FOSS4G - FOSS4GPU: Faster GIS via GPU and cuSpatial
Channel: FOSS4G
Date: 24/10/2021

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
This is a medium-level technical talk describing the relevance, development of, and use of the cuSpatial library. The talk will progress as follows:
1. Me, NVIDIA
2. Why GPUs? Growing datasets and stagnating performance.
3. RAPIDS: A suite of GPU accelerated FOSS data science libraries
4. cuSpatial: A python library that integrates with GeoPandas and uses the GPU
5. Examples and uses of cuSpatial APIs
6. Interesting future cuSpatial APIs
7. Using cuSpatial with GeoPandas to read and write data
8. Using cuSpatial with cudf's parquet reader for Arrow-based I/O
9. Premade benchmark examples comparing cuSpatial to CPU based methods
10. Call to action to give it a try, give feedback, or contribute to our github repository.

Authors and Affiliations –
Thomson Comer
NVIDIA

Track –
Software

Topic –
Data collection, data sharing, data science, open data, big data, data exploitation platforms

Level –
2 - Basic. General basic knowledge is required.

Language of the Presentation –
English