A Hardware Efficient Random Number Generator for Nonuniform Distributions with Arbitrary Precision

Schryver, Christian De; Schmidt, Daniel; Wehn, Norbert; Korn, Elke; Marxen, Henning; Kostiuk, Anton; Korn, Ralf

doi:10.1155/2012/675130

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…Several methods for generating random number have been proposed indecent years. Most of the methods were implemented in hardware rather than software (De Schryver et al, 2012;VishnuRaj & Yuvaraj, 2014). These methods provide a maximum periodic of random sequence and high throughput rate while adhering to established statistical standards by applying a seeding mechanism.…”

Section: Related Workmentioning

confidence: 99%

Implementation of the Binary Random Number Generator Using the Knight Tour Problem

Mahmood

Rahim²,

Othman³

2016

MAS

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<p>A random number can be defined as a set of numbers produced by a numerical function, in which the next number is unpredictable and a relationship between successive occurrences is lacking. Moreover, these sequences cannot be reproduced unless the same generator function with an exact initial value is used. The design of a random number generator must overcome the previous problems of a low periodic and the capacity to reproduce the same sequence. This paper proposes the knight tour as a tool for generating pseudo random numbers. These random numbers can be use in the encryption process or in a password generator for network administrators. The randomness test suite is used to ensure the randomness of outcome sequences. Roughly, 75% of the test results obtained is better than the results from other works. The statistical properties and security analysis indicate that the knight tour application is highly successful in generating a pseudo random number with good statistical results, high linear complexity and strong capacity to withstand attacks.</p>

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Section: Related Workmentioning

confidence: 99%

Implementation of the Binary Random Number Generator Using the Knight Tour Problem

Mahmood

Rahim²,

Othman³

2016

MAS

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“…The only drawback in our case is the cost of the mathematical operations involved; namely: sin, cos, log, sqrt, which are computationally intensive in all of the targeted platforms: CPU, XeonPhi, GPU, and FPGA. Other possibilities, such as the inverse cumulative distribution function (ICDF), have proven to be more resource-and energy-efficient [46,47] in specific architectures. However, we are interested in a portable project that we can deploy to fairly compare all targeted platforms.…”

Section: Local Random Number Generationmentioning

confidence: 99%

Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency

Desmettre

Korn

Varela

et al. 2016

Risks

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Risk analysis and management currently have a strong presence in financial institutions, where high performance and energy efficiency are key requirements for acceleration systems, especially when it comes to intraday analysis. In this regard, we approach the estimation of the widely-employed portfolio risk metrics value-at-risk (VaR) and conditional value-at-risk (cVaR) by means of nested Monte Carlo (MC) simulations. We do so by combining theory and software/hardware implementation. This allows us for the first time to investigate their performance on heterogeneous compute systems and across different compute platforms, namely central processing unit (CPU), many integrated core (MIC) architecture XeonPhi, graphics processing unit (GPU), and field-programmable gate array (FPGA). To this end, the OpenCL framework is employed to generate portable code, and the size of the simulations is scaled in order to evaluate variations in performance. Furthermore, we assess different parallelization schemes, and the targeted platforms are evaluated and compared in terms of runtime and energy efficiency. Our implementation also allowed us to derive a new algorithmic optimization regarding the generation of the required random number sequences. Moreover, we provide specific guidelines on how to properly handle these sequences in portable code, and on how to efficiently implement nested MC-based VaR and cVaR simulations on heterogeneous compute systems.

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“…Fortunately, NVIDIA provides random generators in versions posterior to CUDA 3.2. In this work, the chosen random number generator is the Mersenne Twister method [26][27][28][29] available in the CUDA Software Development Kit [30]. This generator is invoked in the function RandomGPU, which generates random vector proportional to population size.…”

Section: Inputmentioning

confidence: 99%

CUDA parallel programming for simulation of epidemiological models based on individuals

Filho

Paula

Coelho

et al. 2015

Math. Meth. Appl. Sci.

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Mathematical models are of great value in epidemiology to help understand the dynamics of the various infectious diseases, as well as in the conception of effective control strategies. The classical approach is to use differential equations to describe, in a quantitative manner, the spread of diseases within a particular population. An alternative approach is to represent each individual in the population as a string or vector of characteristic data and simulate the contagion and recovery processes by computational means. This type of model, referred in the literature as MBI (models based on individuals), has the advantage of being flexible as the characteristics of each individual can be quite complex, involving, for instance, age, sex, pre‐existing health conditions, environmental factors, social habits, etc. However, when it comes to simulations involving large populations, MBI may require a large computational effort in terms of memory storage and processing time. In order to cope with the problem of heavy computational effort, this paper proposes a parallel implementation of MBI using a graphics processor unit compatible with CUDA. It was found that, even in the case of a simple susceptible–infected–recovered model, the computational gains in terms of processing time are significant. Copyright © 2015 John Wiley & Sons, Ltd.

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A Hardware Efficient Random Number Generator for Nonuniform Distributions with Arbitrary Precision

Cited by 15 publications

References 17 publications

Implementation of the Binary Random Number Generator Using the Knight Tour Problem

Implementation of the Binary Random Number Generator Using the Knight Tour Problem

Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency

CUDA parallel programming for simulation of epidemiological models based on individuals

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