1 Ocak 2016 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1145/2851500", 
  "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.", 
  "author": [
    {
      "family": "Yilmaz", 
      "given": " Buse"
    }, 
    {
      "family": "Aktemur", 
      "given": " Baris"
    }, 
    {
      "family": "Garzaran", 
      "given": " Maria J."
    }, 
    {
      "family": "Kamin", 
      "given": " Sam"
    }, 
    {
      "family": "Kirac", 
      "given": " Furkan"
    }
  ], 
  "container_title": "ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION", 
  "id": "57993", 
  "issue": "1", 
  "issued": {
    "date-parts": [
      [
        2016, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication", 
  "type": "article-journal", 
  "volume": "13"
}