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Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis

Authors:

Torsten HoeflerAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 4

Article No.: 65, Pages 1 - 43

https://doi.org/10.1145/3320060

Published: 30 August 2019 Publication History

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Abstract

Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.

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Bouvier TNicolae BCostan ABicer TFoster IAntoniu G(2025)Efficient distributed continual learning for steering experiments in real-timeFuture Generation Computer Systems10.1016/j.future.2024.07.016162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.07.016
Guan LZhang SChen Y(2024)Energy-Efficient Deep Learning TrainingAdvances in Energy Recovery and Efficiency Technologies [Working Title]10.5772/intechopen.1007491Online publication date: 4-Nov-2024
https://doi.org/10.5772/intechopen.1007491
Romero-Sandí HNúñez GRojas E(2024)A snapshot of parallelism in distributed deep learning trainingRevista Colombiana de Computación10.29375/25392115.505425:1(60-73)Online publication date: 30-Jun-2024
https://doi.org/10.29375/25392115.5054
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Index Terms

Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis
1. Computing methodologies
2. General and reference
  1. Document types
    1. Surveys and overviews

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 4

July 2020

769 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3359984

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 August 2019

Accepted: 01 March 2019

Received: 01 August 2018

Published in CSUR Volume 52, Issue 4

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Cited By

Bouvier TNicolae BCostan ABicer TFoster IAntoniu G(2025)Efficient distributed continual learning for steering experiments in real-timeFuture Generation Computer Systems10.1016/j.future.2024.07.016162:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.future.2024.07.016
Guan LZhang SChen Y(2024)Energy-Efficient Deep Learning TrainingAdvances in Energy Recovery and Efficiency Technologies [Working Title]10.5772/intechopen.1007491Online publication date: 4-Nov-2024
https://doi.org/10.5772/intechopen.1007491
Romero-Sandí HNúñez GRojas E(2024)A snapshot of parallelism in distributed deep learning trainingRevista Colombiana de Computación10.29375/25392115.505425:1(60-73)Online publication date: 30-Jun-2024
https://doi.org/10.29375/25392115.5054
Liu WXu XWu JJiang J(2024)Federated Meta Reinforcement Learning for Personalized TasksTsinghua Science and Technology10.26599/TST.2023.901006629:3(911-926)Online publication date: Jun-2024
https://doi.org/10.26599/TST.2023.9010066
Nichols DMarathe AMenon HGamblin TBhatele A(2024)HPC-Coder: Modeling Parallel Programs using Large Language ModelsISC High Performance 2024 Research Paper Proceedings (39th International Conference)10.23919/ISC.2024.10528929(1-12)Online publication date: May-2024
https://doi.org/10.23919/ISC.2024.10528929
Liu ZXu XQiao PLi D(2024)Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A SurveyACM Computing Surveys10.1145/3703453Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3703453
Mohanty SBhasi VSon MKandemir MDas C(2024)FAAStloop: Optimizing Loop-Based Applications for Serverless ComputingProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698560(943-960)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698560
Jia DYuan GXie YLin XMi N(2024)A Data-Loader Tunable Knob to Shorten GPU Idleness for Distributed Deep LearningACM Transactions on Architecture and Code Optimization10.1145/368054621:4(1-25)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3680546
Das Sharma DBlankenship RBerger D(2024)An Introduction to the Compute Express Link (CXL) InterconnectACM Computing Surveys10.1145/366990056:11(1-37)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3669900
Jacobs STanaka MZhang CZhang MAminadabi RSong SRajbhandari SHe YKuznetsov PGelles ROlivetti D(2024)System Optimizations for Enabling Training of Extreme Long Sequence Transformer ModelsProceedings of the 43rd ACM Symposium on Principles of Distributed Computing10.1145/3662158.3662806(121-130)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3662158.3662806
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