Auto-tuning for floating-point precision with Discrete Stochastic Arithmetic - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of computational science Année : 2019

Auto-tuning for floating-point precision with Discrete Stochastic Arithmetic

Résumé

The type length chosen for floating-point numbers (e.g. 32 bits or 64 bits) may have an impact on the execution time, especially on SIMD (Single Instruction Multiple Data) units. Furthermore optimizing the types used in a numerical simulation causes a reduction of the data volume that is possibly transferred. In this paper we present PROMISE, a tool that makes it possible to optimize the numerical types in a program by taking into account the requested accuracy on the computed results. With PROMISE the numerical quality of results is verified using DSA (Discrete Stochastic Arithmetic) that enables one to estimate round-off errors. The search for a suitable type configuration is performed with a reasonable complexity thanks to the delta debugging algorithm. The PROMISE tool has been successfully tested on programs implementing several numerical algorithms including linear system solving and also on an industrial code that solves the neutron transport equations.
Fichier principal
Vignette du fichier
PROMISE.pdf (369.25 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01331917 , version 1 (14-06-2016)

Identifiants

Citer

Stef Graillat, Fabienne Jézéquel, Romain Picot, François Févotte, Bruno Lathuilière. Auto-tuning for floating-point precision with Discrete Stochastic Arithmetic. Journal of computational science, 2019, 36, pp.101017. ⟨10.1016/j.jocs.2019.07.004⟩. ⟨hal-01331917⟩
845 Consultations
689 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More