An intelligent system for accelerating parallel SVM classification problems on large datasets using GPU

Abstract

Support Vector Machine (SVM) is one of the most popular tools for solving general classification and regression problems because of its high predicting accuracy. However, the training phase of nonlinear kernel based SVM algorithm is a computationally expensive task, especially for large datasets. In this paper, we propose an intelligent system to solve large classification problems based on parallel SVM. The system utilizes the latest powerful GPU device to improve the speed performance of SVM training and predicting phases. The memory constraint issue brought by large datasets is addressed through either data reduction or data chunking techniques. The complete system includes multiple executable modules and all of them are managed through a main script, which reduces the implementation difficulty and offers platform portability. Empirical results have shown that our system achieves an order of magnitude speed up compared to the classic SVM tool, LIBSVM. The speed performance is further improved to two orders of magnitude by slightly compromising on the predicting accuracy.