The rising complexity and scale of agent-based models (ABMs) necessitate efficient computational strategies to manage the increasing demand for processing power and memory. This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures. It covers best practices in memory management, Java options, BehaviorSpace execution, and AWS instance selection. By implementing these optimizations and selecting appropriate AWS instances, we achieved a 32\% reduction in computational costs and improved performance consistency. Through a comparative analysis of NetLogo simulations on different AWS instances using the wolf-sheep predation model, we demonstrate the performance gains achievable through these optimizations.

翻译：随着基于主体模型（ABMs）的复杂性和规模日益增长，需要高效的计算策略来应对对处理能力和内存日益增长的需求。本文为在亚马逊网络服务（AWS）及其他云基础设施上运行大规模模型，提供了优化NetLogo（一种广泛使用的ABMs平台）的全面指南。内容涵盖内存管理、Java选项、BehaviorSpace执行以及AWS实例选择的最佳实践。通过实施这些优化并选择合适的AWS实例，我们实现了计算成本降低32%并提升了性能一致性。通过使用狼-羊捕食模型对不同AWS实例上的NetLogo模拟进行对比分析，我们展示了通过这些优化可实现的性能提升。