Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor Cerebras Wafer Scale Engine (WSE), hold particular promise. However, practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms' large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms.

翻译：计算硬件的持续改进有望提升对跨尺度现象进行硅基建模的能力，这些现象涉及进化生物学和人工生命领域中若干重大未解问题，例如个体性转变、生态-进化动力学以及稀有进化事件。新兴的面向机器学习/人工智能的硬件加速器，如包含85万个处理器的Cerebras晶圆级引擎，展现出特别的应用前景。然而，如何在这些平台的海量处理器上开展信息丰富的进化实验仍存在实际挑战。本文聚焦于从WSE平台上的智能体进化过程中提取系统发育信息这一核心问题。为实现该目标，我们对去中心化的硅基系统发育追踪方法进行了重大改进，相关成果已在本文中报道。这些改进带来了数量级的性能提升。同时，我们提出了一种适用于WSE硬件的异步岛屿遗传算法框架。通过采用具备追踪功能的简易智能体模型进行仿真及硬件实测，该遗传算法在种群规模达1600万智能体时，每分钟可运行超过100万代进化。我们验证了从这些实验中重建的系统发育树，并证明其适用于推断潜在的进化条件。特别地，我们展示了从晶圆级模拟中提取的清晰系统发育计量信号，该信号能有效区分启用与禁用适应动力学机制的实验组。综合来看，这些基准测试与验证实验表明，基于智能体的进化模拟在保持高效性与可观测性的同时，具备高度可扩展的强劲潜力。所开发的技术将使一系列先前难以处理的全新研究问题成为可能，推动进化生物学与人工生命领域在多种新兴高性能计算平台上的进一步探索。