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Random Number Generation: A Practitioner's Overview

<!--HTML--><p align="justify"> We will look at random number generation from the point-of-view of Monte Carlo computations. Thus, we will examine several serial methods of pseudorandom number generation and two different parallelization techniques. Among the techniques discuss...

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Autor principal: Mascagni, Michael
Lenguaje:eng
Publicado: 2012
Materias:
Acceso en línea:http://cds.cern.ch/record/1477129
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author Mascagni, Michael
author_facet Mascagni, Michael
author_sort Mascagni, Michael
collection CERN
description <!--HTML--><p align="justify"> We will look at random number generation from the point-of-view of Monte Carlo computations. Thus, we will examine several serial methods of pseudorandom number generation and two different parallelization techniques. Among the techniques discussed with be &quot;parameterization,&quot; which forms the basis for the Scalable Parallel Random Number Generators (SPRNG) library. SPRNG was developed several years ago by the author, and has become widely used within the international Monte Carlo community. SPRNG is briefly described, and the lecture ends with a short revue of quasirandom number generation. Quasirandom numbers offer many Monte Carlo applications the advantage of superior convergence rates. We also discuss plans for a new version of SPRNG adapted to hybrid architectures.</p> <h4>About the speaker</h4> <p align="justify"> Dr. Mascagni is full professor at Florida State University, where he runs a research group consisting of post-doctoral associates, graduate students, and undergraduate workers. The areas they work on are parallel and distributed computing, Grid computing, random number generation, Monte Carlo methods, computational number theory and discrete algorithms, and applications to materials science, biochemistry, electrostatics, and finance. </p> <p align="justify"> He is on the editorial board of three journals in his field, and is a member of the ACM (Association of Computing Machinery), SIAM (Society of Applied Mathematics), and IMACS (International Association of Mathematics and Computers in Simulation). He is also a member of the Board of Directors of IMACS. He has approximately 100 refereed technical papers that have appeared in a wide variety of publications in areas including Applied Mathematics, Computer Science, Simulation Science, Monte Carlo Methods, Computational Science, High-Performance Computing, Scientific Computing, Computational Physics, and Computational Neuroscience. He has been a visiting professor at the University of Padova in Italy, the University of Salzburg in Austria, and the Swiss Federal Technical Institute-Zürich in Switzerland, and is a consultant to industry and government. He has made technical presentations in 18 countries and in most of the 50 U.S. states.</p>
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spelling cern-14771292022-11-02T22:30:08Zhttp://cds.cern.ch/record/1477129engMascagni, MichaelRandom Number Generation: A Practitioner's OverviewRandom Number Generation: A Practitioner's OverviewComputing Seminar<!--HTML--><p align="justify"> We will look at random number generation from the point-of-view of Monte Carlo computations. Thus, we will examine several serial methods of pseudorandom number generation and two different parallelization techniques. Among the techniques discussed with be &quot;parameterization,&quot; which forms the basis for the Scalable Parallel Random Number Generators (SPRNG) library. SPRNG was developed several years ago by the author, and has become widely used within the international Monte Carlo community. SPRNG is briefly described, and the lecture ends with a short revue of quasirandom number generation. Quasirandom numbers offer many Monte Carlo applications the advantage of superior convergence rates. We also discuss plans for a new version of SPRNG adapted to hybrid architectures.</p> <h4>About the speaker</h4> <p align="justify"> Dr. Mascagni is full professor at Florida State University, where he runs a research group consisting of post-doctoral associates, graduate students, and undergraduate workers. The areas they work on are parallel and distributed computing, Grid computing, random number generation, Monte Carlo methods, computational number theory and discrete algorithms, and applications to materials science, biochemistry, electrostatics, and finance. </p> <p align="justify"> He is on the editorial board of three journals in his field, and is a member of the ACM (Association of Computing Machinery), SIAM (Society of Applied Mathematics), and IMACS (International Association of Mathematics and Computers in Simulation). He is also a member of the Board of Directors of IMACS. He has approximately 100 refereed technical papers that have appeared in a wide variety of publications in areas including Applied Mathematics, Computer Science, Simulation Science, Monte Carlo Methods, Computational Science, High-Performance Computing, Scientific Computing, Computational Physics, and Computational Neuroscience. He has been a visiting professor at the University of Padova in Italy, the University of Salzburg in Austria, and the Swiss Federal Technical Institute-Zürich in Switzerland, and is a consultant to industry and government. He has made technical presentations in 18 countries and in most of the 50 U.S. states.</p> oai:cds.cern.ch:14771292012
spellingShingle Computing Seminar
Mascagni, Michael
Random Number Generation: A Practitioner's Overview
title Random Number Generation: A Practitioner's Overview
title_full Random Number Generation: A Practitioner's Overview
title_fullStr Random Number Generation: A Practitioner's Overview
title_full_unstemmed Random Number Generation: A Practitioner's Overview
title_short Random Number Generation: A Practitioner's Overview
title_sort random number generation: a practitioner's overview
topic Computing Seminar
url http://cds.cern.ch/record/1477129
work_keys_str_mv AT mascagnimichael randomnumbergenerationapractitionersoverview