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

• URL: http://arxiv.org/abs/2206.03684v1
• Date: Wed, 8 Jun 2022 05:47:42 GMT
• Title: Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations
• Authors: Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Fatima Benbouzid-Sitayeb, Karima Benatchba, Hugh Leather, and Riyadh Baghdadi
• Abstract summary: 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. Preliminary results show that the proposed techniques achieve a 2.35x geometric mean speedup over state of the art polyhedral compilers.
• Score: 1.5699353548228476
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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