P2P Distributed ML Architecture Reducing Communication Costs
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Solution Overview
Problem
Existing distributed Machine Learning (ML) systems face inefficiencies due to their reliance on asymmetric client-server architectures, which incur high network communication costs and are not well-suited for peer-to-peer (P2P) synchronization, limiting the performance of ML programs in datacenter environments.
Innovation Solution
A distributed peer-to-peer system is implemented, featuring a parser module to convert loss functions into expression graphs and multiplication trees, a compressor module to extract sufficient factors, and a communication module to reduce network communication costs by broadcasting these factors across machines, optimizing the update of model parameter matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a peer-to-peer architecture is used for distributed machine learning, then fault tolerance and resource elasticity are improved, but network communication cost increases due to quadratic synchronization requirements
Solution Approach 1:
The patent extracts and transmits only the essential gradient information (sufficient statistics) between machines rather than complete model states. Each machine computes local gradients and extracts only the necessary update factors, reducing communication volume from O(n²) to O(n) while maintaining P2P fault tolerance benefits
Solution Approach 2:
The model parameter matrix is segmented into multiple machines, with each machine holding a portion. The gradient computation is also segmented, allowing parallel processing while reducing synchronization overhead through the factor extraction method described in the patent
2Loss of energy
If a client-server architecture is used for distributed machine learning, then network communication cost is reduced, but fault tolerance and resource elasticity deteriorate due to single failure points
Solution Approach 1:
The patent implements an asymmetric communication pattern in the P2P architecture where machines send compressed gradient factors rather than full gradients. This asymmetric information exchange reduces communication overhead while maintaining the symmetric fault-tolerant structure of P2P, resolving the contradiction between communication efficiency and reliability
3Manufacturing precision
If complete gradients are transmitted in peer-to-peer distributed machine learning, then model update accuracy is maintained, but communication bandwidth consumption increases
Solution Approach 1:
The patent extracts sufficient statistical factors from complete gradients that contain only the essential information needed for model updates. These extracted factors are transmitted between machines, maintaining update accuracy while reducing bandwidth consumption by eliminating redundant gradient components
Solution Approach 2:
Instead of transmitting complete gradients and having receiving machines process them, the patent inverts the approach by transmitting only the extracted sufficient factors that directly enable model updates at the receiving end, reducing communication volume while preserving accuracy
Data Source
AI summary
A computer in a distributed peer-to-peer system is disclosed. The distributed system includes a plurality of computers configured to run a distributed machine learning (ML) program represented as an expression of a target loss function with a model parameter matrix. The computer includes: a parser module configured to convert a loss function in the distributed program into an expression graph and then one or more multiplication trees; a parameter replica module in communication with the parser module, the parameter replica module configured to maintain the model parameter matrix of the ML program; a compressor module in communication with the parameter replica module, the compressor module configured to extract sufficient factors from the expression graph for updating the model matrix; and a communication module in communication with the compressor module, the communication module configured to send the sufficient factors for updating model matrix to other machines in the distributed system.