HyParSVM: a new hybrid parallel software for support vector machine learning on SMP clusters

Authors:
Tatjana Eitrich;Wolfgang Frings;Bruno Lang
Affiliations:
Central Institute for Applied Mathematics, Research Centre Jülich, Germany;Central Institute for Applied Mathematics, Research Centre Jülich, Germany;Applied Computer Science and Scientific Computing Group, Department of Mathematics, University of Wuppertal, Germany
Venue:
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Year:
2006

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Abstract

In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. The support vector machine (SVM) method is a well-known and reliable machine learning technique for classification and regression tasks. Based on a recently developed shared memory decomposition algorithm for support vector machine classifier design we increased the level of parallelism by implementing a cross validation routine based on message passing. With this extention we obtained a flexible parallel SVM software that can be used on high-end machines with SMP architectures to process the large data sets that arise more and more in bioinformatics and other fields of research.