Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations

Massinissa Merouani; Khaled Afif Boudaoud; Iheb Nassim Aouadj; Nassim Tchoulak; Fatima Benbouzid-Sitayeb; Karima Benatchba; Hugh Leather; Riyadh Baghdadi

arXiv · 2206.03684

Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations

Massinissa Merouani·Khaled Afif Boudaoud·Iheb Nassim Aouadj·Nassim Tchoulak·Fatima Benbouzid-Sitayeb·Karima Benatchba·Hugh Leather·Riyadh Baghdadi

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Abstract

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).

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Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Fatima Benbouzid-Sitayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi. 2022-06-08. Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations. https://arxiv.org/abs/2206.03684

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