Communication Dans Un Congrès Année : 2026

Guiding Polyhedral Scheduling for Vectorization through Constraints Generated from an SLP Algorithm

Résumé

Polyhedral schedulers present well established techniques to extract parallelism, improve data locality, and generate tiled code for statically analyzable loops. However, as the polyhedral model abstracts programs in a mathematical representation detached from language, architectural, and hardware specific constraints, encoding vectorization in an affine form can prove challenging. In this paper, we present an approach to integrate information on vectorization decisions made by an SLP algorithm (Autovesk) into a polyhedral compiler (Pluto) through the addition of constraints to the schedule. We execute the SLP vectorization algorithm preserving annotated statement instance information. From its output, we create a set of constraints aiming to enforce vectorization. Those optional constraints are injected during the scheduling process of the polyhedral compiler. We evaluate the performance and make use of hardware counters to check the relevancy of our method on the Polybench/C suite.

Fichier principal
Vignette du fichier
IMPACT_2026.pdf (534.54 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05466086 , version 1 (19-01-2026)

Licence

Identifiants

  • HAL Id : hal-05466086 , version 1

Citer

Tom Hammer, Stéphane Genaud, Vincent Loechner. Guiding Polyhedral Scheduling for Vectorization through Constraints Generated from an SLP Algorithm. IMPACT '26 - 16th International Workshop on Polyhedral Compilation Techniques, Jan 2026, Krakow, Poland. ⟨hal-05466086⟩
143 Consultations
139 Téléchargements

Partager

  • More