1 Ocak 2016 Dergi makalesi Açık Erişim

Yilmaz, Buse; Aktemur, Baris; Garzaran, Maria J.; Kamin, Sam; Kirac, Furkan

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/57993</identifier>
  <creators>
    <creator>
      <creatorName>Yilmaz, Buse</creatorName>
      <givenName>Buse</givenName>
      <familyName>Yilmaz</familyName>
      <affiliation>Ozyegin Univ, TR-34794 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Aktemur, Baris</creatorName>
      <givenName>Baris</givenName>
      <familyName>Aktemur</familyName>
      <affiliation>Ozyegin Univ, TR-34794 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Garzaran, Maria J.</creatorName>
      <givenName>Maria J.</givenName>
      <familyName>Garzaran</familyName>
    </creator>
    <creator>
      <creatorName>Kamin, Sam</creatorName>
      <givenName>Sam</givenName>
      <familyName>Kamin</familyName>
    </creator>
    <creator>
      <creatorName>Kirac, Furkan</creatorName>
      <givenName>Furkan</givenName>
      <familyName>Kirac</familyName>
      <affiliation>Ozyegin Univ, TR-34794 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Autotuning Runtime Specialization For Sparse Matrix-Vector Multiplication</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2016</publicationYear>
  <dates>
    <date dateType="Issued">2016-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/57993</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/2851500</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.</description>
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