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The sparse matrix vector product (SMVP) is the kernel operation in many scientific applications. This kernel is an irregular problem, which has led to the development of several compressed storage formats. This paper discusses scalable implementations of sparse matrix-vector products, which are crucial for high performance solutions of large-scale linear(More)
The present paper discusses the implementations of sparse matrix-vector products, which are crucial for high performance solutions of large-scale linear equations, on a PC-Cluster. Three storage formats for sparse matrices compressed row storage, block compressed row storage and sparse block compressed row storage are evaluated. Although using BCRS format(More)
The matrix-vector product is one of the most important computational components of Krylov methods. This kernel is an irregular problem, which has led to the development of several compressed storage formats. We design a data structure for distributed matrix to compute the matrix-vector product efficiently on distributed memory parallel computers using MPI.(More)
A theory is developed that explains the stepsize patterns observed when standard predictor-corrector methods with variable stepsize strategy are used to solve stii or mildly stii problems. In some cases an algorithmic steady state occurs with smooth almost constant stepsizes; at other times an oscillating stepsize pattern of stepsizes is observed with the(More)
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