SuperLU{_}DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
In this paper, we present the main algorithmic features in the software package SuperLU{_}DIST, a distributed-memory sparse direct solver for large sets of linear equations. We give in detail our parallelization strategies, with focus on scalability issues, and demonstrate the parallel performance and scalability on current machines. The solver is based on sparse Gaussian elimination, with an innovative static pivoting strategy proposed earlier by the authors. The main advantage of static pivoting over classical partial pivoting is that it permits a priori determination of data structures and communication pattern for sparse Gaussian elimination, which makes it more scalable on distributed memory machines. Based on this a priori knowledge, we designed highly parallel and scalable algorithms for both LU decomposition and triangular solve and we show that they are suitable for large-scale distributed memory machines.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE. Office of Advanced Scientific Computing Research. Division of Mathematical Information and Computational Sciences. Contracts DE-AC03-76SF00098 and DE-FG03-94ER25219 and DE-FC03-98ER25351; National Science Foundation Cooperative Agreement ACI-9619010 and ACI-98ER25351; University of Tennessee Subcontract ORA4466 from Agenzia regionale per la protezione ambientale Contract DAAL03-91-C0047 (US)
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 836786
- Report Number(s):
- LBNL-49388; R&D Project: KS1210; TRN: US200504%%296
- Journal Information:
- ACM Transaction on Mathematical Software, Vol. 29, Issue 2; Other Information: Submitted to ACM Transaction on Mathematical Software: Volume 29, No.2; Journal Publication Date: 6/2003; PBD: 27 Mar 2002
- Country of Publication:
- United States
- Language:
- English
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