The Community for Technology Leaders
RSS Icon
Issue No.03 - May-June (2012 vol.32)
pp: 24-36
Kurt Debattista , University of Warwick
Thomas Bashford-Rogers , University of Warwick
Vibhor Aggarwal , University of Warwick
Alan Chalmers , University of Warwick
Traditionally, high computational costs have restricted high-fidelity interactive rendering to expensive shared-memory or dedicated distributed processors. Desktop grids offer a low-cost alternative by combining arbitrary computational resources connected to a network, such as the resources in a laboratory or an office. However, the prevalent interactive rendering algorithms can't seamlessly handle the variable computational power offered by a desktop grid's nondedicated resources. A proposed fault-tolerant algorithm renders high-fidelity images at an interactive rate that can handle variable resources. A conventional approach of rescheduling failed jobs in a volatile environment would inhibit performance when rendering at interactive rates because the time margins are small. Instead, this method uses quasi-random sampling along with image reconstruction. This video shows examples of scenes rendered on a desktop grid.
interactive rendering, desktop grids, fault tolerance, image reconstruction, high-fidelity rendering, parallel computing, computer graphics, graphics and multimedia
Kurt Debattista, Thomas Bashford-Rogers, Vibhor Aggarwal, Alan Chalmers, "High-Fidelity Interactive Rendering on Desktop Grids", IEEE Computer Graphics and Applications, vol.32, no. 3, pp. 24-36, May-June 2012, doi:10.1109/MCG.2010.67
1. C. Benthin, I. Wald, and P. Slusallek, “A Scalable Approach to Interactive Global Illumination,” Computer Graphics Forum, vol. 22, no. 3, 2003, pp. 621–630.
2. I. Wald et al., “Interactive Global Illumination Using Fast Ray Tracing,” Proc. 13th Eurographics Workshop Rendering (EGWR 02), Eurographics Assoc., 2002, pp. 15–24.
3. B. Walter, G. Drettakis, and S. Parker, “Interactive Rendering Using the Render Cache,” Proc. 10th Eurographics Workshop Rendering (EGWR 99), Eurographics Assoc., 1999, pp. 19–30.
4. A. Chalmers, T. Davis, and E. Reinhard eds., Practical Parallel Rendering, A K Peters, 2002.
5. T. Kollig, and A. Keller, “Efficient Multidimensional Sampling,” Computer Graphics Forum, vol. 21, no. 3, 2002, pp. 557–563.
6. M. Litzkow, M. Livny, and M. Mutka, “Condor—a Hunter of Idle Workstations,” Proc. 8th Int'l Conf. Distributed Computing Systems, IEEE Press, 1988, pp. 104–111.
7. J.T. Kajiya, “The Rendering Equation,” Proc. Siggraph, ACM Press, 1986, pp. 143–150.
8. S. Daly, “The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity,” Digital Images and Human Vision, MIT Press, 1993, pp. 179–206.
9. V. Aggarwal et al., “Time-Constrained High-Fidelity Rendering on Local Desktop Grids,” Proc. 2009 Eurographics Symp. Parallel Graphics and Visualization, Eurographics Assoc., 2009, pp. 103–110.
30 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool