Parallel Construction of Local Clearance Triangulations

University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap; Blekinge Tekniska Högskola/Institutionen för datavetenskap

Author: Simon Gummesson; Mikael Johnson; [2019]

Keywords: LCT; GPGPU; GPU; Navmesh;

Abstract: The usage of navigation meshes for path planning in games and otherdomains is a common approach. One type of navigation mesh that recently has beendeveloped is the Local Clearance Triangulation (LCT). The overall aim of the LCT isto construct a triangulation in such a way that a property called theLocal Clearancecan be used to calculate a path in a more efficient and cheap way. At the time ofwriting the thesis there only exists one solution that creates an LCT, this solution isonly using the CPU. Since the process of creating an LCT involves the insertion ofmany points and edge flips which only affects a local area it would be interesting toinvestigate the potential performance gain of using the GPU. The objective of the thesis is to develop a GPU version based on thecurrent CPU LCT solution and to investigate in which cases the proposed GPU al-gorithm performs better. A GPU version and a CPU version of the proposed algorithm has beendeveloped to measure the performance gain of using the GPU, there are no algorith-mic differences between these versions. To measure the performance of the algorithmtwo tests have been constructed, the first test is called the Object Insertion test andmeasures the time it takes to build an LCT using generated test maps. The sec-ond test is called the Internal test and measures the internal performance of thealgorithm. A comparison between the GPU algorithm with an LCT library called Triplanner was also done. The proposed algorithm performed better on larger maps when imple-mented on a GPU compared to a CPU implementation of the algorithm. The GPU performance compared to the Triplanner was faster in some of the larger maps. An algorithm that builds an LCT from scratch is presented. Theresults show that using the proposed algorithm on the GPU substantially increasesthe performance of the algorithm compared to when implementing it on a CPU.

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