1 Ocak 2016 Dergi makalesi Açık Erişim

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

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    <subfield code="a">Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication</subfield>
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    <subfield code="a">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.</subfield>
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