Work

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

Year: 2022

Type: preprint

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).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2206.03684

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

Institutions: New York University Abu Dhabi, École Nationale Supérieure d'Informatique

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4281661024

Cites:

Cited by: 1

Related to: 10