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arXiv · 1610.05141

Efficient Random Sampling -- Parallel, Vectorized, Cache-Efficient, and Online

Peter Sanders·Sebastian Lamm·Lorenz Hübschle-Schneider·Emanuel Schrade·Carsten Dachsbacher
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Abstract

We consider the problem of sampling $n$ numbers from the range $\{1,\ldots,N\}$ without replacement on modern architectures. The main result is a simple divide-and-conquer scheme that makes sequential algorithms more cache efficient and leads to a parallel algorithm running in expected time $\mathcal{O}(n/p+\log p)$ on $p$ processors, i.e., scales to massively parallel machines even for moderate values of $n$. The amount of communication between the processors is very small (at most $\mathcal{O}(\log p)$) and independent of the sample size. We also discuss modifications needed for load balancing, online sampling, sampling with replacement, Bernoulli sampling, and vectorization on SIMD units or GPUs.

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Peter Sanders, Sebastian Lamm, Lorenz Hübschle-Schneider, Emanuel Schrade, Carsten Dachsbacher. 2016-10-17. Efficient Random Sampling -- Parallel, Vectorized, Cache-Efficient, and Online. https://doi.org/10.1145/3157734

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