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Engineering Shared-Memory Parallel Shuffling to Generate Random Permutations In-Place

Author Manuel Penschuck

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Subject Classification

ACM Subject Classification
  • Theory of computation → Shared memory algorithms
Keywords
  • Shuffling
  • random permutation
  • parallelism
  • in-place
  • algorithm engineering
  • practical implementation

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Abstract

Shuffling is the process of placing elements into a random order such that any permutation occurs with equal probability. It is an important building block in virtually all scientific areas. We engineer, - to the best of our knowledge - for the first time, a practically fast, parallel shuffling algorithm with O(√n log n) parallel depth that requires only poly-logarithmic auxiliary memory (with high probability). In an empirical evaluation, we compare our implementations with a number of existing solutions on various computer architectures. Our algorithms consistently achieve the highest through-put on all machines. Further, we demonstrate that the runtime of our parallel algorithm is comparable to the time that other algorithms may take to acquire the memory from the operating system to copy the input.

Cite As Get BibTex

Manuel Penschuck. Engineering Shared-Memory Parallel Shuffling to Generate Random Permutations In-Place. In 21st International Symposium on Experimental Algorithms (SEA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 265, pp. 5:1-5:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.SEA.2023.5

Author Details

Manuel Penschuck
  • Goethe Universität Frankfurt, Germany

Funding

  • Penschuck, Manuel: Supported by the Deutsche Forschungsgemeinschaft (DFG) under grant ME 2088/5-1 (FOR 2975 - Algorithms, Dynamics, and Information Flow in Networks).

Supplementary Materials

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