Sprng: A Scalable Library for Pseudorandom Number Generation

Mascagni, Michael

doi:10.1142/9789814291071_0027

Cited by 78 publications

(105 citation statements)

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“…It is essential to note that to compute the result to the statistical accuracy of 5.4×10 −5 it took only 30 seconds on an ordinary desktop computer. The same computations performed on a k-node cluster with k independent streams of pseudorandom numbers (e. g. from the SPRNG library [25]) would decrease the computational time by factor 1/k. Also we found out that the ergodic walk on boundary algorithm is the most efficient Monte Carlo method for this problem.…”

Section: Computational Results For the Unit Cubementioning

confidence: 99%

The random walk on the boundary method for calculating capacitance

Mascagni

Simonov

2004

Journal of Computational Physics

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Section: Computational Results For the Unit Cubementioning

confidence: 99%

The random walk on the boundary method for calculating capacitance

Mascagni

Simonov

2004

Journal of Computational Physics

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“…The authors of MT19937 (Matsumoto & Nishimura, 2000), and a research team at Florida State University (Mascagni et al, 2000), have proposed schemes that would dynamically "spawn" new generators and their streams are be kept separate because each new generator is controlled by a unique set of parameters. The intuition for this approach is very appealing.…”

Section: Where Do Random Numbers Come From?mentioning

confidence: 99%

Monte Carlo Analysis in Academic Research

Johnson

2013

Oxford Handbooks Online

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Monte Carlo analysis is a research strategy that incorporates randomness into the design, implementation or evaluation of theoretical models. It began in the 1940s, when the development of computer hardware and mathematical models made it possible to generate streams of random numbers. These random number streams are combined with mathematical models in order to create models and evaluate theories of random processes. This chapter attempts to tame this diverse, unmanageable collection of concepts and methods by dividing simulation projects into three types. The first, commonly called "Monte Carlo simulation," is used to evaluate statistical estimators. When an estimation procedure is proposed, it is standard procedure to test it against a variety of simulated research problems. A second type of project, referred to as "Markov chain Monte Carlo" (or MCMC), helps researchers to draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third 1 type of project arises in the study of complex systems, which are characterized by a large number of loosely interconnected, autonomous elements. Commonly known as agent-based models, these simulations have found enthusiastic advocates in environmental and social sciences.Keywords: Monte Carlo, Markov chain Monte Carlo (MCMC), pseudo random number generation (PRNG), Bayesian statistics, agent-based modeling Monte Carlo (MC) analysis is a general term that refers to research that employs random numbers, usually in the form of a computer model (or simulation). Although this research began in the natural sciences, computer science, and mathematics, it is now widely applied in social science as well. This essay attempts to explain the fundamental ideas that spurred the creation of these new procedures as well as their eventual adaptation for use in social science research.This chapter is not a "how to" guide for simulation, but rather it is a "what for" or "why you might want to" guide. Some of the difficulties that arise in MC research projects are considered as well. It begins with some background information on the development of computers and algorithms for random numbers. After that, the chapter takes up applications in the evaluation of proposed statistical estimators, the practice of Bayesian statistics via computer simulation, and investigation of complex systems through agent-based models. Some conclusions about the challenges that face the field are presented, along with a conclusion. A significant part of the presentation is about the exciting developments that have occurred since 1990. Rapid improvements in hardware and software have opened up opportunities for scholars to work with models that were previously prohibited by conceptual and technical barriers. At the current time, we are able to conceptualize and implement models that were simply impossible just 10 years ago. The extremely rapid progress has been driven by a fruitful interaction of substantive researchers in the natural and social science...

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“…For example, random seeds for each process should all be different values, at least. SPRNG (Mascagni, 1999) is a useful random number generator for parallel programming. It allows for the dynamic creation of independent random number streams on parallel machines without interprocessor communication.…”

Section: Parallel Applications In Statistical Computingmentioning

confidence: 99%

Parallel Computing Techniques

Nakano

2011

Handbook of Computational Statistics

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Sprng: A Scalable Library for Pseudorandom Number Generation

Cited by 78 publications

References 14 publications

The random walk on the boundary method for calculating capacitance

The random walk on the boundary method for calculating capacitance

Monte Carlo Analysis in Academic Research

Parallel Computing Techniques

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