#catchments — Public Fediverse posts on home.social

Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
--
https://doi.org/10.1016/j.envsoft.2024.106244 <-- shared paper
--
“HIGHLIGHTS
• A memory-efficient watershed delineation algorithm was introduced.
• The new algorithm uses a node-skipping depth-first search to save memory.
• Both input and output data are stored in a shared matrix to reduce required memory.
• It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
• It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
#GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments

#gis #spatial #mapping #hydrology #water #code

Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
--
https://doi.org/10.1016/j.envsoft.2024.106244 <-- shared paper
--
“HIGHLIGHTS
• A memory-efficient watershed delineation algorithm was introduced.
• The new algorithm uses a node-skipping depth-first search to save memory.
• Both input and output data are stored in a shared matrix to reduce required memory.
• It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
• It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
#GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments

#gis #spatial #mapping #hydrology #water #code

Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
--
https://doi.org/10.1016/j.envsoft.2024.106244 <-- shared paper
--
“HIGHLIGHTS
• A memory-efficient watershed delineation algorithm was introduced.
• The new algorithm uses a node-skipping depth-first search to save memory.
• Both input and output data are stored in a shared matrix to reduce required memory.
• It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
• It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
#GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments

#gis #spatial #mapping #hydrology #water #code

Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
--
https://doi.org/10.1016/j.envsoft.2024.106244 <-- shared paper
--
“HIGHLIGHTS
• A memory-efficient watershed delineation algorithm was introduced.
• The new algorithm uses a node-skipping depth-first search to save memory.
• Both input and output data are stored in a shared matrix to reduce required memory.
• It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
• It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
#GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments

#catchments #largescale #modeling #model #meshed #benchmark

💧🌏 Greg Cocks @GregCocks · 2024-10-28 · 17:28 UTC

Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
--
https://doi.org/10.1016/j.envsoft.2024.106244 <-- shared paper
--
“HIGHLIGHTS
• A memory-efficient watershed delineation algorithm was introduced.
• The new algorithm uses a node-skipping depth-first search to save memory.
• Both input and output data are stored in a shared matrix to reduce required memory.
• It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
• It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
#GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments