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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors

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Computer Information Systems and Industrial Management (CISIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12133))

Abstract

In the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore, we measured results for various numbers of threads with ranges depending on a given processor(s) as well as compact and scatter affinities. Based on results we formulate conclusions with respect to optimal parameters and relative performances which can serve as hints for researchers training similar networks using modern processors.

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Notes

  1. 1.

    http://ai-benchmark.com/.

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Acknowledgments

This work is partially supported by Intel and the Intel Labs Academic Compute Environment. Work partially performed within statutory activities of Dept. of Computer Architecture, Faculty of ETI, Gdańsk University of Technology.

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Authors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland

    Klaudia Jabłońska & Paweł Czarnul

Authors
  1. Klaudia Jabłońska
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  2. Paweł Czarnul
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Corresponding author

Correspondence to Paweł Czarnul .

Editor information

Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

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Jabłońska, K., Czarnul, P. (2020). Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-47679-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47678-6

  • Online ISBN: 978-3-030-47679-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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