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Journal articles

Performance Optimization and Modeling of Blocked Sparse Kernels

Abstract : We present a method for automatically selecting optimal implementations of sparse matrix-vector operations. Our software “AcCELS” (Accelerated Compress-storage Elements for Linear Solvers) involves a setup phase that probes machine characteristics, and a run-time phase where stored characteristics are combined with a measure of the actual sparse matrix to find the optimal kernel implementation. We present a performance model that is shown to be accurate over a large range of matrices.
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Contributor : Alfredo Buttari Connect in order to contact the contributor
Submitted on : Friday, December 20, 2019 - 11:54:14 AM
Last modification on : Wednesday, November 3, 2021 - 7:17:22 AM

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Alfredo Buttari, Victor Eijkhout, Julien Langou, Salvatore Filippone. Performance Optimization and Modeling of Blocked Sparse Kernels. International Journal of High Performance Computing Applications, SAGE Publications, 2016, 21 (4), pp.467-484. ⟨10.1177/1094342007083801⟩. ⟨hal-02421133⟩



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