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Leveraging XSEDE HPC resources to address computational challenges with high-resolution topography data

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Shaowen WangAuthors Info & Claims

XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment

Article No.: 59, Pages 1 - 2

https://doi.org/10.1145/2616498.2616564

Published: 13 July 2014 Publication History

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Abstract

Leveraging service-oriented architectures and taking advantage of the high-performance compute resources provided by XSEDE, we have developed standards-based web services to address the challenges associated with processing large volumes of high resolution topography data. These web services make results from community software packages and other cyberinfrastructure-based applications available to the wider earth sciences community via the OpenTopography Facility and the CyberGIS Gateway.

References

[1]

Tesfa, T.K., Tarboton, D.G., Watson, D.W., Schreuders, K.A.T., Baker, M.E., Wallace, R.M., 2011. Extraction of hydrological proximity measures from DEMs using parallel processing. Environmental Modelling & Software 26(12) 1696--1709, http://dx.doi.org/10.1016/j.envsoft.2011.07.018.

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[2]

Padmanabhan, A., Youn, C., Hwang, M., Liu, Y., Wang, S., Wilkins-Diehr, N., Crosby, C. 2013. Integration of Science Gateways: A Case Study with CyberGIS and OpenTopography, In Proceeding of XSEDE '13 Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, ACM, 28p. DOI=10.1145/2484762.2484808

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Zhang WWitharana CLiljedahl AKanevskiy M(2018)Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial ImageryRemote Sensing10.3390/rs1009148710:9(1487)Online publication date: 18-Sep-2018
https://doi.org/10.3390/rs10091487
Nandigam VCrosby CArrowsmith RPhan MLin KGross BHart D(2017)Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopographyPractice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact10.1145/3093338.3093345(1-3)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3093338.3093345
Li ZHodgson MLi W(2017)A general-purpose framework for parallel processing of large-scale LiDAR dataInternational Journal of Digital Earth10.1080/17538947.2016.126984211:1(26-47)Online publication date: 6-Jan-2017
https://doi.org/10.1080/17538947.2016.1269842
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Index Terms

Leveraging XSEDE HPC resources to address computational challenges with high-resolution topography data
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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XSEDE16: Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale

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 ...
Integration of science gateways: a case study with CyberGIS and OpenTopography
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Science gateways are collaborative software environments designed to enable community-driven development and use of cyberinfrastructure services, software tools, applications, and data through common interfaces, typically an online portal, customized to ...
Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopography
PEARC '17: Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact

The OpenTopography science gateway provides efficient online access to high resolution topographic data and processing tools for a broad spectrum of research communities. We have integrated XSEDE HPC resources into the OpenTopography processing workflow ...

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Information

Published In

XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment

July 2014

445 pages

ISBN:9781450328937

DOI:10.1145/2616498

General Chair:
Scott Lathrop
National Center for Supercomputing Applications
,
Program Chair:
Jay Alameda
National Center for Supercomputing Applications

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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NSF: National Science Foundation
Drexel University
Indiana University: Indiana University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2014

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XSEDE '14

XSEDE '14: Annual Conference of the Extreme Science and Engineering Discovery Environment

July 13 - 18, 2014

GA, Atlanta, USA

Acceptance Rates

XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;

Overall Acceptance Rate 129 of 190 submissions, 68%

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Cited By

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Zhang WWitharana CLiljedahl AKanevskiy M(2018)Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial ImageryRemote Sensing10.3390/rs1009148710:9(1487)Online publication date: 18-Sep-2018
https://doi.org/10.3390/rs10091487
Nandigam VCrosby CArrowsmith RPhan MLin KGross BHart D(2017)Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopographyPractice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact10.1145/3093338.3093345(1-3)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3093338.3093345
Li ZHodgson MLi W(2017)A general-purpose framework for parallel processing of large-scale LiDAR dataInternational Journal of Digital Earth10.1080/17538947.2016.126984211:1(26-47)Online publication date: 6-Jan-2017
https://doi.org/10.1080/17538947.2016.1269842
Swetnam TPelletier JRasmussen CCallahan NMerchant NLyons ERynge MLiu YNandigam VCrosby CGaither K(2016)Scaling GIS analysis tasks from the desktop to the cloud utilizing contemporary distributed computing and data management approachesProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949573(1-6)Online publication date: 17-Jul-2016
https://dl.acm.org/doi/10.1145/2949550.2949573

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Abstract

References

Cited By

Index Terms

Recommendations

Accelerating TauDEM for Extracting Hydrology Information from National-Scale High Resolution Topographic Dataset: Extended Abstract

Integration of science gateways: a case study with CyberGIS and OpenTopography

Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopography

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