Machine Learning for Optimal Compression Format Prediction on Multiprocessor Platform

Mehrez, Ichrak; Hamdi-Larbi, Olfa; Dufaud, Thomas; Emad, Nahid

doi:10.1109/hpcs.2018.00047

2018 International Conference on High Performance Computing &Amp; Simulation (HPCS) 2018

DOI: 10.1109/hpcs.2018.00047

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Machine Learning for Optimal Compression Format Prediction on Multiprocessor Platform

Ichrak Mehrez¹,

Olfa Hamdi-Larbi²,

Thomas Dufaud³

et al.

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2021

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Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

Hamdi-Larbi

Mehrez

Dufaud

2021

Parallel Process. Lett.

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Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

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Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

Hamdi-Larbi

Mehrez

Dufaud

2021

Parallel Process. Lett.

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Machine Learning for Optimal Compression Format Prediction on Multiprocessor Platform

Cited by 1 publication

References 12 publications

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations

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