Accelerating K-Means on the Graphics Processor via CUDA

Author(s):  
Mario Zechner ◽  
Michael Granitzer
Keyword(s):  
2011 ◽  
Vol 182 (7) ◽  
pp. 1414-1420 ◽  
Author(s):  
J.M. Alcaraz-Pelegrina ◽  
P. Rodríguez-García

2015 ◽  
Vol 24 (01) ◽  
pp. 1450012 ◽  
Author(s):  
Krzysztof Sopyla ◽  
Paweł Drozda

This paper presents the SECu-SVM algorithm for solving classification problems. It allows for a significant acceleration of the standard SVM implementations by transferring the most time-consuming computations from the standard CPU to the Graphics Processor Units (GPU). In addition, highly efficient Sliced EllR-T sparse matrix format was used for storing the dataset in GPU memory, which requires a very low memory footprint and is also well adapted to parallel processing. Performed experiments demonstrate an acceleration of 4–100 times over LibSVM. Moreover, in the majority of cases the SECu-SVM is less time-consuming than the best sparse GPU implementations and allows for handling significantly larger classification datasets.


2010 ◽  
Vol 16 (S2) ◽  
pp. 730-731
Author(s):  
J Michálek ◽  
M Čapek ◽  
J Janáček ◽  
L Kubínová

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.


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