TY - GEN
A1 - Andrzejewski, Witold
A1 - Gramacki, Artur
A1 - Gramacki, Jarosław
A2 - Korbicz, Józef - red.
A2 - Uciński, Dariusz - red.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
L1 - http://zbc.uz.zgora.pl/Content/78892/AMCS_2013_23_4_14.pdf
L2 - http://zbc.uz.zgora.pl/Content/78892
KW - bandwidth selection
KW - graphics processing unit
KW - probability density function
KW - nonparametric estimation
KW - kernel estimation
T1 - Graphics processing units in acceleration of bandwidth selection for kernel density estimation
UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=78892
ER -