A Parallel Rectangle Intersection Algorithm on GPU+CPU

Abstract

In this paper, we investigate efficient algorithms and implementations using GPU plus CPU to solve the rectangle intersection problem on a plane. The problem is to report all intersecting pairs of iso-oriented rectangles, whose parallelization on GPUs poses two major computational challenges: data partition and the massive output. The algorithm we presented is called PRI-GC, Parallel Rectangle Intersection algorithm on GPU+CPU, which consists of two phases: mapping and intersection-checking. In the mapping phase, rectangles are hashed into different subspaces (called cells) to reduce the unnecessary intersection checking for far-apart rectangles. In the intersection-checking phase, pairs of rectangles within the same cell are examined in parallel, and the intersecting pairs of rectangles are reported. Several optimization techniques, including rectangles re-ordering, output data compressing/encoding, and the execution overlapping of GPU and CPU, are applied to enhance the performance. We had evaluated the performance of PRI-GC and the result shows over 30x speedup against two well-implemented sequential algorithms on single CPU. The effectiveness of each optimization technique for this problem was evaluated as well. Several parameters, including different degrees of rectangle coverage, different block sizes, and different cell sizes, were also experimented to explore their influences on the performance of PRI-GC.