| Autor: |
Yilmaz, Buse, Aktemur, Baris, Garzaran, Maria J., Kamin, Sam, Kirac, Furkan |
| Rok vydání: |
2016 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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