Parallelization Strategies for Spatial Agent-Based Models

Authored by Nuno Fachada, Vitor V Lopes, Rui C Martins, Agostinho C Rosa

Date Published: 2017

DOI: 10.1007/s10766-015-0399-9

Sponsors: Portuguese Foundation for Science and Technology (FCT)

Platforms: Java

Model Documentation: UML ODD Pseudocode Mathematical description

Model Code URLs: https://github.com/fakenmc/pphpc/tree/java/java

Abstract

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: (1) compare the performance of this implementation with an existing NetLogo implementation; and, (2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: (1) model parallelization can yield considerable performance gains; (2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, (3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.

Tags

Simulation Agent-based modeling environment Graphics processing units GPU Platforms Framework Shared memory Parallelization strategies Multithreading Implementations