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 re...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2024-06-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024147/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841528822230417408 |
|---|---|
| author | GUO Liang WANG Shaopeng QUAN Wei LI Jie |
| author_facet | GUO Liang WANG Shaopeng QUAN Wei LI Jie |
| author_sort | GUO Liang |
| collection | DOAJ |
| description | 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, latency, reliability, scalability and security of datacenter networks. The requirements and related key technologies of intelligent computing networks for large model training were studied, and the standard specifications, academic research, and case practices of intelligent computing networks were analyzed, in order to promote the development of intelligent computing networks. |
| format | Article |
| id | doaj-art-05e8fd059c344d78aa34f7c59080461e |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-05e8fd059c344d78aa34f7c59080461e2025-01-15T03:33:32ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-06-014013714564852431Research on the development of intelligent computing network for large modelsGUO LiangWANG ShaopengQUAN WeiLI JieIn 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, latency, reliability, scalability and security of datacenter networks. The requirements and related key technologies of intelligent computing networks for large model training were studied, and the standard specifications, academic research, and case practices of intelligent computing networks were analyzed, in order to promote the development of intelligent computing networks.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024147/large modelintelligent computing centernetwork technology |
| spellingShingle | GUO Liang WANG Shaopeng QUAN Wei LI Jie Research on the development of intelligent computing network for large models Dianxin kexue large model intelligent computing center network technology |
| title | Research on the development of intelligent computing network for large models |
| title_full | Research on the development of intelligent computing network for large models |
| title_fullStr | Research on the development of intelligent computing network for large models |
| title_full_unstemmed | Research on the development of intelligent computing network for large models |
| title_short | Research on the development of intelligent computing network for large models |
| title_sort | research on the development of intelligent computing network for large models |
| topic | large model intelligent computing center network technology |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024147/ |
| work_keys_str_mv | AT guoliang researchonthedevelopmentofintelligentcomputingnetworkforlargemodels AT wangshaopeng researchonthedevelopmentofintelligentcomputingnetworkforlargemodels AT quanwei researchonthedevelopmentofintelligentcomputingnetworkforlargemodels AT lijie researchonthedevelopmentofintelligentcomputingnetworkforlargemodels |