This report characterizes the data center industry and its importance for AI
development. Data centers are industrial facilities that efficiently provide
compute at scale and thus constitute the engine rooms of today's digital
economy. As large-scale AI training and inference become increasingly
computationally expensive, they are dominantly executed from this designated
infrastructure. Key features of data centers include large-scale compute
clusters that require extensive cooling and consume large amounts of power, the
need for fast connectivity both within the data center and to the internet, and
an emphasis on security and reliability. The global industry is valued at
approximately $250B and is expected to double over the next seven years. There
are likely about 500 large (above 10 MW) data centers globally, with the US,
Europe, and China constituting the most important markets. The report further
covers important actors, business models, main inputs, and typical locations of
data centers.

这篇报告描述了数据中心行业以及其对人工智能开发的重要性，重点介绍了数据中心的关键特征、全球行业规模和重要市场，并涵盖了重要参与者、商业模式、主要输入和典型位置。

规模计算 —— 对数据中心行业的广泛研究

Compute at Scale -- A Broad Investigation into the Data Center Industry

Data Centers are huge power consumers, both because of the energy required
for computation and the cooling needed to keep servers below thermal redlining.
The most common technique to minimize cooling costs is increasing data room
temperature. However, to avoid reliability issues, and to enhance energy
efficiency, there is a need to predict the temperature attained by servers
under variable cooling setups. Due to the complex thermal dynamics of data
rooms, accurate runtime data center temperature prediction has remained as an
important challenge. By using Gramatical Evolution techniques, this paper
presents a methodology for the generation of temperature models for data
centers and the runtime prediction of CPU and inlet temperature under variable
cooling setups. As opposed to time costly Computational Fluid Dynamics
techniques, our models do not need specific knowledge about the problem, can be
used in arbitrary data centers, re-trained if conditions change and have
negligible overhead during runtime prediction. Our models have been trained and
tested by using traces from real Data Center scenarios. Our results show how we
can fully predict the temperature of the servers in a data rooms, with
prediction errors below 2 C and 0.5 C in CPU and server inlet temperature
respectively.

本研究利用语法进化技术，生成数据中心温度模型并预测 CPU 和进气口温度，在减少冷却成本、增强能量效率方面具有重要意义。通过实际数据中心场景的跟踪，结果表明可以完全预测数据室中服务器的温度，并且预测误差分别低于 2℃ 和 0.5℃ 的 CPU 和服务器进气口温度。

运行时数据中心温度预测技术的语法演化应用

Runtime data center temperature prediction using Grammatical Evolution techniques

A method is proposed to cool down atoms in a harmonic trap without
phase-space compression as in a perfectly slow adiabatic expansion, i.e.,
keeping the populations of the instantaneous initial and final levels
invariant, but in a much shorter time. This may require that the harmonic trap
becomes an expulsive parabolic potential in some time interval. The cooling
times achieved are also shorter than previous minimal times using
optimal-control bang-bang methods and real frequencies.

介绍了一种在谐振陷阱内冷却原子的新方法，可在保持瞬时初末能级的群体不变的前提下，通过在短时间内使谐振陷阱成为排斥抛物线势来实现，冷却时间比以往使用的最优控制方法和实际频率的最小时间还要短。