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Title An Evaluation of GPU filters for Accelerating the 2D Convex Hull
Authors Roberto Carrasco, Héctor Ferrada, Cristobal Navarro, Nancy Hitschfeld
Publication date February 2024
Abstract The Convex Hull is one of the most relevant structures in
computational geometry, with many applications such as in computer graphics,
robotics, and data mining. Despite the advances in the new algorithms in
this area, it is often needed to improve the performance to solve more
significant problems quickly or in real-time processing. This work presents
an experimental evaluation of GPU filters to reduce the cost of computing
the 2D convex hull. The techniques first perform a preprocessing of the
input set, filtering all points within an eight-vertex polygon to obtain a
reduced set of candidate points. We use parallel computation and the use of
the Manhattan distance as a metric to find the vertices of the polygon and
perform the point filtering. For the filtering stage we study different
approaches; from custom CUDA kernels to libraries such as Thrust and Cub.
Four types of point distributions are tested: a normal distribution
(favorable case), uniform (favorable case), circumference (the worst case),
and a case where points are shifted randomly from the circumference
(intermediate case). The experimental evaluation shows that the GPU
filtering algorithm can be up to 17.5× faster than a sequential CPU
implementation, and the whole convex hull computation can be up to 160×
faster than the fastest implementation provided by the CGAL library for a
uniform distribution and 23× for a normal distribution.
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Pages article 104793
Volume 184
Journal name Journal of Parallel and Distributed Computing
Publisher Elsevier Science (Amsterdam, The Netherlands)
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