Computational Switch Parameter Server for Distributed Deep Learning
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Solution Overview
Problem
In distributed deep learning training environments, the communication synchronization operations of parameter server systems consume significant communication bandwidth and latency, impacting the performance of high-performance computing applications.
Innovation Solution
Implementing a parameter server system within the networking infrastructure of a computing system, utilizing network interface cards, virtual NICs, and computational switches to aggregate and distribute model parameters, thereby reducing communication bandwidth and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a parameter server system is implemented for distributed DL training, then model training can be performed in parallel across multiple worker nodes, but communication synchronization operations consume significant bandwidth and latency
Solution Approach 1:
The patent merges the parameter server functionality with the network infrastructure by implementing a computational switch that combines networking and parameter aggregation functions. This integration allows the system to maintain parallel training productivity while reducing communication bandwidth consumption through more efficient data routing and processing at the network edge.
Solution Approach 2:
The computational switch acts as an intermediary between worker nodes and the central parameter server. It performs local parameter aggregation and routing decisions, reducing the communication burden on the central server and optimizing bandwidth utilization across the distributed training system.
2Productivity
If a parameter server system is implemented for distributed DL training, then model training can be performed in parallel across multiple worker nodes, but communication synchronization operations introduce significant latency
Solution Approach 1:
The patent segments the parameter aggregation function by implementing distributed parameter servers at different levels of the network hierarchy. This segmentation allows local parameter aggregation closer to the data sources, reducing the distance and time for parameter synchronization across the distributed training system.
Solution Approach 2:
The computational switch performs preliminary parameter aggregation and preprocessing at the network edge before data reaches the central server. This preliminary action reduces the volume and criticality of data that requires synchronous processing, thereby reducing overall synchronization latency.
3Productivity
If traditional parameter server implementation is used, then parameter aggregation can be performed centrally, but networking infrastructure becomes a bottleneck for data parallel distributed training
Solution Approach 1:
The patent merges networking functions with computational functions by implementing computational switches that handle both data routing and parameter aggregation. This integration eliminates the bottleneck between networking and computation, improving training efficiency while managing infrastructure complexity through functional consolidation.
Solution Approach 2:
The computational switch provides multi-functionality by simultaneously performing network switching, parameter aggregation, and data routing tasks. This universal component reduces the need for separate dedicated systems, simplifying the overall infrastructure while improving the efficiency of distributed training operations.
Data Source
AI summary
Techniques are provided for implementing a parameter server within a networking infrastructure of a computing system to reduce the communication bandwidth and latency for performing communication synchronization operations of the parameter server. For example, a method includes executing a distributed deep learning (DL) model training process to train model parameters of a DL model using a plurality of worker nodes executing on one or more server nodes of a computing system, and executing a parameter server within a networking infrastructure of the computing system to aggregate local model parameters computed by the plurality of worker nodes and to distribute aggregated model parameters to the plurality of worker nodes using the networking infrastructure of the computing system.