1 Ocak 2016 Dergi makalesi Açık Erişim

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

Dublin Core

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  <dc:creator>Yilmaz, Buse</dc:creator>
  <dc:creator>Aktemur, Baris</dc:creator>
  <dc:creator>Garzaran, Maria J.</dc:creator>
  <dc:creator>Kamin, Sam</dc:creator>
  <dc:creator>Kirac, Furkan</dc:creator>
  <dc:date>2016-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/57993</dc:identifier>
  <dc:identifier>oai:zenodo.org:57993</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 13(1)</dc:source>
  <dc:title>Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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