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]

See also

References

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


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