Data size has far exceeded compute speed. Most complex tasks are followed by long run-times before we get any answers. GPUs are one of the easiest ways to parallelize large computations, with considerable time savings. cuSpatial is a FOSS library for accelerating spatial workflows on the GPU.

cuSpatial is an open-source indexing, transform, and geometry library being developed within the RAPIDS ecosystem. RAPIDS is a collection of FOSS high-performance GPU data science libraries for Data Science, Machine Learning, numerical analysis, and geospatial analytics.

cuSpatial is in development and enables GPU acceleration for a number of common GIS workflows including:

• GeoPandas integration
• fast I/O with Apache Arrow and Apache Parquet
• point-in-polygon
• cubic spline fitting
• hausdorff-distance based clustering
• haversine distance and geographic to euclidean projection