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* [https://vs.sav.sk/?lang=en§ion=departments&sub=vvt&sub2=services Supercomputer "Aurel"], 4096 CPU cores (Power7 architecture), features GRASS GIS 7.4 | * [https://vs.sav.sk/?lang=en§ion=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://projects.ncsu.edu/hpc//Software/Software.php henry2 at NCSU] (Intel Xeon based Linux cluster; GRASS GIS 7.2.0 installed around 2017) | |||
* [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 20:14, 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
- Supercomputer "Aurel", 4096 CPU cores (Power7 architecture), features GRASS GIS 7.4
- GRASS GIS in JRC's JEODPP, A versatile data-intensive computing platform for information retrieval from big geospatial data
- henry2 at NCSU (Intel Xeon based Linux cluster; GRASS GIS 7.2.0 installed around 2017)
- 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 also
Related wiki pages
- https://grasswiki.osgeo.org/wiki/Parallel_GRASS_jobs
- https://grasswiki.osgeo.org/wiki/Working_with_GRASS_without_starting_it_explicitly
- https://grasswiki.osgeo.org/wiki/GRASS_and_Shell
- https://grasswiki.osgeo.org/wiki/GRASS_GIS_Performance
- https://grasswiki.osgeo.org/wiki/Large_raster_data_processing
- https://grasswiki.osgeo.org/wiki/Large_vector_data_processing
Presentations
- https://fosdem.org/2018/schedule/event/geo_grass/
- https://archive.fosdem.org/2015/schedule/event/grass_7/
- State of GIS at the High Performance Computing Cluster (2012)
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: 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:
- Special issue "High-Performance Computing in Geoscience and Remote Sensing", Sensors (ISSN 1424-8220; CODEN: SENSC9)