  As an important application of spatial databases in pathology imaging
analysis, cross-comparing the spatial boundaries of a huge amount of segmented
micro-anatomic objects demands extremely data- and compute-intensive
operations, requiring high throughput at an affordable cost. However, the
performance of spatial database systems has not been satisfactory since their
implementations of spatial operations cannot fully utilize the power of modern
parallel hardware. In this paper, we provide a customized software solution
that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison
in a cost-effective way. Our solution consists of an efficient GPU algorithm
and a pipelined system framework with task migration support. Extensive
experiments with real-world data sets demonstrate the effectiveness of our
solution, which improves the performance of spatial cross-comparison by over 18
times compared with a parallelized spatial database approach.
