On Cache Limits for Dataflow Applications and Related Efficient Memory Management Strategies - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

On Cache Limits for Dataflow Applications and Related Efficient Memory Management Strategies

Résumé

The dataflow paradigm frees the designer to focus on the functionality of an application, independently from the underlying architecture executing it. While mapping the dataflow computational part to the cores seems obvious, the memory aspects do not match accordingly. Dataflow compilers usually do not consider the presence of caches when generating code. A generally accepted idea is that bigger and multi-level caches improve the performance of applications. Unfortunately, state-of-the-art dataflow compilers may prove the exception to this rule. This paper presents two efficient memory management strategies for dataflow applications through a study on the impact of sharing, size, and the number of levels of caches on them. The results show that bigger is not always better, and the foreseen future of more cores and bigger caches do not guarantee software-free better performance for dataflow applications. We propose two strategies, that can be used concurrently, to address the memory aspects of the dataflow model: copy-onwrite and non-temporal memory transfers. Experimental results show that we speed up a computer stereo vision application by 2.1× and reduce the number of L1 data cache misses by 45% while maintaining the actors' source code and design intact.
Fichier principal
Vignette du fichier
dasip21AuthorVersion.pdf (1.11 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03125551 , version 1 (29-01-2021)

Identifiants

Citer

Alemeh Ghasemi, Rodrigo Cataldo, Jean-Philippe Diguet, Kevin Martin. On Cache Limits for Dataflow Applications and Related Efficient Memory Management Strategies. DASIP 2021: Workshop on Design and Architectures for Signal and Image Processing, Jan 2021, Budapest -Online, Hungary. ⟨10.1145/3441110.3441573⟩. ⟨hal-03125551⟩
105 Consultations
157 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More