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Understanding the Performances of SMVP on Multiprocessor Platform

Abstract : Sparse Matrix Vector Product (SMVP) is an important kernel in many scientific applications. In this paper we study the performances of this kernel on multiprocessor platform using four different compression format (CSR, CSC, ELL and COO). Our aim is to extract runtime environment parameters, matrix characteristics and algorithm parameters that impact performances. This work is in the context of implementing an auto-tuner system for Optimal sparse Compression Format (OCF) selection.
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Contributor : Thomas Dufaud Connect in order to contact the contributor
Submitted on : Wednesday, August 29, 2018 - 4:02:45 AM
Last modification on : Wednesday, May 11, 2022 - 12:36:04 PM


  • HAL Id : hal-01863729, version 1


Ichrak Mehrez, Olfa Hamdi-Larbi, Thomas Dufaud, Nahid Emad. Understanding the Performances of SMVP on Multiprocessor Platform. The 2018 International Conference on Parallel and Distributed Processing Techniques & Applications PDPTA'18, Jul 2018, Las Vegas, United States. ⟨hal-01863729⟩



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