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Performance Optimization and Modeling of Blocked Sparse Kernels

Alfredo Buttari 1 Victor Eijkhout 2 Julien Langou 3 Salvatore Filippone 4
1 IRIT-APO - Algorithmes Parallèles et Optimisation
IRIT - Institut de recherche en informatique de Toulouse
2 Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas
Texas Advanced Computing Center
3 Department of Mathematical and Statistical Sciences
4 University of Rome "Tor Vergeta"
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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Journal articles
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https://hal.archives-ouvertes.fr/hal-02421133
Contributor : Alfredo Buttari <>
Submitted on : Friday, December 20, 2019 - 11:54:14 AM
Last modification on : Tuesday, September 8, 2020 - 9:00:04 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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