Multiclass Support Vector Machine

 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 project, it is described how a naïve 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 solver, LIBSVM, while guaranteeing the same accuracy.

Please find attached the multisvm 2.0 release of the source code.

The link to the source code repository where future versions will be available is

* Sample datasets were removed due to their large file size. These can be obtained from the code repository site or the LIBSVM site.

** To compile the code please add the following CUDA libraries to the bin folder of the project or download the release code from the google code repository (which already contains these as part of the visual studio solution).