Accelerating SVM with GPU: The State of the Art

Pawel Drozda, Krzysztof Sopyla

Abstract: This article summarizes the achievements that have been made in the field of GPU SVM acceleration. In particular, the algorithms which allow the acceleration of SVM classification performed on dense datasets are presented and the limitations of the dataset size are pointed out. Moreover, the solutions which deal with large sparse collections are demonstrated. These algorithms apply different sparse dataset formats to make possible the classification on the GPU. Finally, GPU implementations for different SVM kernel functions are provided.