  We present a novel GPU-accelerated implementation of the QuickHull algorihtm
for calculating convex hulls of planar point sets. We also describe a practical
solution to demonstrate how to efficiently implement a typical
Divide-and-Conquer algorithm on the GPU. We highly utilize the parallel
primitives provided by the library Thrust such as the parallel segmented scan
for better efficiency and simplicity. To evaluate the performance of our
implementation, we carry out four groups of experimental tests using two groups
of point sets in two modes on the GPU K20c. Experimental results indicate that:
our implementation can achieve the speedups of up to 10.98x over the
state-of-art CPU-based convex hull implementation Qhull [16]. In addition, our
implementation can find the convex hull of 20M points in about 0.2 seconds.
