Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication
The authors investigate single and multiple sparse matrix-vector multiplications (SpMxV). For single SpMxV, they propose two cache-size-aware row/column reordering methods based on one-dimensional (1D) and two-dimensional (2D) top-down sparse matrix partitioning. They make use of the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The authors evaluate performances of the proposed models and methods on a wide range of sparse matrices. They point out that the proposed methods outperform the available schemes, and that the methods based on 2D partitioning perform better than 1D partitioning methods.
scientific article; zbMATH DE number 6207557