LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

no code implementations • 18 Mar 2024 • Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Islem Kara Bernou, Hamza Benyamina, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi

In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs.

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

no code implementations • 8 Jun 2022 • Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Fatima Benbouzid-Sitayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi

In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers.

A Deep Learning Based Cost Model for Automatic Code Optimization

no code implementations • 11 Apr 2021 • Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, Saman Amarasinghe

Unlike previous models, the proposed one works on full programs and does not rely on any heavy feature engineering.

Feature Engineering

QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection

1 code implementation • International Work-Conference on Artificial Neural Networks 2019 • Souhila Sadeg, Leila Hamdad, Amine Riad Remache, Mehdi Nedjmeddine Karech, Karima Benatchba, Zineb Habbas

In this work, we propose a hybrid metaheuristic that integrates a reinforcement learning algorithm with Bee Swarm Optimization metaheuristic (BSO) for solving feature selection problem.

feature selection Multi-agent Reinforcement Learning +3