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

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  • HAL Id : hal-02301248 , version 1

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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⟩
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