arXiv cs.MA· Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Ya\u{g}l{\i}k\c{c}{\i}, Mohammad Sadrosadati, Fons Rademakers, Onur Mutlu·· 2 天前AI 评分25
TeraAgent：可模拟 5000 亿智能体的分布式智能体仿真引擎
TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents
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TeraAgent 是一款分布式智能体仿真引擎，可在 84,096 个 CPU 核心上模拟 5000 亿个智能体，规模达当前最先进平台 BioDynaMo 的 292 倍。它通过定制序列化机制与 delta 编码降低跨服务器数据传输，序列化中位加速 110x、反序列化 37x，单核智能体更新速率是 Biocellion 的 8 倍，原位可视化性能提升 39x。
正文 · 原文
Abstract:Agent-based simulation is a method for studying highly complex systems in disciplines as diverse as biology, medicine, sociology, economics, finance, and more. In this method, an agent represents a small part of the system, such as a human cell, and, through interactions among many agents, the system's behavior emerges, like the growth of cancer in the human body. Real-world complex systems, such as a human organ, comprise billions of agents. However, the state-of-the-art agent-based simulation platform, BioDynaMo, does not support the distributed processing of a simulation across servers. This limitation restricts the scalability of agent counts to the computational resources available within a single server, hindering users from conducting larger and more meaningful simulations. In this paper, we introduce TeraAgent, a new distributed agent-based simulation engine. To overcome BioDynaMo's scalability limitation, TeraAgent enables efficient exchange of agent information across servers by implementing two solutions: 1) a tailored serialization mechanism that allows agents to be accessed and mutated directly at the receive buffer, and 2) delta encoding that significantly reduces data transfer across servers by leveraging the iterative nature of agent-based simulations. Built on our solutions, TeraAgent 1) enables extreme-scale simulations with half a trillion agents (292x that of BioDynaMo) on 84,096 CPU cores, 2) reduces time-to-result leveraging parallelism across compute nodes, 3) improves interoperability with third-party tools, and 4) provides users with more hardware flexibility. TeraAgent serializes agents with a median speedup of 110x (37x for deserialization) over the state-of-the-art ROOT I/O baseline, delivers 8x the agent update rate per CPU core of Biocellion, and improves in-situ visualization performance by 39x.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA); Performance (cs.PF); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2509.24063 [cs.DC] |
| (or arXiv:2509.24063v2 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2509.24063 arXiv-issued DOI via DataCite |
Submission history
From: Lukas Johannes Breitwieser [view email]
[v1]
Sun, 28 Sep 2025 20:35:29 UTC (4,208 KB)
[v2]
Thu, 15 Oct 2026 12:50:52 UTC (2,440 KB)
来源：arXiv cs.MA · arxiv.org