December 5, 2018 Report Open Access

Sabina Manafli

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.1967555</identifier>
  <creators>
    <creator>
      <creatorName>Sabina Manafli</creatorName>
      <affiliation>CERN openlab summer student</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Benchmarking Machine Learning in HEP</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>CERN openlab</subject>
    <subject>summer student programme</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-12-05</date>
  </dates>
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    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The interest on machine learning workloads in the HEP community has increased exponentially in the last years, making more and more important the need of a thorough benchmarking of the most relevant/significant workloads that are going to run on the experiments. The purpose of this project is to build a set of techniques to benchmark deep neural networks on different&lt;br&gt;
hardware. By using different tools and methodologies we make several important observations and conclusions based on the performance of deep learning application running on GPUs which have different compute capabilities.&lt;/p&gt;</description>
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