Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

作者：Yilmaz Buse^*; Aktemur Baris^*; Garzaran Maria J^*; Kamin Sam^*; Kirac Furkan^*

来源：ACM Transactions on Architecture and Code Optimization, 2016, 13(1): 5.

DOI：10.1145/2851500

• Performance
• Experimentation
• Measurement
• Autotuning
• runtime code generation
• sparse matrix-vector multiplication

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.

• 出版日期2016-4