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

Sylvain Contassot-Vivier (1) , Jean-François Couchot (2) , Mohammed Bakiri (2) , Pierre-Cyrille Heam (2)

1 SIMBIOT - SIMulating and Building IOT
2 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)

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 initialization, 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.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Cryptographie et sécurité [cs.CR]; Technologies Émergeantes [cs.ET]; Génie logiciel [cs.SE]; Informatique ubiquitaire; Système multi-agents [cs.MA]; Modélisation et simulation

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. International Conference on High Performance Computing & Simulation, Jul 2019, Dublin, Ireland. ⟨hal-03221902⟩