Parallel Grid-Based Colocation Mining Algorithms on GPUs for Big Spatial Event Data

Abstract

Colocation patterns refer to subsets of spatial features whose instances are frequently located together. Mining colocation patterns is important in many applications such as identifying relationships between diseases and environmental factors, but is computationally challenging due to the large number of instances and candidate patterns. Existing algorithms are mostly sequential, and thus can be insufficient for big spatial event data. Recently, parallel colocation mining algorithms have been developed based on the Map-reduce framework, which is economically expensive. Another work proposed a GPU algorithm based on iCPI tree, but assumes that the number of neighbors for each instance is within a small constant, and thus cannot be used when instances are dense and unevenly distributed. To address these limitations, we recently proposed grid-based GPU colocation mining algorithms that include a novel cell-aggregate-based upper bound filter, and two refinement algorithms. In this paper, we provide theoretical analysis of running time. Furthermore using GPU profiling, we identify our recent GPU implementation, GPU-grid-join, as a memory bound problem and to address its bottlenecks, we proposes GPU-grid-join+, an optimized GPU algorithm. Our experimental results on real world data shows that GPU-grid-join+ achieves 4 to 12-fold speedup over GPU-grid-join both running on Nvidia P100 GPU as well as 56 to 126-fold speedup over OpenMP implementation over Intel(R) Xeon(R) CPU with 12 cores. Also for synthetic data, the speedup is in ranges 3 to 7-fold and 9 to 42-fold respectively.