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.

翻译：基于智能体的建模为表示和模拟复杂系统提供了一种通用方法。由于模拟运行所需的计算时间（扩展性至少与系统规模（即智能体数量）呈线性关系），研究大规模系统颇具挑战性。鉴于多智能体系统的内在模块化特性，并行计算是克服这一挑战的自然途径。然而，由于智能体之间存在信息共享与通信，并行化并非易事。我们提出了一种面向共享内存的多智能体系统并行执行协议。该方法适用于可基于顺序计算形式化，且涉及局部更新（即仅涉及少量智能体）的模型。该协议具有自底向上和异步特性，能够以自适应且优雅的方式处理异构计算。我们通过文化动力学与疾病传播的多智能体系统实例，阐明了该方法的潜在性能提升效果。