Fast Support Vector Machine Training and Classification on Graphics Processors
Abstract
Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. We describe a solver for Support Vector Machine training, using Platt's Sequential Minimal Optimization algorithm and an adaptive first and second order working set selection heuristic, which achieves speedups of 9-35x over LIBSVM running on a traditional processor. We also present a GPU-based system for SVM classification which achieves speedups of 81-138x over LibSVM (5-24x over our own CPU-based SVM classifier).
Cite
Text
Catanzaro et al. "Fast Support Vector Machine Training and Classification on Graphics Processors." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390170Markdown
[Catanzaro et al. "Fast Support Vector Machine Training and Classification on Graphics Processors." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/catanzaro2008icml-fast/) doi:10.1145/1390156.1390170BibTeX
@inproceedings{catanzaro2008icml-fast,
title = {{Fast Support Vector Machine Training and Classification on Graphics Processors}},
author = {Catanzaro, Bryan and Sundaram, Narayanan and Keutzer, Kurt},
booktitle = {International Conference on Machine Learning},
year = {2008},
pages = {104-111},
doi = {10.1145/1390156.1390170},
url = {https://mlanthology.org/icml/2008/catanzaro2008icml-fast/}
}