Machine learning for optimal compression format prediction on multiprocessor platform

Abstract

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.