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.

PolyTOPS: Reconfigurable and Flexible Polyhedral Scheduler

no code implementations • 12 Jan 2024 • Gianpietro Consolaro, Zhen Zhang, Harenome Razanajato, Nelson Lossing, Nassim Tchoulak, Adilla Susungi, Artur Cesar Araujo Alves, Renwei Zhang, Denis Barthou, Corinne Ancourt, Cedric Bastoul

Different scenarios, depending on the target architecture, compilation environment, and application domain, may require different kinds of optimization to best exploit the architecture feature set.

Scheduling

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.