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Gaussian random number generators
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ACM Computing Surveys (CSUR) archive
Volume 39 ,  Issue 4  (2007) table of contents
Article No. 11  
Year of Publication: 2007
ISSN:0360-0300
Authors
David B. Thomas  Imperial College
Wayne Luk  Imperial College
Philip H.W. Leong  The Chinese University of Hong Kong and Imperial College
John D. Villasenor  University of California, Los Angeles
Publisher
ACM  New York, NY, USA
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ABSTRACT

Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.


REFERENCES

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1
2
 
3
Andraka, R. and Phelps, R. 1998. An FPGA based processor yields a real time high fidelity radar environment simulator. In Military and Aerospace Applications of Programmable Devices and Technologies Conference.
4
 
5
 
6
Box, G. E. P. and Muller, M. E. 1958a. A note on the generation of random normal deviates. Annals Math. Stat. 29, 610--611.
 
7
Box, G. E. P. and Muller, M. E. 1958b. A note on the generation of random normal deviates. Annals Math. Stat. 29, 2, 610--611.
8
 
9
Brent, R. P. 1993. Fast normal random number generators on vector processors. Tech. Rep. TR-CS-93-04, Department of Computer Science, The Australian National University, Canberra 0200 ACT, Australia.
 
10
Brent, R. P. 1997. A fast vectorised implementation of Wallace's normal random number generator. Tech. Rep. TR-CS-97-07, Department of Computer Science, The Australian National University, Canberra 0200 ACT, Australia.
 
11
Brent, R. P. 2003. Some comments on C. S. Wallace's random number generators. Comput. J., to appear.
 
12
Chen, J., Moon, J., and Bazargan, K. 2004. Reconfigurable readback-signal generator based on a field-programmable gate array. IEEE Trans. Magn. 40, 3, 1744--1750.
 
13
 
14
Danger, J.-L., Ghazel, A., Boutillon, E., and Laamari, H. 2000. Efficient FPGA implementation of Gaussian noise generator for communication channel emulation. In ICECS'2000. IEEE, Jounieh, Lebanon.
 
15
Devroye, L. 1986. Non-Uniform Random Variate Generation. Springer-Verlag, http://cg.scs.carleton.ca/~luc/rnbookindex.html, New York.
 
16
Forsythe, G. E. 1972. Von Neumann's comparison method for random sampling from the normal and other distributions. Math. Computation 26, 120, 817--826.
 
17
Gebhardt, F. 1964. Generating normally distributed random numbers by inverting the normal distribution function. Math. Computation 18, 86, 302--306.
18
 
19
Kabal, P. 2000. Generating Gaussian pseudo-random deviates. Tech. Rep., Department of Electrical and Computer Engineering, McGill University.
20
21
 
22
Knuth, D. E. 1981. Seminumerical Algorithms, Second ed. The Art of Computer Programming, vol. 2. Addison-Wesley, Reading, Massachusetts.
23
24
 
25
 
26
 
27
L'Ecuyer, P. and Simard, R. 2005. TestU01. http://www.iro.umontreal.ca/~simardr/indexe.html.
 
28
 
29
Lehmer, D. H. 1949. Mathematical methods in large-scale computing units. In Proceedings of the 2nd Symposium on Large-Scale Digital Calculating Machinery. Harvard University Press, Cambridge, Massachusetts, 141--146.
30
31
 
32
Marsaglia, G. 1964. Generating a variable from the tail of the normal distribution. Technometrics 6, 101--102.
 
33
Marsaglia, G. 1997. The Diehard random number test suite. http://stat.fsu.edu/pub/diehard/.
 
34
Marsaglia, G. 2004. Evaluating the normal distribution. J. Statis. Softw. 11, 4, 1--11.
 
35
Marsaglia, G. and Bray, T. A. 1964. A convenient method for generating normal variables. SIAM Rev. 6, 3, 260--264.
36
 
37
Marsaglia, G. and Tsang, W. W. 1984a. A fast, easily implemented method for sampling from decreasing or symmetric unimodal density functions. SIAM J. Sci. Statis. Comput. 5, 349--359.
 
38
Marsaglia, G. and Tsang, W. W. 1984b. A fast, easily implemented method for sampling from decreasing or symmetric unimodal density functions. SIAM J. Sci. Stat. Comput. 5, 2 (June), 349--359.
39
 
40
Marsaglia, G. and Tsang, W. W. 2000. The ziggurat method for generating random variables. J. Statis. Softw. 5, 8, 1--7.
41
 
42
McCollum, J. M., Lancaster, J. M., Bouldin, D. W., and Peterson, G. D. 2003. Hardware acceleration of pseudo-random number generation for simulation applications. In IEEE Southeastern Symposium on System Theory. IEEE, Morgantown, WV.
 
43
Moler, C. 1995. Random thoughts: 10435 years is a very long time. http://www.mathworks.com/company/newsletters/news_notes/pdf/Cleve.pdf.
 
44
Moler, C. B. 2004. Numerical Computing in Matlab. Society for Industrial and Applied Mathematics, USA.
45
 
46
Muller, M. E. 1958. An inverse method for the generation of random normal deviates on large-scale computers. Mathematical Tables and Other Aids to Computation 12, 63, 167--174.
47
48
 
49
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. 1997. Numerical Recipes in C, Second ed. Cambridge University Press, Cambridge.
 
50
Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E., Leigh, S., Levenson, M., Vangel, M., Banks, D., Heckert, A., Dray, J., and Vo, S. 2001. A statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST special publication 800-22, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland See http://csrc.nist.gov/rng/.
51
 
52
Teichroew, D. 1953. Distribution sampling with high speed computers. (PhD thesis).
 
53
Thomas, D. B. and Luk, W. 2006. Non-uniform random number generation through piecewise linear approximations. In International Conference on Field Programmable Logic and Applications.
54
 
55
Wallace, C. 2005. Random number generators. Tech. rep., Monash University. See http://www.datamining.monash.edu.au/software/random/.
56
 
57
Wetherill, G. B. 1965. An approximation to the inverse normal function suitable for the generation of random normal deviates on electronic computers. Appl. Statis. 14, 2, 201--205.
 
58
Wichura, M. J. 1988. Algorithm AS 241: The percentage points of the normal distribution. Appl. Statis. 37, 3, 477--484.
 
59
Xilinx. 2002. Additive white Gaussian noise (AWGN) core. CoreGen documentation file.


Collaborative Colleagues:
David B. Thomas: colleagues
Wayne Luk: colleagues
Philip H.W. Leong: colleagues
John D. Villasenor: colleagues