A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

Autor:	Vanek, Jan, Michalek, Josef, Psutka, Josef
Rok vydání:	2017
Předmět:	Computer Science - Distributed Parallel and Cluster Computing 68W10 D.1.3 I.5.5
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.06470 Zobrazit plný text záznamu View this record from Arxiv