Agent Based Parallelization of Computational Geometry Algorithms
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The Multi-Agent Spatial Simulation (MASS) library is a parallel programming library that utilizes agent-based modeling (ABM) to parallelize big data analysis. In this research, we aim to build on the previous research using MASS and extend the applicability of the library to a computationally complex problem area – computational geometry. We have developed agent based algorithms for four problems in this area – Closest pair of points, Voronoi diagram, Convex hull, and Delaunay triangulation, which is a maiden effort using ABM for such problems. This research presents parallel solutions to these four problems using two other big data analysis platforms – Hadoop MapReduce and Apache Spark. We provide a comprehensive analysis of how MASS based implementations compare to the implementations using the other two frameworks. Programmability and execution time are key criteria used to evaluate the parallel solutions. This paper discusses design approaches and algorithm specifications for four problems in all three parallel platforms and then proceeds to discuss the results. Results showed that MASS library fares well in terms of providing a capability to build intuitive parallel solutions and to perform multiple analyses in-memory on the input data. Furthermore, we discovered potential areas of enhancement for the library, which can situate the MASS library as a better contender for parallelizing data analysis in the future.