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).