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面向大模型的智算网络发展研究

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近年来,全球进入智能计算的蓬勃发展期,作为具有巨量参数和复杂结构的深度学习模型,大模型训练需要在多卡、多服务器间实现训练参数的快速同步,所以对算力中心网络的带宽、时延、可靠性、可扩展性和安全性等提出更高要求.研究了面向大模型训练的智算网络的需求和相关关键技术,对智算网络的研究成果、标准规范和案例实践进行了分析,以期进一步促进智算网络的发展.
Research on the development of intelligent computing network for large models
In recent years,the world has entered a period of vigorous development in intelligent computing.As deep learning models with huge parameters and complex structures,large model training requires fast synchronization of training parameters between multiple cards and servers,which imposes higher requirements on the bandwidth,la-tency,reliability,scalability and security of datacenter networks.The requirements and related key technologies of in-telligent computing networks for large model training were studied,and the standard specifications,academic re-search,and case practices of intelligent computing networks were analyzed,in order to promote the development of intelligent computing networks.

large modelintelligent computing centernetwork technology

郭亮、王少鹏、权伟、李洁

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中国信息通信研究院云计算与大数据研究所,北京 100191

北京交通大学电子信息工程学院,北京 100044

大模型 智算中心 网络技术

新一代人工智能国家科技重大项目

2021ZD0113003

2024

电信科学
中国通信学会 人民邮电出版社

电信科学

CSTPCD北大核心
影响因子:0.902
ISSN:1000-0801
年,卷(期):2024.40(6)
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