1 citations · 1 across the 3 of their papers we have counts for
6 papers
Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop Optimization
Massinissa Merouani, Islem Kara Bernou, Riyadh Baghdadi
Automatic code optimization remains a difficult challenge, particularly for complex loop nests on modern hardware. This paper investigates a novel approach to code optimization whe…
A Deep Learning Model for Predicting Transformation Legality
Avani Tiwari, Yacine Hakimi, Riyadh Baghdadi
Compilers must check the legality of code transformations to guarantee the correctness of applying a sequence of code transformations to a given code. While such a legality check n…
A Data-driven Analysis of Code Optimizations
Yacine Hakimi, Riyadh Baghdadi
As the demand for computational power grows, optimizing code through compilers becomes increasingly crucial. In this context, we focus on fully automatic code optimization techniqu…
LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
Massinissa Merouani, Afif Boudaoud, Riyadh Baghdadi
The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets.…
Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Djamel Rassem Lamouri, Iheb Nassim Aouadj, Smail Kourta +1
Compilers are crucial in optimizing programs and accelerating their execution. However, optimizing programs automatically using compilers is not trivial. Recent work has attempted…
Data-efficient Performance Modeling via Pre-training
Chunting Liu, Riyadh Baghdadi
Performance models are essential for automatic code optimization, enabling compilers to predict the effects of code transformations on performance and guide search for optimal tran…