Machine Learning for Optimal Compression Format Prediction on Multiprocessor Platform

Ichrak Mehrez (1) , Olfa Hamdi-Larbi (2) , Thomas Dufaud (1, 3) , Nahid Emad (1, 3)

1 LI-PaRAD - Laboratoire d'Informatique Parallélisme Réseaux Algorithmes Distribués
2 UTM - Université de Tunis El Manar
3 MDLS - Maison de la Simulation

Many scientific applications handle large size sparse matrices which can be stored using special compression formats to reduce memory space and processing time. The choice of the Optimal Compression Format (OCF) is a critical process that involves several criteria. In this paper, we propose to use machine learning approach to predict the OCF (among CSR, CSC, ELL and COO) for SMVP kernel on multiprocessor platform. Our goal is not only to reach high accuracy values but also to minimize the LUBS (Loss Under Best Selection). Our main contribution consists in using data parallel model to extract features dataset. Experimental results show that we achieve more than 95% accuracy.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Ichrak Mehrez, Olfa Hamdi-Larbi, Thomas Dufaud, Nahid Emad. Machine Learning for Optimal Compression Format Prediction on Multiprocessor Platform. 2018 International Conference on High Performance Computing & Simulation (HPCS), Jul 2018, Orleans, France. ⟨10.1109/HPCS.2018.00047⟩. ⟨hal-01927621⟩