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randomparallel-processingprngmersenne-twister

Pseudo-random number generator for cluster environment

How can I generate independent pseudo-random numbers on a cluster, for Monte Carlo simulation for example? I can have many compute nodes (e.g. 100), and I need to generate millions of numbers on each node. I need a warranty that a PRN sequence on one node will not overlap the PRN sequence on another node.

  • I could generate all PRN on root node, then send them to other nodes. But it would be far too slow.
  • I could jump to a know distance in the sequence, on each node. But is there such an algorithm for Mersenne-Twister or for any other good PRNG, which can be done with a reasonable amount of time and memory?
  • I could use different generators on each node. But is it possible with good generators like Mersenne-Twister? How could it be done?
  • Any other though?
Solution

  • You should never use potentially overlapping random streams obtained from the same original stream. If you have not tested the resulting interleaved stream, you have no idea of its statistic quality.

    Fortunately, Mersenne Twister (MT) will help you in your distribution task. Using its dedicated algorithm, called Dynamic Creator (DC hereafter), you can create independent random number generators that will produce highly independent random streams.

    Each stream will be created on the node that will be using it. Basically, think of DC as a constructor in object oriented paradigm that creates different instances of MT. Each different instance is designed to produce highly independent random sequences.

    You can find DC here: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/DC/dc.html
    It's quite straightforward to use and you'll be able to fix different parameters such as the number of different MT instances you want to obtain or the period of these MTs. Depending on its input parameter, DC will runtime will change.

    In addition of the README coming along with DC, take a look at the file example/new_example2.c in the DC archive. It shows example of calls to get independent sequences given a different input identifier, which is basically what you have to identify cluster jobs.

    Finally, if you intend to learn more about how to use PRNGs in parallel or distributed environments, I suggest you read this scientific articles:

    Practical distribution of random streams for stochastic High Performance Computing, David RC Hill, in International Conference on High Performance Computing and Simulation (HPCS), 2010