The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such as cooling and IT energy usage. A significant challenge in this pursuit is the lack of a configurable and scalable thermal data center model that offers an end-to-end pipeline. Data centers consist of multiple IT components whose geometric configuration and heat dissipation make thermal modeling difficult. This paper presents PyDCM, a customizable Data Center Model implemented in Python, that allows users to create unique configurations of IT equipment with custom server specifications and geometric arrangements of IT cabinets. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations and scales sublinearly with the number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via the Gymnasium wrapper to optimize data center cooling and offers a user-friendly platform for testing various data center design prototypes.

翻译：随着全球对可持续性和减少碳排放的关注日益增强，各国政府和企业正重新审视数据中心设计与运营的方式。鉴于数据中心能耗极高且计算负载呈指数级增长，其成为优化功耗的关键领域，尤其在冷却与IT能源使用方面。当前面临的一大挑战是缺乏可配置、可扩展且提供端到端流程的热数据中心模型。数据中心包含多个IT组件，其几何布局与散热特性使热建模变得复杂。本文提出PyDCM——基于Python实现的可定制数据中心模型，允许用户通过自定义服务器规格与IT机柜几何排布，创建独特的IT设备配置。通过采用向量化热计算方法，PyDCM的计算速度较当前Energy Plus建模实现提升数十倍（30倍），且随着CPU数量增加呈次线性扩展。此外，PyDCM通过Gymnasium接口支持深度强化学习应用，以优化数据中心冷却，并为测试各类数据中心设计原型提供了用户友好型平台。