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The Cartographer
Communication-Efficient (Weighted) Reservoir Sampling from Fully Distributed Data Streams
October 24, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Lorenz HΓΌbschle-Schneider, Peter Sanders
arXiv ID
1910.11069
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC
Citations
5
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We consider communication-efficient weighted and unweighted (uniform) random sampling from distributed data streams presented as a sequence of mini-batches of items. This is a natural model for distributed streaming computation, and our goal is to showcase its usefulness. We present and analyze fully distributed, communication-efficient algorithms for both versions of the problem. An experimental evaluation of weighted reservoir sampling on up to 256 nodes (5120 processors) shows good speedups, while theoretical analysis promises further scaling to much larger machines.
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