aggarwal;Kurt Debattista;Piotr Dubla;Thomas Bashford-Rogers;Alan Chalmers

  • Affiliations:

  • The Digital Lab, University of Warwick, UK;The Digital Lab, University of Warwick, UK;The Digital Lab, University of Warwick, UK;The Digital Lab, University of Warwick, UK;The Digital Lab, University of Warwick, UK

  • Venue:
  • EG PGV'09 Proceedings of the 9th Eurographics conference on Parallel Graphics and Visualization
  • Year:
  • 2009

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Abstract

Parallel 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.