Gramacki, Artur
In this paper we investigate the possibility of utilizing Graphics Processing Units (GPUs) to accelerate the finding of the bandwidth. The contribution of this paper is threefold: (a) we propose algorithmic optimization to one of bandwidth finding algorithms, (b) we propose efficient GPU versions of three bandwidth finding algorithms and (c) we experimentally compare three of our GPU implementations with the ones which utilize only CPUs. Our experiments show orders of magnitude improvements over CPU implementations of classical algorithms.
Graphics processing units in acceleration of bandwidth selection for kernel density estimation
nonparametric estimation
The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a very serious drawback of using KDEs is the large number of calculations required to compute them, especially to find the optimal bandwidth parameter.
bandwidth selection
Uciński, Dariusz - red.
Biblioteka Uniwersytetu Zielonogórskiego
artykuł
AMCS, volume 23, number 4 (2013)
2013
Zielona Góra: Uniwersytet Zielonogórski
kernel estimation
Korbicz, Józef - red.
probability density function
graphics processing unit
https://www.amcs.uz.zgora.pl/?action=papers&issue=55
2013
Andrzejewski, Witold
Gramacki, Jarosław
eng