Accelerating TauDEM for extracting hydrology information from national-scale high resolution topographic dataset

Ahmet Artu Yildirim, David Tarboton, Yan Liu, Nazmus Shams Sazib, Shaowen Wang

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

Abstract

We present performance improvements on parallel hydrology algorithms in TauDEM suite that allowed us to process the 10m USGS 3DEP topographic dataset (667GB, 180 billion raster cells) by adopting a block-wise data decomposition feature and maximazing the disk parallelism.

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

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Keywords

  • HPC
  • Parallel pitremove algorithm
  • TauDEM

ASJC Scopus subject areas

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

Cite this

Yildirim, A. A., Tarboton, D., Liu, Y., Sazib, N. S., & Wang, S. (2016). Accelerating TauDEM for extracting hydrology information from national-scale high resolution topographic dataset. In Proceedings of XSEDE 2016: Diversity, Big Data, and Science at Scale [a3] (ACM International Conference Proceeding Series; Vol. 17-21-July-2016). Association for Computing Machinery. https://doi.org/10.1145/2949550.2949582