Efficient sampling of random permutations

  • Authors:
  • Jens Gustedt

  • Affiliations:
  • INRIA Lorraine and LORIA, France

  • Venue:
  • Journal of Discrete Algorithms
  • Year:
  • 2008

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Abstract

We show how to uniformly distribute data at random (not to be confounded with permutation routing) in two settings that are able to deal with massive data: coarse grained parallelism and external memory. In contrast to previously known work for parallel setups, our method is able to fulfill the three criteria of uniformity, work-optimality and balance among the processors simultaneously. To guarantee the uniformity we investigate the matrix of communication requests between the processors. We show that its distribution is a generalization of the multivariate hypergeometric distribution and we give algorithms to sample it efficiently in the two settings.