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Basic data
| Standard ID | GB/T 45401.2-2025 (GB/T45401.2-2025) |
| Description (Translated English) | Artificial intelligence - Scheduling and cooperation for computing devices - Part 2: Framework for distributed computing |
| Sector / Industry | National Standard (Recommended) |
| Classification of Chinese Standard | L70 |
| Classification of International Standard | 35.020 |
| Word Count Estimation | 26,217 |
| Date of Issue | 2025-03-28 |
| Date of Implementation | 3/28/2025 |
| Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration |
GB/T 45401.2-2025: Artificial intelligence - Scheduling and cooperation for computing devices - Part 2: Framework for distributed computing
---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
ICS 35.020
CCSL70
National Standard of the People's Republic of China
AI computing equipment scheduling and coordination
Part 2.Distributed Computing Framework
Released on 2025-03-28
2025-03-28 Implementation
State Administration for Market Regulation
The National Standardization Administration issued
Table of contents
Preface III
Introduction IV
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
4 Abbreviations 2
5 Overview 2
5.1 Overall Framework 2
5.2 Applicability 3
6 Computing Equipment Technical Requirements 3
6.1 Cloud-side equipment 3
6.2 Side equipment 4
6.3 Devices on the client side 4
7 Distributed computing collaboration technology requirements 5
7.1 Architecture 5
7.2 General requirements 5
7.3 Component Requirements 6
7.4 Cloud-to-Cloud Collaboration Requirements 8
7.5 Requirements for cloud-edge-device collaboration 9
7.6 Multi-terminal collaboration requirements 11
8 Cloud-edge distributed computing collaborative interface 12
8.1 Edge Node Management 12
8.2 Deployment and Update 14
8.3 Task Operation Management 17
References 19
Foreword
This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents"
Drafting.
This document is part 2 of GB/T 45401 "Scheduling and coordination of artificial intelligence computing equipment". GB/T 45401 has been published
The following parts.
--- Part 1.Virtualization and scheduling;
--- Part 2.Distributed computing framework.
Please note that some of the contents of this document may involve patents. The issuing organization of this document does not assume the responsibility for identifying patents.
This article was proposed and coordinated by the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. China Electronics Standardization Institute, Huawei Technologies Co., Ltd., Beijing University of Aeronautics and Astronautics, Chinese Academy of Sciences
Software Research Institute, Huawei Cloud Computing Technology Co., Ltd., Alibaba Cloud Computing Co., Ltd., Beijing Baidu Netcom Technology Co., Ltd., Inspur Electronics
Information Industry Co., Ltd., Shanghai SenseTime Intelligent Technology Co., Ltd., Peking University Wuhan Artificial Intelligence Research Institute, Shanghai Artificial Intelligence Industry Co., Ltd.
Industry Association, China Mobile Communications Group Co., Ltd., Institute of Computing Technology, Chinese Academy of Sciences, iFLYTEK Co., Ltd., Peking University,
Shenzhen Yuntian Lifei Technology Co., Ltd., Shanghai Tianshu Zhixin Semiconductor Co., Ltd., Beijing Biren Technology Development Co., Ltd., Hangzhou Hai
Kangwei Digital Technology Co., Ltd., China Southern Power Grid Artificial Intelligence Technology Co., Ltd., Loongson Technology Co., Ltd., Suzhou Deng
Lin Technology Co., Ltd., Zhejiang Dahua Technology Co., Ltd., Ant Technology Group Co., Ltd., Guoke Chushi (Chongqing) Software Co., Ltd.
Company, Guangdong Power Grid Co., Ltd., Guangdong Broadcasting Corporation Limited, Shanghai Computer Software Technology Development Center, Shanghai Wenyuxin
Information Technology Co., Ltd., BOE Technology Group Co., Ltd., and Tianjin (Binhai) Artificial Intelligence Innovation Center.
The main drafters of this document are. Cao Xiaoqi, Dong Jian, Yang Yuze, Bao Wei, Xu Yang, Yu Chao, Li Binbin, Wang Waner, Luan Zhongzhi, Zhu Yixin, Dong Qian,
Meng Lingzhong, Zheng Zimu, Wu Tao, Tian Xiaoli, Zhang Yaqiang, Ma Shanshan, Ma Chenghao, Zhao Chunhao, Wu Geng, Cao Xi, Wang Yuwei, Wu Ting, Yang Chao, Wang Zhifang,
Yu Xuesong, Ding Ruiquan, Ye Tingqun, Dong Zhaojie, Ma Wanyue, Dai Jun, Kong Weisheng, Guo Zhihui, Luo Yongjun, Yan Yuping, Chen Haomin, Yang Bo, Chen Minggang,
Niu Keke, Zhong Kaitao, Jiang Xingqun, and Shi Dianxi.
Introduction
With the continuous development of AI computing, the deployment and use of computing devices that carry AI applications have become distributed and full-scale.
The same AI computing task often requires the collaboration of multiple computing devices to provide services for users in different regions and types.
It is necessary to rationally utilize and allocate computing equipment resources of different forms, and clarify the necessary technical architecture, capability requirements and interfaces.
Etc., provide a reference framework and evaluation system for products, and alleviate the current situation of horizontal collaboration and fragmentation of different forms of artificial intelligence computing devices.
GB/T 45401 “Scheduling and coordination of artificial intelligence computing devices” is planned to consist of two parts.
--- Part 1.Virtualization and Scheduling, aims to establish the architecture of the virtualization and scheduling system for artificial intelligence computing devices and stipulate the technical requirements
Request and corresponding test methods.
--- Part 2.Distributed computing framework, which aims to establish the architecture of distributed computing for artificial intelligence computing devices and specify functions and performance
Technical requirements,define the distributed computing collaboration interface.
AI computing equipment scheduling and coordination
Part 2.Distributed Computing Framework
1 Scope
This document establishes the architecture of distributed computing for artificial intelligence computing devices, specifies functional and performance technical requirements, and defines distributed computing
It is a collaborative interface.
This document is applicable to the design, development, and testing of distributed artificial intelligence computing systems.
2 Normative references
The contents of the following documents constitute essential clauses of this document through normative references in this document.
For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to
This document.
GB/T 41867 Information Technology Artificial Intelligence Terminology
GB/T 42018-2022 Information technology artificial intelligence platform computing resources specification
GB/T 45401.1-2025 Scheduling and collaboration of artificial intelligence computing devices Part 1.Virtualization and scheduling
GB/T 45280-2025 Unified interface for heterogeneous artificial intelligence accelerators
GB/T 45087-2024 Artificial Intelligence Server System Performance Test Method
3 Terms and definitions
GB/T 41867, GB/T 42018-2022, GB/T 45401.1-2025 and GB/T 45280-2025 and the following terms
The following terms and definitions apply to this document.
3.1
A machine learning method or process that uses the Internet as the preferred communication carrier to complete the same machine learning task on different subsystems.
Learning tasks.
Note 1.The Internet includes the local Internet and the wide area Internet.
Note 2.Distributed machine learning is divided into distributed training and distributed reasoning according to the different types of machine learning tasks.
[Source. ISO /IEC 2382.2015, 2178059, modified]
3.2
federated [machine] learning
A machine learning method or process that enables multiple participants to collaboratively build and use machine learning models without exposing the participants' private
data.
3.3
Incremental learning
A multi-stage adaptive learning approach in which knowledge acquired in a precursor phase is transformed into a suitable form for subsequent
...
