Sparse matrix-vector multiplication

Sparse matrix-vector multiplication (SpMV) of the form $y = Ax$ is a widely used computational kernel existing in many scientific applications. The input matrix $A$ is sparse. The input vector $x$ and the output vector $y$ are dense. In the case of a repeated $y = Ax$ operation involving the same input matrix $A$ but possibly changing numerical values of its elements, $A$ can be preprocessed to reduce both the parallel and sequential run time of the SpMV kernel.[1]

References

"Hypergraph Partitioning Based Models and Methods for Exploiting Cache Locality in Sparse Matrix-Vector Multiplication". Retrieved 13 April 2014.

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[1] "Hypergraph Partitioning Based Models and Methods for Exploiting Cache Locality in Sparse Matrix-Vector Multiplication". Retrieved 13 April 2014.