Abstract
Support vector machines are widely used for classification and regression tasks. However, sequential implementations for support vector machines are usually unable to deal with the increasing size of current real-world learning problems. In this context, Intel®Xeon PhiTM processors allow easily incorporating high performance computing strategies to improve execution times. This article proposes a parallel implementation of the popular LIBSVM library, specially adapted to the Intel®Xeon PhiTM architecture. The proposed implementation is evaluated using publicly available datasets corresponding to classification and regression tasks. Results show that the proposed parallel version computes the same results than the original LIBSVM while reducing the time needed for training by up to a factor of 4.81.
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Acknowledgement
The work of R. Massobrio and S. Nesmachnow was partly supported by PEDECIBA and ANII, Uruguay. R. Massobrio would like to thank ANII, Uruguay and Fundación Carolina, Spain. B. Dorronsoro would like to acknowledge the Spanish MINECO-FEDER for the support provided under contracts TIN2014-60844-R (the SAVANT project) and RYC-2013-13355.
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Massobrio, R., Nesmachnow, S., Dorronsoro, B. (2018). Support Vector Machine Acceleration for Intel Xeon Phi Manycore Processors. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_20
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DOI: https://doi.org/10.1007/978-3-319-73353-1_20
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