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A Comparison of ARM Against x86 for Distributed Machine Learning Workloads

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Performance Evaluation and Benchmarking for the Analytics Era (TPCTC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10661))

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Abstract

The rise of Machine Learning (ML) in the last decade has created an unprecedented surge in demand for new and more powerful hardware. Various hardware approaches exist to take on these large demands motivating the need for hardware performance benchmarks to compare these diverse hardware systems. In this paper, we present a comprehensive analysis and comparison of available benchmark suites in the field of ML and related fields. The analysis of these benchmarks is used to discuss the potential of ARM processors within the context of ML deployments. Our paper concludes with a brief hardware performance comparison of modern, server-grade ARM and x86 processors using a benchmark suite selected from our survey.

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Acknowledgements

This work was supported by the Data Center Technology Lab of Huawei.

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Authors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Sebastian Kmiec, Jonathon Wong & Hans-Arno Jacobsen

  2. Futurewei Technologies, Santa Clara, CA, USA

    Da Qi Ren

Authors
  1. Sebastian Kmiec
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  2. Jonathon Wong
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  3. Hans-Arno Jacobsen
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  4. Da Qi Ren
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Corresponding author

Correspondence to Hans-Arno Jacobsen .

Editor information

Editors and Affiliations

  1. Cisco Systems, Inc., San Jose, California, USA

    Raghunath Nambiar

  2. Server Technologies, Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

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Kmiec, S., Wong, J., Jacobsen, HA., Ren, D.Q. (2018). A Comparison of ARM Against x86 for Distributed Machine Learning Workloads. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking for the Analytics Era. TPCTC 2017. Lecture Notes in Computer Science(), vol 10661. Springer, Cham. https://doi.org/10.1007/978-3-319-72401-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-72401-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72400-3

  • Online ISBN: 978-3-319-72401-0

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