Analytical Cache Modeling and Tilesize Optimization for Tensor Contractions - CORSE - Computer Optimization and Run-time SystEms Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Analytical Cache Modeling and Tilesize Optimization for Tensor Contractions

Résumé

Data movement between processor and memory hierarchy is a fundamental bottleneck that limits the performance of many applications on modern computer architectures. Tiling and loop permutation are key techniques for improving data locality. However, selecting effective tile-sizes and loop permutations is particularly challenging for tensor contractions due to the large number of loops. Even state-of-the-art compilers usually produce sub-optimal tile-sizes and loop permutations, as they rely on naive cost models. In this paper we provide an analytical model based approach to multi-level tile size optimization and permutation selection for tensor contractions. Our experimental results show that this approach achieves comparable or better performance than state-of-theart frameworks and libraries for tensor contractions.
Fichier principal
Vignette du fichier
main-hal.pdf (443.44 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02418875 , version 1 (19-12-2019)

Identifiants

Citer

Rui Li, Aravind Sukumaran-Rajam, Richard Veras, Tze Meng Low, Fabrice Rastello, et al.. Analytical Cache Modeling and Tilesize Optimization for Tensor Contractions. SC 2019 - International Conference for High Performance Computing, Networking, Storage and Analysis, Nov 2019, Denver, United States. pp.1-13, ⟨10.1145/3295500.3356218⟩. ⟨hal-02418875⟩
193 Consultations
837 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More