Difference between revisions of "Supercomputing"

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(Overview of GRASS GIS in supercomputing environments: add NCSU and NCSA UIUC)
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* [https://vs.sav.sk/?lang=en&section=departments&sub=vvt&sub2=services Supercomputer "Aurel"], 4096 CPU cores (Power7 architecture), features GRASS GIS 7.4
 
* [https://vs.sav.sk/?lang=en&section=departments&sub=vvt&sub2=services Supercomputer "Aurel"], 4096 CPU cores (Power7 architecture), features GRASS GIS 7.4
 
* GRASS GIS in JRC's JEODPP, [https://doi.org/10.1016/j.future.2017.11.007 A versatile data-intensive computing platform for information retrieval from big geospatial data]
 
* GRASS GIS in JRC's JEODPP, [https://doi.org/10.1016/j.future.2017.11.007 A versatile data-intensive computing platform for information retrieval from big geospatial data]
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* [https://projects.ncsu.edu/hpc//Software/Software.php henry2 at NCSU] (Intel Xeon based Linux cluster; GRASS GIS 7.2.0 installed around 2017)
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* [https://wiki.ncsa.illinois.edu/pages/viewpage.action?pageId=47294247 ROGER, the CyberGIS supercomputer] at NCSA UIUC (batch compute nodes: 24x, 10 cores, 2.6 GHz, 256 GB RAM, 500 GB of local storage, cluster-wide General Parallel File System (GPFS) 4.5PB; GRASS GIS available alongside GDAL, PDAL, Geotools, and R)
  
 
= Processing Practices =
 
= Processing Practices =

Revision as of 13:14, 23 May 2018

This page aims to

  • overview installations of GRASS GIS in supercomputing (HPC, HTC) environments and related applications
  • document best practices of processing big geospatial data, common mistakes and errors and how to work-around them

Overview of GRASS GIS in supercomputing environments

Processing Practices

See also

Related wiki pages

Presentations

Publications

Upcoming in 2018: