We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Parallelization for big EO data processing

Formale Metadaten

Titel
Parallelization for big EO data processing
Serientitel
Anzahl der Teile
17
Autor
Mitwirkende
Lizenz
CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache
Produzent
Produktionsjahr2023
ProduktionsortWageningen

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
High-resolution, continental-scale modeling enabled by modern, massive datasets, requires development of scalable geoprocessing workflows. To enable participants to effectively use available computational resources (laptop, desktop, institutional HPC), she introduced basic parallelization concepts such as parallelization efficiency and scaling. She explained various approaches to parallelization in GRASS GIS, an open source geoprocessing engine, that rely on OpenMP, Python and Bash. In the hands-on part, participants speeded up an urban growth model by parallelizing different parts of this complex geoprocessing workflow using techniques that are easily applicable to a wide range of analyses and computational resources. The workshop was run in a Jupyter Notebook environment using GRASS GIS Python API to run GRASS tools and visualize results of the analysis in a reproducible way. Participants were able to either run the workshop on their laptops (see instructions) or in a cloud environment (using WholeTale, no installation required).