Parallel calculation of the median and order statistics on GPUs with application to robust regression

Details Parallel calculation of the median and order statistics on GPUs with application to robust regression Parallel calculation of the median and order statistics on GPUs with application to robust regression

2011-04-14

arxiv.org/abs/1104.2732v1

Computer Science

Distributed, Parallel, and Cluster Computing

Scientific paper

We present and compare various approaches to a classical selection problem on Graphics Processing Units (GPUs). The selection problem consists in selecting the $k$-th smallest element from an array of size $n$, called $k$-th order statistic. We focus on calculating the median of a sample, the $n/2$-th order statistic. We introduce a new method based on minimization of a convex function, and show its numerical superiority when calculating the order statistics of very large arrays on GPUs. We outline an application of this approach to efficient estimation of model parameters in high breakdown robust regression.

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