A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations
New Technologies of the Information Society, University of West Bohemia in Pilsen, Technicka 8, 306 14 Plzen, Czech Republic
arXiv:1707.06470 [cs.DC], (20 Jul 2017)
@article{vanek2017comparison,
title={A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations},
author={Vanek, Jan and Michalek, Josef and Psutka, Josef},
year={2017},
month={jul},
archivePrefix={"arXiv"},
primaryClass={cs.DC}
}
Last several years, GPUs are used to accelerate computations in many computer science domains. We focused on GPU accelerated Support Vector Machines (SVM) training with non-linear kernel functions. We had searched for all available GPU accelerated C++ open-source implementations and created an open-source C++ benchmark project. We modifed all the implementations to run on actual hardware and software and in both Windows and Linux operating systems. The benchmark project offers making a fair and direct comparison of the individual implementations under the same conditions, datasets, and hardware. In addition, we selected the most popular datasets in the community and tested them. Finally, based on the evaluation, we recommended the best-performing implementations for dense and sparse datasets.
July 22, 2017 by hgpu
