Parallel visualization on large clusters using MapReduce

Huy T. Vo, Jonathan Bronson, Brian Summa, João L D Comba, Juliana Freire, Bill Howe, Valerio Pascucci, Cláudio T. Silva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Large-scale visualization systems are typically designed to efficiently "push" datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 80000 2 pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster.

Original languageEnglish (US)
Title of host publication1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
Pages81-88
Number of pages8
DOIs
StatePublished - 2011
Event1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Providence, RI, United States
Duration: Oct 23 2011Oct 24 2011

Publication series

Name1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings

Other

Other1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011
CountryUnited States
CityProvidence, RI
Period10/23/1110/24/11

Keywords

  • Hadoop
  • MapReduce
  • cloud computing
  • gigapixels
  • large meshes
  • volume rendering

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Vo, H. T., Bronson, J., Summa, B., Comba, J. L. D., Freire, J., Howe, B., Pascucci, V., & Silva, C. T. (2011). Parallel visualization on large clusters using MapReduce. In 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings (pp. 81-88). [6092321] (1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings). https://doi.org/10.1109/LDAV.2011.6092321