Agent-based models (ABMs) offer a powerful framework for understanding complex systems. However, their computational demands often become a significant barrier as the number of agents and complexity of the simulation increase. Traditional ABM platforms often struggle to fully exploit modern computing resources, hindering the development of large-scale simulations. This paper presents Vahana.jl, a high performance computing open source framework that aims to address these limitations. Building on the formalism of synchronous graph dynamical systems, Vahana.jl is especially well suited for models with a focus on (social) networks. The framework seamlessly supports distribution across multiple compute nodes, enabling simulations that would otherwise be beyond the capabilities of a single machine. Implemented in Julia, Vahana.jl leverages the interactive Read-Eval-Print Loop (REPL) environment, facilitating rapid model development and experimentation.

翻译：基于智能体的模型（ABMs）为理解复杂系统提供了强大的框架。然而，随着智能体数量与模拟复杂度的增加，其计算需求往往成为显著障碍。传统的ABM平台通常难以充分利用现代计算资源，阻碍了大规模模拟的发展。本文提出Vahana.jl，一个旨在解决这些局限性的高性能计算开源框架。基于同步图动力系统的形式化体系，Vahana.jl特别适用于关注（社交）网络的模型。该框架无缝支持跨多个计算节点的分布式部署，使得原本超出单机能力的模拟成为可能。Vahana.jl采用Julia语言实现，充分利用交互式读取-求值-输出循环（REPL）环境，促进了模型的快速开发与实验探索。