Building the 'digital cornerstone' of the intelligent era

Overview

Computing Power Center Technology: Building the "Digital Cornerstone" of the Intelligent Era

In today's world swept by the digital wave, computing power is no longer just cold computing capability, but a core engine driving social progress, industrial transformation and technological innovation. As the physical carrier of this engine, computing power centers are reshaping our world at an unprecedented speed and scale. For example, in the field of autonomous driving, computing power centers provide strong support for massive data processing and real-time analysis, enabling vehicles to travel safely in complex environments. In the area of AI medical diagnosis, computing power centers can process large volumes of medical imaging data, assist doctors in making rapid and accurate diagnoses, and greatly improve the efficiency and precision of clinical treatment. It is not only a culmination of technologies, but also a symbol of new-quality productive forces, hailed as "the coal and steel of the digital age". By deeply analyzing the technical system of computing power centers, we can clearly see the rise of a highly complex and precisely coordinated "super intelligent factory", backed by numerous technological innovations and far-sighted layouts for the future.

Physical Architecture: The "Super Lego Castle" of the Hardware World

The physical form of a computing power center is a marvel of modern engineering. It is not a simple expansion of traditional computer rooms, but a "super mecha factory" tailored for the AI era, where every detail is meticulously designed to achieve ultimate performance and reliability.

- High-Density Computing Cluster: At its core are thousands of high-performance GPUs (such as A100, H100, L40S, etc.), neatly arranged in high-density cabinets to form a powerful parallel computing array. These GPUs are connected via high-speed interconnection technologies like NVLink and NVSwitch, forming a massive computing power network capable of processing huge amounts of data simultaneously and supporting the training of large models with hundreds of billions of parameters. For instance, in a certain ultra-large-scale computing power center, the number of GPUs in a single cluster can reach tens of thousands, with computing power exceeding the EFLOPS level, comparable to the combined performance of hundreds of thousands of ordinary servers.

- Ultimate Heat Dissipation System: GPUs generate enormous heat when running at full load, making traditional air cooling inadequate. Liquid cooling technology has become the mainstream, including cold plate liquid cooling and immersion liquid cooling. Cold plate liquid cooling uses coolant to directly contact the chip heat sink and carry away heat; immersion liquid cooling submerges the entire server in special coolant, increasing heat dissipation efficiency several times over. In addition, a sophisticated HVAC air conditioning system and hot-cold aisle containment design ensure that cold air is precisely delivered to equipment and hot air is quickly exhausted, reducing the data center's PUE (Power Usage Effectiveness) to below 1.3, or even close to 1.1, thus significantly lowering energy consumption.

- Power Supply and Redundancy Assurance: Computing power centers are "power giants", with a single rack power consumption reaching tens or even hundreds of kilowatts. To ensure stable power supply, they are usually equipped with large-capacity UPS battery banks (capable of supporting hours of uninterrupted power supply) and backup power systems composed of multiple diesel generators. Meanwhile, dual-power supply from mains electricity and UPS, combined with N+1 redundancy design for diesel generators, ensures continuous operation even under extreme conditions (such as natural disasters and power grid failures), avoiding huge losses caused by interrupted training tasks.

- High-Speed Interconnection Network: The internal network of a computing power center is the "expressway" for data. 10 Gigabit Ethernet is already widespread, while InfiniBand (IB) networks are widely used in high-performance computing scenarios, with their low-latency and high-bandwidth characteristics controlling inter-node communication latency at the microsecond level. The RoCEv2 protocol implements RDMA (Remote Direct Memory Access) functionality on top of Ethernet, further reducing costs and improving compatibility. In addition, Fat-Tree topology and Spine-Leaf architecture are adopted to optimize network structure, reduce data forwarding layers, and ensure efficient communication during large-scale parallel computing.

Core of the Technology Stack: The "Vital Organs" of the Computing Power Network

A computing power center is not just a pile of hardware; it relies on a complex and efficient technology stack to achieve resource coordination and scheduling, ensuring that computing resources are maximally utilized.

1. Data Plane: The "Expressway" of Computing Power

  - RDMA over Converged Ethernet (RoCEv2): By eliminating the software overhead of the traditional TCP/IP protocol stack, it achieves zero-copy, low-latency data transmission from memory to memory. In distributed training, operations such as parameter synchronization and gradient aggregation are extremely sensitive to latency. RoCEv2 can reduce communication latency to tens of microseconds, greatly improving model training efficiency.

    - Lossless Network Assurance: PFC (Priority Flow Control) and ECN (Explicit Congestion Notification) technologies are used to build a zero-packet-loss network environment. Packet loss can cause training tasks to roll back, resulting in computing power waste. Lossless network technology dynamically adjusts transmission strategies through real-time traffic monitoring and congestion warning, ensuring data integrity and transmission stability.

    - Time-Sensitive Networking (TSN): Reserves bandwidth and time windows for critical tasks, ensuring low jitter and predictable latency during cross-data center collaboration. For example, in autonomous driving simulation scenarios, millisecond-level latency differences can lead to distorted simulation results. TSN technology is like opening a "dedicated lane" for AI training, supporting applications with extremely high real-time requirements.

    - Intelligent Network Management: AI algorithms are introduced to optimize network traffic scheduling. By real-time monitoring link load and predicting traffic trends, routing strategies are dynamically adjusted, increasing network resource utilization by more than 20%. For instance, a deep learning-based traffic prediction model can identify hotspots in advance and proactively perform load balancing.

2. Control Plane: The Intelligent "Command Brain"

    - Resource Scheduling Platform: Based on orchestration systems such as Kubernetes (K8s) or Slurm, a unified resource management platform is built. The platform can real-time monitor the usage of GPU, CPU, memory, storage and network resources, dynamically allocate computing nodes according to task priorities and resource requirements, and avoid resource fragmentation. For example, low-load GPUs are preferentially allocated for inference tasks, while high-performance clusters are scheduled for training tasks, achieving optimal resource utilization.

    - Distributed Training Framework Adaptation: Supports mainstream frameworks such as TensorFlow, PyTorch and Megatron LM. By optimizing data parallelism, model parallelism and pipeline parallelism strategies, the speed of large model training is increased. For example, in the training of hundreds-of-billions-parameter models, hybrid parallelism technology can reduce training time to one-third of the original.

 

Localization and Independent Controllability: The Cornerstone of Strategic Security

Against the backdrop of growing global supply chain uncertainties, the technological autonomy of computing power centers has become increasingly important. China is accelerating the localization and replacement of core technologies to build a secure and controllable computing power base.

- Independent Innovation Breakthroughs: Domestic AI chips are continuously breaking through in performance and energy efficiency. For example, a certain domestic GPU has reached the level of international mainstream products in FP16 computing power, and through optimized instruction sets and improved memory bandwidth, it performs better in specific scenarios. Domestic high-speed interconnection chips and network protocols have also made progress, with some products entering the commercial application stage.

- Empowerment by the "Eastern Data and Western Computing" Strategy: Through the "Eastern Data and Western Computing" project, the optimal layout of computing resources across the country is promoted. Large-scale computing power centers are built in energy-rich western regions, leveraging low-cost electricity and natural cooling sources to reduce operational costs; edge computing nodes are deployed in eastern regions to meet low-latency requirements. Cross-regional computing power scheduling platforms can realize on-demand resource allocation, such as scheduling non-real-time rendering tasks to the west and keeping real-time inference tasks in the east, improving overall resource utilization.

- Policy and Standard Leadership: The state has issued a series of policies to support the development of computing power infrastructure. For example, the *Three-Year Action Plan for the Development of New-Type Data Centers* proposes that by 2025, the computing power scale will exceed 300 EFLOPS, and the localization ratio will be significantly increased. Meanwhile, industry associations are promoting the establishment of standard systems for computing power scheduling and data security to facilitate the healthy development of the industrial ecosystem.

Application Scenarios: From Laboratories to Industrial Implementation

The value of computing power centers is ultimately reflected at the application level, and their surging computing power is empowering thousands of industries, reshaping production and daily life.

- Scientific Research Innovation: In the field of basic science, computing power centers support molecular dynamics simulations in superhard material research and development, accelerating the discovery of new materials; in geological exploration, they predict mineral distribution through seismic data analysis; climate simulation accuracy is increased to the 100-meter level, helping with extreme weather early warning. For example, a global climate model simulation project participated in by a computing power center has increased the prediction time resolution from hourly to minute-level.

- Industrial Empowerment: In the field of intelligent connected vehicles, computing power centers provide massive simulation scenario training for autonomous driving algorithms, shortening the development cycle; the biopharmaceutical industry uses AI for protein structure prediction and drug molecule screening, improving new drug R&D efficiency several times over; in the aerospace field, CFD simulations are used to optimize aircraft design, reducing wind tunnel test costs. A pharmaceutical company has shortened the candidate drug screening time from years to months with the help of a computing power center.

- Urban Governance: AI models play a significant role in smart cities. Traffic congestion management optimizes traffic light timing through real-time road condition data analysis, reducing congestion time by 30%; energy dispatching systems integrate multi-source data such as power grids and meteorology to achieve accurate load forecasting and renewable energy consumption; in the field of public security, video analysis technology is used to improve the response speed to abnormal events.

- Cultural Technology: In 5G digital film and television bases, computing power centers support AIGC applications such as text-to-image and image-to-video generation, compressing the rendering time of an animated film from years to months. New business formats such as metaverse scene construction and digital human production also rely on powerful computing power to achieve real-time rendering and interaction. A cultural tourism project has increased tourist experience satisfaction by 40% through AI-generated personalized tour guide content.

- Exploration of Emerging Fields: The integration of quantum computing and classical computing power has become a new direction; computing power centers provide support for quantum algorithm simulation; brain-computer interface research relies on high-performance computing to analyze neural signals; blockchain computing power networks ensure transaction security and efficiency. These cutting-edge explorations are opening up new industrial blue oceans.

Future Outlook: Towards a Green, Intelligent and Ubiquitous Computing Power Ecosystem

In the future, computing power centers will present three major trends, driving them to become the "super brain" of the digital economy.

1. Green and Low-Carbon Development: Driven by the dual carbon goals, computing power centers are accelerating their green transformation. Liquid cooling technology will be fully popularized, with some data centers achieving a PUE below 1.15; natural cooling technologies (such as indirect evaporative cooling and lake water cooling) will be widely used in suitable areas; the proportion of renewable energy such as photovoltaic and wind power will continue to increase, with some computing power centers achieving 100% green power supply. For example, a western computing power center has installed photovoltaic panels on its roof, with annual power generation meeting more than 10% of its electricity demand.

2. Service-Oriented and Platform-Based Development: Computing power will gradually evolve into a public service, and the "Computing as a Service (CaaS)" model will emerge. Through the "public computing power service + industrial incubation platform", small and medium-sized enterprises can obtain computing resources on demand, lowering the threshold for AI development. Computing power trading platforms will realize cross-regional resource scheduling, and idle computing power can participate in the sharing economy. For example, a city-level computing power platform has connected more than 100 scientific research institutions and enterprises, increasing computing power utilization by 50%.

3. Networked Collaboration: Computing power centers will no longer be isolated islands, but will realize cross-regional and cross-platform resource scheduling through computing power networks. National hub nodes will be connected via 400G/800G ultra-high-speed optical networks, forming a "national unified computing power cloud". Edge computing nodes will collaborate with central clouds to build a "cloud-edge-end" integrated architecture, meeting the computing power needs of different scenarios. For example, in industrial Internet of Things scenarios, edge computing processes device data in real time, while central clouds perform in-depth analysis and model iteration.

4. AI-Driven Intelligent Operation: AI technology will penetrate the entire operation and maintenance process of computing power centers. Machine learning will be used to predict equipment failures and conduct preventive maintenance, reducing downtime rates; intelligent energy management systems will dynamically adjust cooling and power supply strategies to further save energy; digital twin technology will build virtual data centers to simulate and optimize operation schemes. After introducing AI operation and maintenance, a large computing power center has shortened fault response time by 70% and reduced operation and maintenance costs by 20%.

5. Security Technology Upgrade: As computing power centers become critical information infrastructure, their security systems will be continuously strengthened. Technologies such as quantum secure communication, trusted computing and federated learning will ensure data privacy and computing security. For example, in the medical field, distributed training technology based on federated learning can realize "data available but not visible", resolving the contradiction between privacy and collaboration.

The Majestic Power in Silence

Computing power centers may not receive noisy applause, but they are nurturing the most magnificent changes in silence. They do not stand in the spotlight, yet they are the source of all intelligent radiance. From the heat of a single GPU to the digital transformation of an entire city; from quantum simulations in laboratories to intelligent production lines in workshops; from precise computing in chip design to real-time rendering in the metaverse—computing power centers are leveraging technological power to prop up a more intelligent, efficient and sustainable future. They are the quietest yet most passionate existence of this era, the "digital cornerstone" of the digital economy era, and a solid ladder for human civilization to leap into the digital world. With the continuous evolution of technology, computing power centers will surely become an eternal engine driving the progress of human civilization in a greener, more intelligent and inclusive form.