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Auto-tuning pour la détection automatique du meilleur format de compression pour matrice creuse

Abstract : Several applications in scientific computing deals with large sparse matrices having regular or irregular structures. In order to reduce required memory space and computing time, these matrices require the use of a particular data storage structure as well as the use of parallel/distributed target architectures. The choice of the most appropriate compression format generally depends on several factors, such as matrix structure, numerical method and target architecture. Given the diversity of these factors, an optimized choice for one input data set will likely have poor performances on another. Hence the interest of using a system allowing the automatic selection of the Optimal Compression Format (OCF) by taking into account these different factors. This thesis is written in this context. We detail our approach by presenting a design of an auto-tuner system for OCF selection. Given a sparse matrix, a numerical method, a parallel programming model and an architecture, our system can automatically select the OCF. In a first step, we validate our modeling by a case study that concerns (i) Horner scheme, and then the sparse matrix vector product (SMVP), as numerical methods, (ii) CSC, CSR, ELL, and COO as compression formats; (iii) data parallel as a programming model; and (iv) a multicore platform as target architecture. This study allows us to extract a set of metrics and parameters that affect the OCF selection. We note that data parallel metrics are not sufficient to accurately choose the most suitable format. Therefore, we define new metrics involving the number of operations and the number of indirect data access. Thus, we proposed a new decision process taking into account data parallel model analysis and algorithm analysis.In the second step, we propose to use machine learning algorithm to predict the OCF for a given sparse matrix. An experimental study using a multicore parallel platform and dealing with random and/or real-world random matrices validates our approach and evaluates its performances. As future work, we aim to validate our approach using other parallel platforms such as GPUs.
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Submitted on : Tuesday, December 18, 2018 - 4:07:08 PM
Last modification on : Wednesday, October 14, 2020 - 4:20:35 AM
Long-term archiving on: : Wednesday, March 20, 2019 - 9:19:18 AM


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  • HAL Id : tel-01959289, version 1



Ichrak Mehrez. Auto-tuning pour la détection automatique du meilleur format de compression pour matrice creuse. Automatique. Université Paris-Saclay; Université de Tunis El-Manar. Faculté des Sciences de Tunis (Tunisie), 2018. Français. ⟨NNT : 2018SACLV054⟩. ⟨tel-01959289⟩



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