A comparison of preconditioned nonsymmetric Krylov methods on a large-scale MIMD machine
Many complex physical processes are modeled by coupled systems of partial differential equations (PDEs). Often, the numerical approximation of these PDEs requires the solution of large sparse nonsymmetric systems of equations. In this paper we compare the parallel performance of a number of preconditioned Krylov subspace methods on a large-scale MIMD machine. These methods are among the most robust and efficient iterative algorithms for the solution of large sparse linear systems. They are easy to implement on various architectures and work well on a wide variety of important problems. In this comparison we focus on the parallel issues associated with both local preconditioners (those that combine information from the entire domain). The various preconditioners are applied to a variety of PDE problems within the GMRES, CCGS, BiCGSTAB, and QMRCGS methods. Conclusions are drawn on the effectiveness of the different schemes based on results obtained from a 1024 processor a nCUBE 2 hypercube.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 10119471
- Report Number(s):
- SAND-91-0333C; CONF-920472-1; ON: DE92006838
- Resource Relation:
- Conference: Copper Mountain conference on iterative methods,Copper Mountain, CO (United States),9-16 Apr 1992; Other Information: PBD: 2 Jan 1992
- Country of Publication:
- United States
- Language:
- English
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