[Idr] 转发: New Version Notification for draft-xu-idr-fare-02.txt
Tiger Xu <xuxiaohu_ietf@hotmail.com> Mon, 02 September 2024 09:41 UTC
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From: Tiger Xu <xuxiaohu_ietf@hotmail.com>
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Subject: [Idr] 转发: New Version Notification for draft-xu-idr-fare-02.txt
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Hi all, In the latest version, we added: - Comparison with related works - Considerations related to server multi-homing scenarios - Improvement on the weighted ECMP load-balancing scheme in 5-stage CLOS networks Any further comments or suggestions are welcome. Best regards, Xiaohu 发件人: internet-drafts@ietf.org <internet-drafts@ietf.org> 日期: 星期一, 2024年9月2日 10:15 收件人: Hang Wu <wuhang@ruijie.com.cn>, Hongyi Huang <hongyi.huang@huawei.com>, Junjie Wang <wangjj@centec.com>, Peilong Wang <wangpeilong01@baidu.com>, Qingliang Zhang <zhangqingliang@h3c.com>, Shraddha Hegde <shraddha@juniper.net>, Tiezheng <litiezheng@ieisystem.com>, Tiezheng Li <litiezheng@ieisystem.com>, Xiaohu Xu <xuxiaohu_ietf@hotmail.com>, Yadong Liu <zeepliu@tencent.com>, Yinben Xia <forestxia@tencent.com>, Zongying He <zongying.he@broadcom.com> 主题: New Version Notification for draft-xu-idr-fare-02.txt A new version of Internet-Draft draft-xu-idr-fare-02.txt has been successfully submitted by Xiaohu Xu and posted to the IETF repository. Name: draft-xu-idr-fare Revision: 02 Title: Fully Adaptive Routing Ethernet using BGP Date: 2024-09-01 Group: Individual Submission Pages: 11 URL: https://www.ietf.org/archive/id/draft-xu-idr-fare-02.txt Status: https://datatracker.ietf.org/doc/draft-xu-idr-fare/ HTMLized: https://datatracker.ietf.org/doc/html/draft-xu-idr-fare Diff: https://author-tools.ietf.org/iddiff?url2=draft-xu-idr-fare-02 Abstract: Large language models (LLMs) like ChatGPT have become increasingly popular in recent years due to their impressive performance in various natural language processing tasks. These models are built by training deep neural networks on massive amounts of text data, often consisting of billions or even trillions of parameters. However, the training process for these models can be extremely resource- intensive, requiring the deployment of thousands or even tens of thousands of GPUs in a single AI training cluster. Therefore, three- stage or even five-stage CLOS networks are commonly adopted for AI networks. The non-blocking nature of the network become increasingly critical for large-scale AI models. Therefore, adaptive routing is necessary to dynamically distribute the traffic to the same destination over multiple equal-cost paths, based on the network capacity and even congestion information along those paths. The IETF Secretariat