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= Overview of GRASS GIS in supercomputing environments =
= Overview of GRASS GIS in supercomputing environments =


* GRASS GIS 8 on LUMI Supercomputer in Finland: https://docs.csc.fi/apps/grass/
* [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]
* [https://hpc.ncsu.edu/Software/Apps.php?app=gis Hazel at NCSU] (Intel Xeon based Linux cluster; GRASS GIS available since 2017)
Past:
* [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 =
See page [[Parallel GRASS jobs]] for Cluster and Grid computing with parallelized code, Job scheduler, and GRASS on a cluster


= See also =
= See also =
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== Publications ==
== Publications ==
* Alvioli, M., A. C. Mondini, F. Fiorucci, M. Cardinali & I. Marchesini (2018) Topography-driven satellite imagery analysis for landslide mapping, Geomatics, Natural Hazards and Risk, 9:1, 544-567, DOI: [https://doi.org/10.1080/19475705.2018.1458050 10.1080/19475705.2018.1458050]
* Delucchi, L., Neteler, M. (2011): g.cloud module for GRASS GIS, FOSS4G 2011 Denver, Slides: https://www.slideshare.net/lucadelu/grass-cloud
* Neteler, M. (2008): Building a cluster for GRASS GIS and other software from the OSGeo stack, https://courses.neteler.org/building-a-cluster-for-grass-gis-and-other-software-from-the-osgeo-stack/


Upcoming in 2018:
* [http://www.mdpi.com/si/15134 Special issue "High-Performance Computing in Geoscience and Remote Sensing"], Sensors (ISSN 1424-8220; CODEN: SENSC9)
* [http://www.mdpi.com/si/15134 Special issue "High-Performance Computing in Geoscience and Remote Sensing"], Sensors (ISSN 1424-8220; CODEN: SENSC9)
* https://doi.org/10.1080/19475705.2018.1458050
 
== Miscellaneous ==
 
* [http://europa.eu/!qk37Tr The European High-Performance Computing Joint Undertaking - EuroHPC]

Latest revision as of 14:17, 19 February 2024

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

Past:

  • 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

See page Parallel GRASS jobs for Cluster and Grid computing with parallelized code, Job scheduler, and GRASS on a cluster

See also

Related wiki pages

Presentations

Publications

Upcoming in 2018:

Miscellaneous