信息通信技术与政策

信息通信技术与政策

信息通信技术与政策 ›› 2025, Vol. 51 ›› Issue (10): 23-30.doi: 10.12267/j.issn.2096-5931.2025.10.004

专题:先进计算创新应用与生态建设 上一篇    下一篇

智能协同驱动的全国一体化算力网:架构演进、实施瓶颈与优化路径研究*

The national computing power network driven by intelligent collaboration: research on architectural evolution, implementation bottlenecks, and optimization pathways

李若尘, 文新博, 杜婧茹, 李世钰, 谢光芬, 董一川   

  1. 鹏城实验室战略与交叉前沿研究部, 深圳 518052
  • 收稿日期:2025-09-15 出版日期:2025-10-25 发布日期:2025-11-06
  • 作者简介:
    李若尘, 鹏城实验室博士后,主要研究方向为人工智能、算力网调度体系、国内外算力网技术动向、宏观政策等
    文新博, 鹏城实验室助理研究员,主要研究方向为算力网技术动向与宏观政策等
    杜婧茹, 鹏城实验室所长助理,主要研究方向为科技体制机制改革、技术动向等
    李世钰, 鹏城实验室博士后,主要研究方向为科技情报、知识图谱等
    谢光芬, 鹏城实验室博士后,主要研究方向信息论、移动通信技术等
    董一川, 鹏城实验室助理研究员,主要研究方向为算力网调度体系、超算、智算等
  • 基金资助:
    *中国工程院科技战略咨询项目(2025-XBZD-15)

LI Ruochen, WEN Xinbo, DU Jingru, LI Shiyu, XIE Guangfen, DONG Yichuan   

  1. Department of Strategic and Advanced Interdisciplinary Research, PCL, Shenzhen 518052, China
  • Received:2025-09-15 Online:2025-10-25 Published:2025-11-06

摘要:

立足国家“东数西算”工程,系统研究人工智能技术驱动全国一体化算力网络的关键协同机制。基于国家“东数西算”工程实践,从调度效率、资源整合等维度切入,剖析当前算力网络的发展态势以及核心瓶颈。同时,通过对鹏城实验室中国算力网等案例的深度剖析,提出算力网智能协同调度的优化路径,为构建高效、安全、自主协同的一体化算力基础设施建设提供理论支撑。

关键词: 算力网, 调度效率, 基础设施, 东数西算, 优化路径

Abstract:

This paper systematically investigates the core mechanisms by which artificial intelligence technology empowers a national integrated computing network, set against the backdrop of China’s “Eastern Data, Western Computing” strategic initiative. Drawing upon practical experiences from this national project, the study analyzes the current development status and critical bottlenecks of computing networks, focusing on dimensions such as scheduling efficiency and resource integration. Concurrently, through an in-depth examination of cases, including the “China Computing Network” at Pengcheng Laboratory, this paper proposes optimized pathways for intelligent collaborative scheduling in computing networks, providing theoretical foundations for the construction of an efficient, secure, and intelligent integrated computing network.

Key words: computing network, scheduling efficiency, AI-infra, eastern data western computing, optimization pathways

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