Fast training of support vector machines on the Cell processor

Abstract

Support vector machines (SVMs) are a widely used technique for classification, clustering and data analysis. While efficient algorithms for training SVM are available, dealing with large datasets makes training and classification a computationally challenging problem. In this paper we exploit modern processor architectures to improve the training speed of LIBSVM , a well known implementation of the sequential minimal optimization algorithm. We describe LIBSVM CBE , an optimized version of LIBSVM which takes advantage of the peculiar architecture of the Cell Broadband Engine. We assess the performance of LIBSVM CBE on real-world training problems, and we show how this optimization is particularly effective on large, dense datasets.