Fast and robust PRNGs based on jumps in N-cubes for simulation, but not exclusively for that. - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Fast and robust PRNGs based on jumps in N-cubes for simulation, but not exclusively for that.

Résumé

Pseudo-Random Number Generators (PRNG) are omnipresent in computer science: they are embedded in all approaches of numerical simulation (for exhaustiveness), optimization (to discover new solutions), testing (to detect bugs) cryptography (to generate keys), and deep learning (for initial-ization, to allow generalizations).. .. PRNGs can be basically divided in two main categories: fast ones, robust ones. The former have often statistical biases such as not being uniformly distributed in all dimensions, having a too short period of time,.. .. In the latter case, statistical quality is present but the generators are not fast. This is typically what is encountered when running a cryptographically secure PRNG. In this paper, we propose alternative architectures, based on jumps in N-cubes, that provide fast and robust PRNGs for efficient simulations, but not exclusively for that.
Fichier principal
Vignette du fichier
main.pdf (394.53 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02301248 , version 1 (30-09-2019)

Licence

Paternité - Pas de modifications

Identifiants

  • HAL Id : hal-02301248 , version 1

Citer

Sylvain Contassot-Vivier, Jean-François Couchot, Mohammed Bakiri, Pierre-Cyrille Heam. Fast and robust PRNGs based on jumps in N-cubes for simulation, but not exclusively for that.. The 2019 International Conference on High Performance Computing & Simulation, Jul 2019, Dublin, Ireland. ⟨hal-02301248⟩
117 Consultations
250 Téléchargements

Partager

Gmail Facebook X LinkedIn More