As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

![Parallel computing flowchart of GWR-MP](https://www.tandfonline.com/na101/home/literatum/publisher/tandf/journals/content/tgsi20/2022/tgsi20.v025.i04/10095020.2022.2064244/20221209/images/large/tgsi_a_2064244_f0002_b.jpeg)

![Parallel computing flowchart of GWR-CUDA algorithm](https://www.tandfonline.com/na101/home/literatum/publisher/tandf/journals/content/tgsi20/2022/tgsi20.v025.i04/10095020.2022.2064244/20221209/images/large/tgsi_a_2064244_f0003_b.jpeg)

![Overviews on the computational speeds of different GWR algorithms](https://www.tandfonline.com/na101/home/literatum/publisher/tandf/journals/content/tgsi20/2022/tgsi20.v025.i04/10095020.2022.2064244/20221209/images/large/tgsi_a_2064244_f0007_c.jpeg)