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The Convex Hull algorithm is one of the most important algorithms incomputational geometry, with many applications such as in computer graphics,robotics, and data mining. Despite the advances in the new algorithms in thisarea, it is often needed to improve the performance to solve more significantproblems quickly or in real-time processing. This work presents an experimentalevaluation of GPU filters to reduce the cost of computing the 2D convex hull.The technique first performs a preprocessing of the input set, filtering allpoints within an eight-vertex polygon in logarithmic time, to obtain a reducedset of candidate points. We use parallel computation and the use of theManhattan distance as a metric to find the vertices of the polygon and performthe point filtering. For the filtering stage we study different approaches;from custom CUDA kernels to libraries such as Thrust and CUB. Three types ofpoint distributions are tested: a normal distribution (favorable case),circumference (the worst case), and a case where points are shifted randomlyfrom the circumference (intermediate case). Experimental evaluation shows thatthe GPU filtering algorithm can be up to 23x faster than a sequential CPUimplementation, and the whole convex hull computation can be up to 30x fasterthan the fastest implementation provided by the CGAL library.

An Evaluation of GPU Filters for Accelerating the 2D Convex Hull