## Random Number Generators for Parallel Applications (1998)

Venue: | in Monte Carlo Methods in Chemical Physics |

Citations: | 17 - 7 self |

### BibTeX

@INPROCEEDINGS{Srinivasan98randomnumber,

author = {Ashok Srinivasan and David M. Ceperley and Michael Mascagni},

title = {Random Number Generators for Parallel Applications},

booktitle = {in Monte Carlo Methods in Chemical Physics},

year = {1998},

pages = {13--36},

publisher = {John Wiley and Sons}

}

### OpenURL

### Abstract

this article is devoted, because these com1 putations require the highest quality of random numbers. The ability to do a multidimensional integral relies on properties of uniformity of n-tuples of random numbers and/or the equivalent property that random numbers be uncorrelated. The quality aspect in the other uses is normally less important simply because the models are usually not all that precisely specified. The largest uncertainties are typically due more to approximations arising in the formulation of the model than those caused by lack of randomness in the random number generator. In contrast, the first class of applications can require very precise solutions. Increasingly, computers are being used to solve very well-defined but hard mathematical problems. For example, as Dirac [1] observed in 1929, the physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are completely known and it is only necessary to find precise methods for solving the equations for complex systems. In the intervening years fast computers and new computational methods have come into existence. In quantum chemistry, physical properties must be calculated to "chemical accuracy" (say 0.001 Rydbergs) to be relevant to physical properties. This often requires a relative accuracy of 10