Chinese Journal of Computational Physics ›› 2024, Vol. 41 ›› Issue (1): 64-74.DOI: 10.19596/j.cnki.1001-246x.8787

• Performance Optimization Techniques and Parallel Numerical Algorithms for Supercomputing • Previous Articles     Next Articles

Feature-modified Algorithm Framework for Parallel Preconditioning in Sparse Linear Solvers

Xiaowen XU1,2(), Zeyao MO1,2, Shaoliang HU1,2, Hengbin AN1,2   

  1. 1. Institute of Applied Physics and Computational Mathematics, Laboratory of Computational Physics, Beijing 100094, China
    2. Software Center for High Performance Numerical Simulation, China Academy of Engineering Physics, Beijing 100088, China
  • Received:2023-06-30 Online:2024-01-25 Published:2024-02-05

Abstract:

To address the high computational complexity of sparse linear solvers caused by complex physical characteristics in practical applications, this paper presents a unified framework for feature-modified preconditioning algorithms. By refining the algebraic features affecting the efficiency from physical characteristics and combining multilevel feature analysis, we construct feature-modified components. The effectiveness of this framework is demonstrated through several typical feature-modified preconditioning algorithms and their application results.

Key words: sparse linear algebraic equations, feature-modified, iterative methods, preconditioning algorithms, parallel algorithms

CLC Number: