AGENT-BASED COMPUTATIONAL GEOMETRY

Abstract

The Multi-Agent Spatial Simulation (MASS) library is a parallel programming library that uses agent-based modeling (ABM) parallelization approach over a distributed cluster. The MASS library contains several applications solving computational geometry problems using ABM algorithms. This research aims to build additional four ABM algorithm-based applications: (1) range search, (2) point location, (3) largest empty circle, and (4) Euclidean shortest path. This research presents ABM solutions implemented with MASS library as well as divide and conquer (D&C) solutions to four problems implemented with big data parallelization platforms MapReduce and Spark. In this paper, we discuss design approaches used in solutions for the four problems. We present ABM and D&C algorithms with MASS, MapReduce, and Spark platforms. We provide a detailed analysis of programmability and execution performance metrics of ABM algorithm-based implementations with MASS against D&C algorithm-based versions with MapReduce and Spark. Results showed that the MASS library provides an intuitive approach to developing parallel solutions to computational geometry problems. We observed that ABM MASS solutions produce competitive performance results when performing computations in-memory over distributed structured datasets.