GPU-based fast clustering via K-Centres and k-NN mode seeking for geospatial industry applications

The emerging trends in data industry, particularly those related to the repeated processing of data streams, are pushing the limits of computer systems and processes. Among them, the near real-time clustering of geospatial location data is paradigmatic due to its scale, requirements and potential industrial applications. As a solution to deal with the large and continuous arrival of geospatial data, modern many-core (GPU-based) computer architectures are used for implementing fast and efficient clustering algorithms. This paper proposes GPU implementations of two different clustering algorithms — K-Centres and k-NN mode seeking — and compare them against their corresponding sequential implementations. Publicly available geospatial datasets have been used to exemplify the achieved performances using GPUs. Our main contribution is providing GPU implementations of the clustering algorithms that are feasible for near real-time problems. Results show speed-ups of up to 19 and 135 times, with the largest dataset, for K-Centres and k-NN mode seeking respectively. Important technical details of the sorting algorithms, required by the GPU implementation of k-NN mode seeking, are also highlighted.