Adaptive parallelization of multi-agent simulations with localized dynamics

TL;DR

This paper introduces an adaptive parallelization protocol for multi-agent agent-based models that leverages localized dynamics to improve computational efficiency in shared-memory systems.

Contribution

It presents a novel asynchronous protocol for parallel execution of agent-based models with localized updates, enhancing scalability and performance.

Findings

Significant performance improvements demonstrated on cultural dynamics models

Effective handling of heterogeneous computations in parallel simulations

Protocol enables scalable simulation of large multi-agent systems

Abstract

Agent-based modelling constitutes a versatile approach to representing and simulating complex systems. Studying large-scale systems is challenging because of the computational time required for the simulation runs: scaling is at least linear in system size (number of agents). Given the inherently modular nature of MABSs, parallel computing is a natural approach to overcoming this challenge. However, because of the shared information and communication between agents, parellelization is not simple. We present a protocol for shared-memory, parallel execution of MABSs. This approach is useful for models that can be formulated in terms of sequential computations, and that involve updates that are localized, in the sense of involving small numbers of agents. The protocol has a bottom-up and asynchronous nature, allowing it to deal with heterogeneous computation in an adaptive, yet graceful manner. We illustrate the potential performance gains on exemplar cultural dynamics and disease spreading MABSs.