2017/07/20 by Jan Vaněk, Vanek, Jan, Josef Michálek +3
Computer Science · #68W10 #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #D.1.3 #Distributed #FOS: Computer and information sciences #I.5.5 #Machine Learning and Data Classification #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1707.06470
openalex publication_date 2017/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.