Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations. (arXiv:2206.03684v1 [cs.PL])

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 …

arxiv deep learning exploration learning loop pl progress report