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Parallel multiclass classification using SVMs on GPUs

Sergio H. Lopez, John R. Williams, Abel Sanchez
Massachusetts Institute of Technology, Cambridge, MA
In Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units (2010), pp. 2-11

@conference{herrero2010parallel,

   title={Parallel multiclass classification using SVMs on GPUs},

   author={Herrero-Lopez, S. and Williams, J.R. and Sanchez, A.},

   booktitle={Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units},

   pages={2–11},

   year={2010},

   organization={ACM}

}

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The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to a new parallel scenario. Graphics Processing Units (GPU) have arisen as high performance platforms to implement data parallel algorithms. In this paper, it is described how a naive implementation of a multiclass classifier based on SVMs can map its inherent degrees of parallelism to the GPU programming model and efficiently use its computational throughput. Empirical results show that the training and classification time of the algorithm can be reduced an order of magnitude compared to a classical multiclass solver, LIBSVM, while guaranteeing the same accuracy.
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