Parallelization Strategies for Spatial Agent-Based Models

TL;DR

This paper explores various parallelization strategies for spatial agent-based models, demonstrating that model parallelization can significantly improve performance while highlighting trade-offs in reproducibility and accuracy.

Contribution

It introduces a multithreaded Java implementation of the PPHPC ABM and compares different parallelization strategies, providing insights into their performance and reproducibility impacts.

Findings

Model parallelization yields significant performance improvements.

Different strategies have specific trade-offs in performance and reproducibility.

PPHPC serves as a benchmark for comparing ABM implementations.

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.