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

arXiv: https://arxiv.org/abs/2206.03684 · HTML: https://mlsanity.com/paper/2206.03684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Abstract (arXiv)

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).