## Random Number Generators for Parallel Computers (1997)

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Venue: | The NHSE Review |

Citations: | 29 - 1 self |

### BibTeX

@ARTICLE{Coddington97randomnumber,

author = {Paul Coddington},

title = {Random Number Generators for Parallel Computers},

journal = {The NHSE Review},

year = {1997},

volume = {2}

}

### OpenURL

### Abstract

Random number generators are used in many applications, from slot machines to simulations of nuclear reactors. For many computational science applications, such as Monte Carlo simulation, it is crucial that the generators have good randomness properties. This is particularly true for large-scale simulations done on high-performance parallel computers. Good random number generators are hard to find, and many widely-used techniques have been shown to be inadequate. Finding high-quality, efficient algorithms for random number generation on parallel computers is even more difficult. Here we present a review of the most commonly-used random number generators for parallel computers, and evaluate each generator based on theoretical knowledge and empirical tests. In conclusion, we provide recommendations for using random number generators on parallel computers. Outline This review is organized as follows: A brief summary of the findings of this review is first presented, giving an overview of the use of parallel random number generators and a list of recommended algorithms. Section 1 is an introduction to random number generators and their use in computer simulations on parallel computers. Section 2 is a summary of the methods used to test and evaluate random number generators, on both sequential and parallel computers. Section 3 gives an overview of the main algorithms used to implement random number generators on sequential computers, provides examples of software implementations of the algorithms, and states any known problems with the algorithms or implementations. Section 4 gives a description of the most common methods used to parallelize the sequential algorithms, provides examples of software implementing these algorithms, and states any known problems ...

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Citation Context ...me modulus. 4.2 Sequence Splitting Another method for parallelizing random number generators is to split the sequence into non-overlapping contiguous sections, each generated by a different processor =-=[4, 6, 7]-=-. For example, one could divide the period of the generator by the number of processors, and jump ahead in the sequence by this amount for each processor. Alternatively, the length of each section of ... |

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Citation Context ...39] are generally used in a form where they can be considered as a special case of a lagged Fibonacci generator using XOR. XOR gives by far the worst randomness properties of any operation for an LFG =-=[1, 2, 15]-=-, so these generators are not recommended. Despite their serious drawbacks, shift register generators have been very popular in the past, mainly because they were comparatively fast. However on modern... |

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Citation Context ...f poor quality. Even generators that perform well in standard statistical tests for randomness have sometimes proven to be unreliable for certain applications, particularly in Monte Carlo simulations =-=[8, 9, 10, 11, 12, 13, 14, 15, 16, 17]-=-. The many problems caused in the past by inadequate random number generators on sequential and vector computers are likely to be repeated in a new generation of simulations using parallel computers, ... |

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Citation Context ...t c by new values A = a N and C = c(a N \Gamma 1)=(a \Gamma 1) (both modulo M) [47, 48, 49, 50]. Jumping ahead in the sequence can also be done for combined LCGs [6, 51] and shift-register generators =-=[52]-=-, but is not practical for LFGs using addition or multiplication, since the computations are much more complex, making it too slow for practical use. A 48-bit LCG using the leapfrog technique has been... |

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