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In this paper, we present a work in progress about a deep learning basedapproach for automatic code optimization in polyhedral compilers. The proposedtechnique explores combinations of affine and non-affine loop transformationsto find the sequence of transformations that minimizes the execution time of agiven program. This exploration is guided by a deep learning based cost modelthat evaluates the speedup that each sequence of transformations would yield.Preliminary results show that the proposed techniques achieve a 2.35x geometricmean speedup over state of the art polyhedral compilers (Pluto).

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