SVM with CUDA Accelerated Kernels for Big Sparse Problems

The SVM algorithm is one of the most frequently used methods for the classification process. For many domains, where the classification problems have many features as well as numerous instances, classification is a difficult and time-consuming task. For this reason, the following paper presents the CSR-GPU-SVM algorithm which accelerates SVM training for large and sparse problems with the use of the CUDA technology. Implementation is based on the SMO(Sequential Minimal Optimization) algorithm and utilizes the CSR(Compressed Sparse Row) sparse matrix format. The proposed solution allows us to perform efficient classification of big datasets, for example rcv1 and newsgroup20, for which classification with dense representation is not possible. The performed experiments have proven the accelerations in the order of 6 - 35 training times compared to original LibSVM implementation.