1 Ocak 2016 Dergi makalesi Açık Erişim

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

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{
  "@context": "https://schema.org/", 
  "@id": 57993, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Ozyegin Univ, TR-34794 Istanbul, Turkey", 
      "name": "Yilmaz, Buse"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Ozyegin Univ, TR-34794 Istanbul, Turkey", 
      "name": "Aktemur, Baris"
    }, 
    {
      "@type": "Person", 
      "name": "Garzaran, Maria J."
    }, 
    {
      "@type": "Person", 
      "name": "Kamin, Sam"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Ozyegin Univ, TR-34794 Istanbul, Turkey", 
      "name": "Kirac, Furkan"
    }
  ], 
  "datePublished": "2016-01-01", 
  "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.", 
  "headline": "Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication", 
  "identifier": 57993, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication", 
  "url": "https://aperta.ulakbim.gov.tr/record/57993"
}