智算中心存储系统优化研究

曹原铭; 雷鸣; 刘芹; 牛瑛霞; 武振宇; 潘洁

doi:10.11959/j.issn.1000-0801.2025160

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智算中心存储系统优化研究

专栏 | 更新时间：2025-08-07

- 智算中心存储系统优化研究
- Research on optimization of storage system in intelligent computing center
- 电信科学 2025年41卷第7期页码：164-175
- 作者机构：
  
  中国移动通信集团设计院有限公司，北京 100080
- 作者简介：
  
  [ "曹原铭（1983- ），男，中国移动通信集团设计院有限公司高级工程师，主要研究方向为云计算、人工智能算力基础设施等。" ]
  [ "雷鸣（1973- ），女，中国移动通信集团设计院有限公司高级工程师，主要研究方向为云计算、人工智能算力基础设施等。" ]
  [ "刘芹（1977- ），女，中国移动通信集团设计院有限公司正高级工程师，主要从事与云计算、人工智能等算力资源相关的咨询及研究工作。" ]
  [ "牛瑛霞（1971- ），女，现就职于中国移动通信集团设计院有限公司，主要研究方向为算力网络、人工智能、数据业务平台等。" ]
  [ "武振宇（1975- ），男，中国移动通信集团设计院有限公司高级工程师，主要研究方向为云计算、算力网络、智算中心等。" ]
  [ "潘洁（1978- ），女，中国移动通信集团设计院有限公司高级工程师，主要研究方向为算力网络安全和网络信息安全。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025160
  中图分类号： TP393
- 收稿日期：2025-03-21，
  
  修回日期：2025-06-27，
  
  录用日期：2025-06-11，
  
  纸质出版日期：2025-07-20
- 稿件说明：
移动端阅览
曹原铭,雷鸣,刘芹等.智算中心存储系统优化研究[J].电信科学,2025,41(07):164-175.

CAO Yuanming,LEI Ming,LIU Qin,et al.Research on optimization of storage system in intelligent computing center[J].Telecommunications Science,2025,41(07):164-175.
曹原铭,雷鸣,刘芹等.智算中心存储系统优化研究[J].电信科学,2025,41(07):164-175. DOI： 10.11959/j.issn.1000-0801.2025160.

CAO Yuanming,LEI Ming,LIU Qin,et al.Research on optimization of storage system in intelligent computing center[J].Telecommunications Science,2025,41(07):164-175. DOI： 10.11959/j.issn.1000-0801.2025160.

摘要

智算中心使用分布式文件存储进行数据预处理和模型训练，使用分布式对象存储进行原始数据获取和模型发布，使用分布式块存储为资源管理平台提供存储服务。同时，模型训练过程中经常使用高性能分布式文件存储缩短checkpoint读写时间，提高集群训练效率。大模型训练全生命周期需要在不同存储协议、不同读写性能的存储系统之间进行数据复制、迁移，导致数据重复存储，且数据复制需要占用计算资源和网络带宽。为了解决上述问题，并为智算集群提供统一命名空间，提出文件、对象融合存储和文件系统分级存储方案，解决不同存储协议间的数据搬运问题，实现高性能文件存储（全闪）和普通性能文件存储（混闪）间的数据自动流动，为超大规模智算集群的存储系统优化方案提供参考。

Abstract

The intelligent computing center uses distributed file storage for data preprocessing and model training

distributed object storage for the acquisition of raw data and model release

and distributed block storage to provide storage for the resource management platform. Meanwhile

high-performance distributed file storage is used to shorten the read and write time of checkpoint during the training process and improve the training efficiency of the cluster. The entire life cycle of large model training requires data copying and migration between storage systems with different storage protocols and different read-write performances

resulting in duplicate data storage. Additionally

data copying requires computing resources and network bandwidth. To address the above issues and provide a unified namespace for the intelligent computing clusters

a file and object converged storage and a hierarchical file storage scheme were proposed to solve the problem of data transfer between different storage protocols and enable automatic data flow between high-performance file storage (all SSD) and ordinary-performance file storage (SSD and HDD)

providing a reference for the optimization of storage systems of ultra-large-scale intelligent computing clusters.

关键词

Keywords

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