Hai-xiang Zhao and Frédéric Magoulès
Support vector machine, Parallel, Multi-core, Decomposition
This paper proposes a new and efficient parallel implementation of support vector machines based on decomposition method for handling large scale datasets. The parallelizing is performed on the most time-and-memory consuming work of training, i.e., to update the vector ƒ. The inner problems are dealt by sequential minimal optimization solver. Since the underlying parallelism is realized by the shared memory version of Map-Reduce paradigm, our system is easy to build and particularly suitable to apply to multi-core and multiprocessor systems. Experimental results show that on most of the tested datasets, our system offers higher than four-fold increase in speed compared to Libsvm, and it is also far more efficient than the MPI implementation Pisvm.
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