Efficient Parallel Algorithms for Linear RankSVM on GPU

Abstract : Linear RankSVM is one of the widely used methods for learning to rank. Although using Order-Statistic Tree (OST) and Trust Region Newton Methods (TRON) are effective to train linear RankSVM on CPU, it becomes less effective when dealing with large-scale training data sets. Furthermore, linear RankSVM training with L2-loss contains quite amount of matrix manipulations in comparison with that with L1-loss, so it has great potential for achieving parallelism on GPU. In this paper, we design efficient parallel algorithms on GPU for the linear RankSVM training with L2-loss based on different queries. The experimental results show that, compared with the state-of-the-art algorithms for the linear RankSVM training with L2-loss on CPU, our proposed parallel algorithm not only can significantly enhance the training speed but also maintain the high prediction accuracy.
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Ching-Hsien Hsu; Xuanhua Shi; Valentina Salapura. 11th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2014, Ilan, Taiwan. Springer, Lecture Notes in Computer Science, LNCS-8707, pp.181-194, 2014, Network and Parallel Computing. 〈10.1007/978-3-662-44917-2_16〉
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Jing Jin, Xiaola Lin. Efficient Parallel Algorithms for Linear RankSVM on GPU. Ching-Hsien Hsu; Xuanhua Shi; Valentina Salapura. 11th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2014, Ilan, Taiwan. Springer, Lecture Notes in Computer Science, LNCS-8707, pp.181-194, 2014, Network and Parallel Computing. 〈10.1007/978-3-662-44917-2_16〉. 〈hal-01403083〉

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