GRASS GSoC 2024 Parallelize Tools - Revision history

Veroandreo: project won't be executed

2024-05-28T20:05:34Z

project won't be executed

← Older revision		Revision as of 20:05, 28 May 2024
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	;Abstract		;Abstract
	::This project aims to parallelize geospatial tools/algorithms to enhance our abilities to utilize and analyze large-scale geospatial and remote sensing data. Although many geographic information system (GIS) tools are widely used, they haven’t been parallelized yet. The serial computation, while functional for modest datasets, significantly restricts the processing time and scalability necessary for handling increasingly larger geospatial datasets. Leveraging the potential of modern computing hardware, we can parallelize tools to mitigate these limitations. Several tools could benefit from parallelization, such as r.texture, r.horizon, r.fill.stats, r/v.surf.idw, r.viewshed, v.to.rast, r.to.vect, r.grow.distance, etc. These tools can be speeded up by parallelization with OpenMP and MPI. Moreover, CUDA can be used for these tools to compute raster and image processing on GPU.
	~~__TOC__~~		This project aims to parallelize geospatial tools/algorithms to enhance our abilities to utilize and analyze large-scale geospatial and remote sensing data. Although many geographic information system (GIS) tools are widely used, they haven’t been parallelized yet. The serial computation, while functional for modest datasets, significantly restricts the processing time and scalability necessary for handling increasingly larger geospatial datasets. Leveraging the potential of modern computing hardware, we can parallelize tools to mitigate these limitations. Several tools could benefit from parallelization, such as r.texture, r.horizon, r.fill.stats, r/v.surf.idw, r.viewshed, v.to.rast, r.to.vect, r.grow.distance, etc. These tools can be speeded up by parallelization with OpenMP and MPI. Moreover, CUDA can be used for these tools to compute raster and image processing on GPU.
	~~== Application ==~~
			;Note:
			Unfortunately, for administrative reasons out of the student's and GRASS GIS project control, this Google Summer of Code accepted proposal won't be executed.


	{{GSoC}}		{{GSoC}}

Annakrat: Add category GSoC

2024-05-13T17:02:55Z

Add category GSoC

← Older revision		Revision as of 17:02, 13 May 2024
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	__TOC__		__TOC__
	== Application ==		== Application ==


			{{GSoC}}

Cyliang: Created page with "Accepted Google Summer of Code 2024 project. {| {{table}} |Student Name: || Chung-Yuan Liang, [https://www.purdue.edu/ Purdue University, West Lafayette, USA] |- |Organization: || [http://www.osgeo.org OSGeo - Open Source Geospatial Foundation] |- | Mentor Name: || Huidae Cho, Vaclav Petras, Maris Nartiss |- | Title: || '''Parallelization of existing tools in GRASS GIS''' |- |} ;Abstract ::This project aims to parallelize geospatial tools/algorithms to..."

2024-05-10T15:36:49Z

Created page with "Accepted Google Summer of Code 2024 project. {| {{table}} |Student Name: || Chung-Yuan Liang, [https://www.purdue.edu/ Purdue University, West Lafayette, USA] |- |Organization: || [http://www.osgeo.org OSGeo - Open Source Geospatial Foundation] |- | Mentor Name: || Huidae Cho, Vaclav Petras, Maris Nartiss |- | Title: || '''Parallelization of existing tools in GRASS GIS''' |- |} ;Abstract ::This project aims to parallelize geospatial tools/algorithms to..."

New page

Accepted Google Summer of Code 2024 project.

{| {{table}}
|Student Name: || [[User:cyliang|Chung-Yuan Liang]], [https://www.purdue.edu/ Purdue University, West Lafayette, USA]
|-
|Organization: || [http://www.osgeo.org OSGeo - Open Source Geospatial Foundation]
|-
| Mentor Name: || Huidae Cho, Vaclav Petras, Maris Nartiss
|-
| Title: || '''Parallelization of existing tools in GRASS GIS'''
|-
|}

;Abstract
::This project aims to parallelize geospatial tools/algorithms to enhance our abilities to utilize and analyze large-scale geospatial and remote sensing data. Although many geographic information system (GIS) tools are widely used, they haven’t been parallelized yet. The serial computation, while functional for modest datasets, significantly restricts the processing time and scalability necessary for handling increasingly larger geospatial datasets. Leveraging the potential of modern computing hardware, we can parallelize tools to mitigate these limitations. Several tools could benefit from parallelization, such as r.texture, r.horizon, r.fill.stats, r/v.surf.idw, r.viewshed, v.to.rast, r.to.vect, r.grow.distance, etc. These tools can be speeded up by parallelization with OpenMP and MPI. Moreover, CUDA can be used for these tools to compute raster and image processing on GPU.
__TOC__
== Application ==