Pseudo-Random Number Generation on GP-GPU

International audienceRandom number generation is a key element of stochastic simulations. It has been widely studied for sequential applications purposes, enabling us to reliably use pseudo-random numbers in this case. Unfortunately, we cannot be so enthusiastic when dealing with parallel stochastic simulations. Many applications still neglect random stream parallelization, leading to potentially biased results. Particular parallel execution platforms, such as Graphics Processing Units (GPUs), add their constraints to those of Pseudo-Random Number Generators (PRNGs) used in parallel. It results in a situation where potential biases can be combined to performance drops when parallelization of random streams has not been carried out rigorously. Here, we propose criteria guiding the design of good GPU-enabled PRNGs. We enhance our comments with a study of the techniques aiming to correctly parallelize random streams, in the context of GPU-enabled stochastic simulations

Novel pseudo random number generation using variant logic framework

Cyber Security requires cryptology for the basic protection. Among different ECRYPT technologies, stream cipher plays a central role in advanced network security applications; in addition, pseudo-random number generators are placed in the core position of the mechanism. In this paper, a novel method of pseudo-random number generation is proposed to take advantage of the large functional space described using variant logic, a new framework for binary logic. Using permutation and complementary operations on classical truth table to form relevant variant table, numbers can be selected from table entries having pseudo-random properties. A simple generation mechanism is described and shown and pseudo-random sequences are analyzed for their cycle property and complexity. Applying this novel method, it can play a useful role in future applications for higher performance of cyber security environments

Analysis of pseudo-random number generation methods

The information about the analysis of pseudo-random number generation methods is provided. The qualitative characteristics of the corresponding algorithms are presented in this paper

Pseudo-Random Number Generation In R For Commonly Used Multivariate Distributions

An increasing number of practitioners and applied statisticians have started using the R programming system in recent years for their computing and data analysis needs. As far as pseudo-random number generation is concerned, the built-in generator in R does not contain multivariate distributions. In this article, R routines for widely used multivariate distributions are presented

JMASM16: Pseudo-Random Number Generation In R For Some Univariate Distributions

An increasing number of practitioners and applied researchers started using the R programming system in recent years for their computing and data analysis needs. As far as pseudo-random number generation is concerned, the built-in generator in R does not contain some important univariate distributions. In this article, complementary R routines that could potentially be useful for simulation and computation purposes are provided

PSEUDO RANDOM NUMBER GENERATION USING EYE BRIGHTNESS RESPONSE

Random numbers play an important and primary role in the use of Cryptography techniques in real time applications. The cryptographic techniques can be easily compromised if the key can be easily guessed. Therefore it is important that the keys are in random and unpredictable in nature. The operating system uses the random numbers to mask passwords and to offer salt and session identifiers. This paper introduces a new software based pseudo random number generation method based on the eye brightness response formula. This function provides a significant change in sensation for minimum required change in signal intensity. The randomness tests are performed to confirm the randomness of the generated random numbers