Efficient spatial co-location pattern mining on multiple GPUs

•A generic parallel algorithm for Co-location Pattern Mining.•Support for multi GPU architectures.•A specialized variant of the algorithm optimized for the NVIDIA GPUs.•Memory-aware solution.•A dedicated algorithm for compression of input data. In this paper, we investigate Co-location Pattern Mining (CPM) from big spatial datasets. CPM consists in searching for types of objects that are frequently located together in a spatial neighborhood. Knowledge about such patterns is very important in fields like biology, environmental sciences, epidemiology etc. However, CPM is computationally challenging, mainly due to the large number of pattern instances hidden in spatial data. In this work, we propose a new solution that can utilize the power of multiple GPUs to increase the performance of CPM. The proposed solution is also capable of coping with the GPU memory limits by dividing the work into multiple packages and compressing internal data structures. Experiments performed on large synthetic and real-world datasets prove that we can achieve an order of magnitude speedups in comparison to the efficient multithreaded CPU implementation. Our solution can greatly improve the performance of data analysis, using widely available and energy efficient graphics cards. As a result, CPM in large datasets is more viable for university researchers as well as smaller companies and organizations.