Review: Large scale distributed visualization on computational Grids: A review

  • Authors:
  • Lizhe Wang;Dan Chen;Ze Deng;Fang Huang

  • Affiliations:
  • Pervasive Technology Institute, Indiana University, Bloomington, IN 47408, USA;School of Computer Science, China University of Geosciences, Wuhan, 430074 Hubei, PR China and School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK;School of Computer Science, China University of Geosciences, Wuhan, 430074 Hubei, PR China;Institute of Geo-Spatial Information Technology, College of Automation, University of Electronic Science and Technology of China, Chengdu 611731, PR China

  • Venue:
  • Computers and Electrical Engineering
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Advances in science and engineering have put high demands on tools for high performance large-scale data exploration and analysis. Visualization is a powerful technology for analyzing data and presenting results. Todays science and engineering have benefited from state-of-the-art of Grid technologies and modern visualization systems. To visualize the large amount of data, rendering technologies are widely used to parallelize visualization tasks over distributed resources on computational Grids. It raises the necessity to balance the computational load and to minimize the network bandwidth requirements. This article explains in Grid environments how new approaches of visualization architecture and load-balancing algorithms address these challenges in a principled fashion. The Grid infrastructure that supports large scale distributed visualization is also introduced. Some typical visualization systems on Grids are referenced for discussions.