Matrix reorderings for "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs"

The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods. This comparison is based on 490 sparse matrices from the SuiteSparse Matrix Collection (https://sparse.tamu.edu) and 6 matrix reordering algorithms. The dataset provided here supplies the permutations and reordered matrices in Matrix Market file format for 3 matrices and 6 reorderings.