[Illinois] PHYS466 2013 Lecture 13: Random Number Generators

By David M. Ceperley

Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL

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Abstract


Bio

Professor Ceperley received his BS in physics from the University of Michigan in 1971 and his Ph.D. in physics from Cornell University in 1976. After one year at the University of Paris and a second postdoc at Rutgers University, he worked as a staff scientist at both Lawrence Berkeley and Lawrence Livermore National Laboratories. In 1987, he joined the Department of Physics at Illinois. Professor Ceperley is a staff scientist at the National Center for Supercomputing Applications at Illinois.

Professor Ceperley's work can be broadly classified into technical contributions to quantum Monte Carlo methods and contributions to our physical or formal understanding of quantum many-body systems. His most important contribution is his calculation of the energy of the electron gas, providing basic input for most numerical calculations of electronic structure. He was one of the pioneers in the development and application of path integral Monte Carlo methods for quantum systems at finite temperature, such as superfluid helium and hydrogen under extreme conditions.

Professor Ceperley is a Fellow of the American Physical Society and a member of the American Academy of Arts and Sciences. He was elected to the National Academy of Sciences in 2006.

Cite this work

Researchers should cite this work as follows:

  • David M. Ceperley (2013), "[Illinois] PHYS466 2013 Lecture 13: Random Number Generators," https://nanohub.org/resources/16942.

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Location

University of Illinois at Urbana-Champaign, Urbana, IL

Submitter

NanoBio Node, Obaid Sarvana, Mor Gueye, George Michael Daley

University of Illinois at Urbana-Champaign

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[Illinois] PHYS 466 Lecture 13: Random Number Generators
  • Random Number Generation (RNG) 1. Random Number Generation (RNG) 0
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  • Random numbers on a computer 2. Random numbers on a computer 424.09548470626311
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  • Intel's new RNG: Ivy Bridge 3. Intel's new RNG: Ivy Bridge 723.60284835733944
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  • Desired Properties of RNG on a computer 4. Desired Properties of RNG on a… 1404.3126719533905
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  • Pseudo Random Sequence 5. Pseudo Random Sequence 1571.1739763715811
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  • Period or Cycle Length (L) 6. Period or Cycle Length (L) 1956.9632626638615
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  • Common PRNG Generators 7. Common PRNG Generators 1970.4758051464639
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  • Uniformity 8. Uniformity 2317.5869881857907
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  • Example of LCG: linear congruent generator 9. Example of LCG: linear congrue… 2321.8019096941252
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  • Example of LCG: In+1 = a In + c mod(m) 10. Example of LCG: In+1 = a In + … 2322.173814533096
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  • For LCG(5,1,16,0): In+1 = 5 In + 1 mod(16) 11. For LCG(5,1,16,0): In+1 = 5 In… 2322.2977828127528
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  • Sequential RNG Problems 12. Sequential RNG Problems 2323.1655607703515
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  • LCG Numbers fall in planes. 13. LCG Numbers fall in planes. 2500.9360737983493
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  • SPRNG: originally a NCSA project 14. SPRNG: originally a NCSA proje… 2651.3095970221721
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  • What is a chisquare test? 15. What is a chisquare test? 2707.7151642660624
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  • Chi-squared test of randomness 16. Chi-squared test of randomness 2918.7091762421105
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  • See 17. See "Numerical Recipes" on res… 2980.1974429519341
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