The PLFG parallel pseudo-random number generator

Tan, C. J. Kenneth

doi:10.1016/s0167-739x(02)00034-1

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Parallel random bit generation methods could dramatically enhance the generation rate and scalability of RNG by producing random bits simultaneously from multiple paths. Parallel pseudorandom number generation algorithms have been developed [6,7] and well applied in the field of mathematical modelling. Parallel hardware pseudorandom number generation methods based on cellular automata have been demonstrated [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Parallel real-time quantum random number generator

Guo

Cheng

et al. 2019

Opt. Lett.

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Quantum random number generation exploits inherent randomness of quantum mechanical processes and measurements. Real-time generation rate of quantum random numbers is usually limited by electronic bandwidth and data processing rates. Here we use a multiplexing scheme to create a fast real-time quantum random number generator based on continuous variable vacuum fluctuations. Multiple sideband frequency modes of a quantum vacuum state within a homodyne detection bandwidth are concurrently extracted as the randomness source. Parallel post-processing of raw data from three sub-entropy sources is realized in one field-programmable gate array (FPGA) based on Toeplitz-hashing extractors. A cumulative generation rate of 8.25 Gbps in real-time is achieved. The system relies on optoelectronic components and circuits that could be integrated in a compact, economical package. c 2019

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Section: Introductionmentioning

confidence: 99%

Parallel real-time quantum random number generator

Guo

Cheng

et al. 2019

Opt. Lett.

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“…Generating random numbers on the GPU suitable for use in Monte Carlo simulations is an ongoing research topic, see, for example, [34][35][36]. Implementing the random number generation in the GPU will not only reduce data transfer and allow for a standalone GPU implementation, an efficient parallel version will also improve the overall performance as the random number generation itself takes a considerable amount of time.…”

Section: Gpu Pf: Random Number Generationmentioning

confidence: 99%

Particle Filtering: The Need for Speed

Hendeby

Karlsson²,

Gustafsson

2010

EURASIP J. Adv. Signal Process.

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The particle filter (PF) has during the last decade been proposed for a wide range of localization and tracking applications. There is a general need in such embedded system to have a platform for efficient and scalable implementation of the PF. One such platform is the graphics processing unit (GPU), originally aimed to be used for fast rendering of graphics. To achieve this, GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper, GPGPU techniques are used to make a parallel recursive Bayesian estimation implementation using particle filters. The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to the one achieved with a traditional CPU implementation. The comparison is made using a minimal sensor network with bearings-only sensors. The resulting GPU filter, which is the first complete GPU implementation of a PF published to this date, is faster than the CPU filter when many particles are used, maintaining the same accuracy. The parallelization utilizes ideas that can be applicable for other applications.

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“…The Mersenne Twister random number generator, proposed by Matsumoto and Nishimura (1998) was implemented. This random number generator has the Mersenne prime period of 2 19937 À 1 and passes several tests for randomness, including the DIEHARD testing suite, generally considered the standard for random number generator testing (Srinivasan, Mascagni, & Ceperley, 2003;Tan, 2002).…”

Section: Exchange R I × N I Randomly Selected Particles Between S I Amentioning

confidence: 99%