In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers. The proposed technique explores combinations of affine and non-affine loop transformations to find the sequence of transformations that minimizes the execution time of a given program. This exploration is guided by a deep learning based cost model that evaluates the speedup that each sequence of transformations would yield. Preliminary results show that the proposed techniques achieve a 2.35x geometric mean speedup over state of the art polyhedral compilers (Pluto).
@article{arxiv.2206.03684,
title = {Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations},
author = {Massinissa Merouani and Khaled Afif Boudaoud and Iheb Nassim Aouadj and Nassim Tchoulak and Fatima Benbouzid-Sitayeb and Karima Benatchba and Hugh Leather and Riyadh Baghdadi},
journal= {arXiv preprint arXiv:2206.03684},
year = {2022}
}