Randomized selection on the GPU

Authors:
Laura Monroe;Joanne Wendelberger;Sarah Michalak
Affiliations:
Los Alamos National Laboratory, Los Alamos, NM;Los Alamos National Laboratory, Los Alamos, NM;Los Alamos National Laboratory, Los Alamos, NM
Venue:
Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics
Year:
2011

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Abstract

We implement here a fast and memory-sparing probabilistic top k selection algorithm on the GPU. The algorithm proceeds via an iterative probabilistic guess-and-check process on pivots for a three-way partition. When the guess is correct, the problem is reduced to selection on a much smaller set. This probabilistic algorithm always gives a correct result and always terminates. Las Vegas algorithms of this kind are a form of stochastic optimization and can be well suited to more general parallel processors with limited amounts of fast memory.