A machine learning-oriented distributed computing interconnection network system and communication method

A distributed computing and interconnection network technology, which is applied in the field of distributed computing interconnection network system and communication, can solve the problems of large network diameter, lack of self-regulation, and increased machine learning task execution time, so as to improve balance ability, The effect of improving the ability to deal with complex network environments and reducing communication time

Active Publication Date: 2020-12-08
XIDIAN UNIV
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Problems solved by technology

However, its shortcomings are: 1. The network diameter of the interconnection network system is large, which increases the execution time of machine learning tasks; 2. The communication method can only complete the transmission of data packets between communication node pairs, and does not have Ability to self-regulate

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  • A machine learning-oriented distributed computing interconnection network system and communication method
  • A machine learning-oriented distributed computing interconnection network system and communication method
  • A machine learning-oriented distributed computing interconnection network system and communication method

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Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] refer to figure 1 , a distributed computing interconnection network system for machine learning, including an optical switching subsystem and a computing subsystem.

[0047] The optical switching subsystem includes a 0 , A 1 ,...,A i ,...,A 2N-1 2N optical switches, where N≥1, A i Indicates the i-th optical switch. For the convenience of observation and description, in this embodiment N=4, the number of optical switches included in the optical switching subsystem is 8, and the specific number is A 0 , A 1 ,...,A 6 , A 7 , each optical switch includes N switching ports, that is, each optical switch includes 4 switching ports.

[0048] The computing subsystem includes N computing units. In this embodiment, the computing subsystem includes 4 computing units, and the number of computing units is consistent with the number...

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Abstract

The invention provides a machine learning-oriented distributed computing interconnection network system and a communication method. The communication efficiency of an interconnection network is improved, and meanwhile, the flow load in the distributed computing interconnection network can be better balanced. The optical switching subsystem of the distributed computing interconnection network comprises 2N optical switches. The computing subsystem comprises N computing units. Each computing unit comprises N sub-computing units, and each sub-computing unit comprises a photoelectric hybrid switchand N computing nodes. The communication process is as follows: the computing node pairs communicating in the same computing unit can finish a communication task only by forwarding of the photoelectric hybrid switch, and the computing node pairs not communicating in the same computing unit need cooperation of the photoelectric hybrid switch and the optical switch to finish the communication task.According to the invention, the system has good expandability; the network diameter is reduced; the communication time delay of the system is reduced; and the capability of the system for coping witha complex network environment is improved.

Description

technical field [0001] The present invention belongs to the field of communication technology, and further relates to a machine learning-oriented distributed computing interconnection network system and communication method in the field of interconnection network communication technology. The network system realizes efficient communication between computing nodes in the distributed computing interconnection network system. Background technique [0002] With the development of computing technology, many emerging applications require huge computing power to complete, especially machine learning applications based on big data and large models. Centralized computing relies entirely on the processing power of a large central computer. Contrary to centralized computing, in distributed computing, multiple computing nodes interconnected through the network have high computing power, and they transfer data to each other, realize information sharing, and work together to complete a p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04Q11/00
CPCH04Q11/0005H04Q2011/0007H04Q2011/0052
Inventor 顾华玺鹿云峰余晓杉王琨任泽昂
Owner XIDIAN UNIV
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