Yılmaz, BuseAktemur, Tankut BarışGarzaran, M. J.Kamin, S.Kıraç, Mustafa Furkan2016-07-292016-07-292016-041544-3973http://hdl.handle.net/10679/4351https://doi.org/10.1145/2851500Due to copyright restrictions, the access to the full text of this article is only available via subscription.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.engrestrictedAccessarticle13112600037390460000510.1145/2851500PerformanceExperimentationMeasurementAutotuningRuntime code generationSparse matrix-vector multiplication2-s2.0-84971629591