Algorithmic Skeletons for General Sparse Matrices on Multi-Core Processors

P. Ciechanowicz (Germany)


algorithmic skeletons, multi-core processing, sparse matrix


We develop an extension of the Muenster Skeleton Library Muesli by a distributed data structure for general sparse matrices. The data structure supports data parallel algorith mic skeletons such as fold, map, and zip. Our implemen tation is highly flexible, object-oriented and makes use of the C++ template mechanism. As a result, the storable data type as well as the compression and distribution scheme can easily be changed and even be substituted by a user defined one. As a unique feature, our implementation not only supports multi-processor architectures, but also effi ciently makes use of current multi-core processors.

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