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Parallel Weighted Random Sampling

Authors Lorenz Hübschle-Schneider, Peter Sanders

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Lorenz Hübschle-Schneider
  • Karlsruhe Institute of Technology, Germany
Peter Sanders
  • Karlsruhe Institute of Technology, Germany

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Lorenz Hübschle-Schneider and Peter Sanders. Parallel Weighted Random Sampling. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 59:1-59:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ESA.2019.59

Abstract

Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
  • Theory of computation → Parallel algorithms
  • Theory of computation → Data structures design and analysis
Keywords
  • categorical distribution
  • multinoulli distribution
  • parallel algorithm
  • alias method
  • PRAM
  • communication efficient algorithm
  • subset sampling
  • reservoir sampling

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