CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

TL;DR

This paper introduces a GPU-accelerated convex hull algorithm with a novel preprocessing approach that significantly speeds up computation for large point sets, combining GPU and CPU stages for efficiency.

Contribution

The paper proposes a new GPU-based convex hull algorithm with a Sorting-based Preprocessing Approach (SPA), improving speed and simplicity over existing methods.

Findings

Achieves 5x to 6x speedup over Qhull for 20 million points

Uses GPU preprocessing to discard interior points efficiently

Combines GPU and CPU stages for practical performance

Abstract

This paper presents a practical GPU-accelerated convex hull algorithm and a novel Sorting-based Preprocessing Approach (SPA) for planar point sets. The proposed algorithm consists of two stages: (1) two rounds of preprocessing performed on the GPU and (2) the finalization of calculating the expected convex hull on the CPU. We first discard the interior points that locate inside a quadrilateral formed by four extreme points, and then distribute the remaining points into several (typically four) sub regions. For each subset of points, we first sort them in parallel, then perform the second round of discarding using SPA, and finally form a simple chain for the current remaining points. A simple polygon can be easily generated by directly connecting all the chains in sub regions. We at last obtain the expected convex hull of the input points by calculating the convex hull of the simple polygon. We use the library Thrust to realize the parallel sorting, reduction, and partitioning for better efficiency and simplicity. Experimental results show that our algorithm achieves 5x ~ 6x speedups over the Qhull implementation for 20M points. Thus, this algorithm is competitive in practical applications for its simplicity and satisfied efficiency.