Distributed Deep Learning Framework based on Shared Memory for Fast Deep Neural Network Training

2018 ◽  
Author(s):  
Eun-Ji Lim ◽  
Shin-Young Ahn ◽  
Yoo-Mi Park ◽  
Wan Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Shared Memory ◽  
Deep Neural Network ◽  
Neural Network Training ◽  
Learning Framework ◽  
Network Training

scholarly journals Proactive Congestion Avoidance for Distributed Deep Learning

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 174
Author(s):  
Minkoo Kang ◽  
Gyeongsik Yang ◽  
Yeonho Yoo ◽  
Chuck Yoo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Queue Length ◽  
Deep Neural Network ◽  
Congestion Avoidance ◽  
Model Parameters ◽  
Neural Network Training ◽  
Network Congestion ◽  
Network Training ◽  
Deep Learning Model

This paper presents “Proactive Congestion Notification” (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training. In DDL, each worker trains a copy of the deep learning model with different training inputs and synchronizes the model gradients at the end of each iteration. However, it is well known that the network communication for synchronizing model parameters is the main bottleneck in DDL. Our key observation is that the DDL architecture makes each worker generate burst traffic every iteration, which causes network congestion and in turn degrades the throughput of DDL traffic. Based on this observation, the key idea behind PCN is to prevent potential congestion by proactively regulating the switch queue length before DDL burst traffic arrives at the switch, which prepares the switches for handling incoming DDL bursts. In our evaluation, PCN improves the throughput of DDL traffic by 72% on average.

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