## Abstract

Linear systems in chemical physics often involve matrices with a certain sparse block structure. These can often be solved very effectively using iterative methods (sequence of matrix-vector products) in conjunction with a block Jacobi preconditioner [B. Poirier, Numer. Linear Algebra Appl. 7 (2000) 715]. In a two-part series, we present an efficient parallel implementation, incorporating several additional refinements. The present study (paper II) indicates that the basic parallel sparse matrix-vector product operation itself is the overall scalability bottleneck, faring much more poorly than the specialized, block Jacobi routines considered in a companion paper (paper I). However, a simple dimensional combination scheme is found to alleviate this difficulty.

Original language | English |
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Pages (from-to) | 198-209 |

Number of pages | 12 |

Journal | Journal of Computational Physics |

Volume | 219 |

Issue number | 1 |

DOIs | |

State | Published - Nov 20 2006 |

## Keywords

- Chemical physics
- Eigensolver
- Linear solver
- Parallel computing
- Preconditioning
- Sparse matrix