A Fast, High Quality, and Reproducible Parallel Lagged-Fibonacci Pseudorandom Number Generator

Mascagni, Michael; Cuccaro, Steven A.; Pryor, Daniel V.; Robinson, M. L.

doi:10.1006/jcph.1995.1130

Cited by 37 publications

(22 citation statements)

References 8 publications

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“…These methods rely on the capability of certain generators to produce different full-period streams, that is, non-overlapping sequences, given different, carefully chosen, seeds. In this way, processors concurrently generate uncorrelated streams, thus providing scalable procedures (Mascagni et al 1995;Mascagni and Srinivasan 2000). Different pseudo-random numbers generators are well-known in literature.…”

Section: Fig 3 Parallel Monte Carlo Algorithm For the Valuation Of Pmentioning

confidence: 98%

Participating life insurance policies: an accurate and efficient parallel software for COTS clusters

et al. 2009

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In this paper we discuss the development of a parallel software for the numerical simulation of Participating Life Insurance Policies in distributed environments. The main computational kernels in the mathematical models for the solution of the problem are multidimensional integrals and stochastic differential equations. The former is solved by means of Monte Carlo method combined with the Antithetic Variates variance reduction technique, while differential equations are approximated via a fully implicit, positivity-preserving, Euler method. The parallelization strategy we adopted relies on the parallelization of Monte Carlo algorithm. We implemented and tested the software on a PC Linux cluster

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Section: Fig 3 Parallel Monte Carlo Algorithm For the Valuation Of Pmentioning

confidence: 98%

Participating life insurance policies: an accurate and efficient parallel software for COTS clusters

et al. 2009

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“…An important property of the ALFG is that the maximal period is ͑2 k Ϫ 1͒2 mϪ1 . This occurs for very specific circumstances [Brent 1994;Marsaglia and Tsay 1985], from which one can infer that this generator has 2 ͑kϪ1͒ϫ͑mϪ1͒ different full-period cycles [Mascagni et al 1995a]. This means that the state space of the ALFG is toroidal, with Eq.…”

Section: Lagged-fibonacci Generatorsmentioning

confidence: 99%

“…Each unique assignment gives a seed in a provably distinct full-period cycle [Mascagni et al 1995a]. …”

Section: Lagged-fibonacci Generatorsmentioning

confidence: 99%

Algorithm 806: SPRNG

Mascagni

Srinivasan

2000

ACM Trans. Math. Softw.

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207

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In this article we present background, rationale, and a description of the Scalable Parallel Random Number Generators (SPRNG) library. We begin by presenting some methods for parallel pseudorandom number generation. We will focus on methods based on parameterization, meaning that we will not consider splitting methods such as the leap-frog or blocking methods. We describe, in detail, parameterized versions of the following pseudorandom number generators: (i) linear congruential generators, (ii) shift-register generators, and (iii) lagged-Fibonacci generators. We briefly describe the methods, detail some advantages and disadvantages of each method, and recount results from number theory that impact our understanding of their quality in parallel applications. SPRNG was designed around the uniform implementation of different families of parameterized random number generators. We then present a short description of SPRNG. The description contained within this document is meant only to outline the rationale behind and the capabilities of SPRNG. Much more information, including examples and detailed documentation aimed at helping users with putting and using SPRNG on scalable systems is available at http://sprng.cs.fsu.edu. In this description of SPRNG we discuss the random-number generator library as well as the suite of tests of randomness that is an integral part of SPRNG. Random-number tools for parallel Monte Carlo applications must be subjected to classical as well as new types of empirical tests of randomness to eliminate generators that show defects when used in scalable environments.The SPRNG software was developed with funding from DARPA Contract Number DABT63-95-C-0123 for ITO: Scalable Systems and Software, entitled A Scalable Pseudorandom Number

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“…(2 1) k − states. But in case of IRR, this is not the case; that means a primitive polynomial of degree k over the ring 2 e  can generate ( 1) ( 1) 2 e k − − shift distinct sequences as given in references 6,8 . This paper focuses on the number of polynomials which generate sequences of maximum period with respect to Brent's 4 condition and corresponding number of different sequences of maximum period for a k -stage IRR.…”

Section: Introductionmentioning

confidence: 99%