Scaling GIS analysis tasks from the desktop to the cloud utilizing contemporary distributed computing and data management approaches: A case study of project-based learning and cyberinfrastructure concepts

T. L. Swetnam, J. D. Pelletier, C. Rasmussen, N. R. Callahan, N. Merchant, E. Lyons, M. Rynge, Y. Liu, V. Nandigam, C. Crosby

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

1 Scopus citations

Abstract

In this paper we present the experience of scaling in parallel a geographic information system modeling framework to hundreds of processors. The project began in an active learning cyberinfrastructure course which was followed by an XSEDE ECSS effort in collaboration across multiple-institutions.

Original languageEnglish (US)
Title of host publicationProceedings of XSEDE 2016
Subtitle of host publicationDiversity, Big Data, and Science at Scale
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450347556
DOIs
StatePublished - Jul 17 2016
EventConference on Diversity, Big Data, and Science at Scale, XSEDE 2016 - Miami, United States
Duration: Jul 17 2016Jul 21 2016

Publication series

NameACM International Conference Proceeding Series
Volume17-21-July-2016

Other

OtherConference on Diversity, Big Data, and Science at Scale, XSEDE 2016
CountryUnited States
CityMiami
Period7/17/167/21/16

Keywords

  • CyberGIS
  • GDAL
  • GRASS
  • Makeflow
  • Work Queue

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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    Swetnam, T. L., Pelletier, J. D., Rasmussen, C., Callahan, N. R., Merchant, N., Lyons, E., Rynge, M., Liu, Y., Nandigam, V., & Crosby, C. (2016). Scaling GIS analysis tasks from the desktop to the cloud utilizing contemporary distributed computing and data management approaches: A case study of project-based learning and cyberinfrastructure concepts. In Proceedings of XSEDE 2016: Diversity, Big Data, and Science at Scale [a21] (ACM International Conference Proceeding Series; Vol. 17-21-July-2016). Association for Computing Machinery. https://doi.org/10.1145/2949550.2949573