High-Performance Partition-based and Broadcast- based Spatial Join on GPU-Accelerated Clusters

Abstract—The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on symmetric spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, we have extended our distributed spatial join framework that was originally designed for broadcast-based spatial joins to partition-based spatial joins. Different from broadcast-based spatial joins that require one input side of a spatial join to be a point dataset and the other side to be sufficiently small for broadcast, the new extension supports non-point spatial data on both sides of a spatial join and allows them to be both large in volumes while still benefit from native parallel hardware acceleration for high performance. We empirically evaluate the performance of the proposed partition-based spatial join prototype system on both a workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art. Our experiment results also empirically quantify the tradeoffs between partition-based and broadcast-based spatial joins by using real data