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Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single-and… (More)

The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single-and… (More)

—Sparse matrix-vector and matrix-transpose-vector multiplication (SpMM T V) repeatedly performed as z A T x and y A z (or y A w) for the same sparse matrix A is a kernel operation widely used in various iterative solvers. One important optimization for serial SpMM T V is reusing A-matrix nonzeros, which halves the memory bandwidth requirement. However,… (More)

We acknowledge PRACE for the Preparatory Access Call Type B (resource) awards for our applications numbered 2010PA0930 and 2010PA2149. The library presented in this work has been developed and tested using these awarded resources, JUQUEEN at Jülich Supercomputing Centre and SuperMUC at Leibniz Supercomputing Center, all of which are based in Germany.

Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single-and… (More)

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