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.
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.
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.
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.