Proactive congestion avoidance for distributed deep learning

Minkoo Kang, Gyeongsik Yang, Yeonho Yoo, Chuck Yoo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number174
Pages (from-to)1-18
Number of pages18
JournalSensors (Switzerland)
Volume21
Issue number1
DOIs
Publication statusPublished - 2021 Jan 1

Keywords

  • Congestion avoidance
  • Deep learning
  • Distributed deep learning
  • Network congestion
  • P4
  • Proactive congestion notification

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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