Zobrazeno 1 - 3
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pro vyhledávání: '"Merouani, Massinissa"'
Autor:
Merouani, Massinissa, Boudaoud, Khaled Afif, Aouadj, Iheb Nassim, Tchoulak, Nassim, Bernou, Islem Kara, Benyamina, Hamza, Tayeb, Fatima Benbouzid-Si, Benatchba, Karima, Leather, Hugh, Baghdadi, Riyadh
While polyhedral compilers have shown success in implementing advanced code transformations, they still have challenges in selecting the most profitable transformations that lead to the best speedups. This has motivated the use of machine learning to
Externí odkaz:
http://arxiv.org/abs/2403.11522
Autor:
Merouani, Massinissa, Boudaoud, Khaled Afif, Aouadj, Iheb Nassim, Tchoulak, Nassim, Benbouzid-Sitayeb, Fatima, Benatchba, Karima, Leather, Hugh, Baghdadi, Riyadh
Publikováno v:
In Proceedings of 12th International Workshop on Polyhedral Compilation Techniques (IMPACT 2022)
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
Externí odkaz:
http://arxiv.org/abs/2206.03684
Autor:
Baghdadi, Riyadh, Merouani, Massinissa, Leghettas, Mohamed-Hicham, Abdous, Kamel, Arbaoui, Taha, Benatchba, Karima, Amarasinghe, Saman
Publikováno v:
Proceedings of the 4th MLSys Conference, San Jose, CA, USA, 2021
Enabling compilers to automatically optimize code has been a longstanding goal for the compiler community. Efficiently solving this problem requires using precise cost models. These models predict whether applying a sequence of code transformations r
Externí odkaz:
http://arxiv.org/abs/2104.04955