[u' @article{lopez_novoa_kernel_2015, title = {Kernel density estimation in accelerators: {Implementation} and performance evaluation}, issn = {0920-8542}, url = {http://link.springer.com/article/10.1007%2Fs11227-015-1577-7}, doi = {10.1007/s11227-015-1577-7}, abstract = {Kernel density estimation (KDE) is a popular technique used to estimate the probability density function of a random variable. KDE is considered a fundamental data smoothing algorithm, and it is a common building block in many scientific applications. In a previous work we presented S-KDE, an efficient algorithmic approach to compute KDE that outperformed other state-of-the-art implementations, providing accurate results in much reduced execution times. Its parallel implementation targeted multi- and many-core processors. In this work we present an OpenCL implementation of S-KDE, targeting modern accelerators in a portable way. We test our implementation on three accelerators from different manufacturers, achieving speedups around 5\xd7 compared to a hand-tuned serial version of S-KDE. We also analyze the performance of the code in these accelerators, to find out to what extent our code exploits their capabilities.}, journal = {The Journal of Supercomputing}, author = {Lopez Novoa, Unai and Mendiburu, Alexander and Miguel-Alonso, Jose}, month = dec, year = {2015}, note = {00000}, keywords = {Q3, jcr0.858, parallel computing, statistics} }']