Name
Abstract | ||
|---|---|---|
Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples the algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/SPR-2012-0352 | Scientific Programming |
Keywords | Field | DocType |
different iterative method,multiple simultaneous linear system,mixed-precision algorithm,sparse matrix,iterative solvers,related linear system,different sparse matrix factorization,sparse linear solvers,large sparse linear system,underlying linear algebra object,iterative method | Linear algebra,Standard algorithms,Linear system,Computer science,Iterative method,Matrix (mathematics),Parallel computing,Computational science,Software,Solver,Sparse matrix | Journal |
Volume | Issue | ISSN |
20 | 3 | 1058-9244 |
Citations | PageRank | References |
2 | 0.43 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Bavier | 1 | 3 | 0.80 |
| Mark Hoemmen | 2 | 297 | 18.02 |
| Sivasankaran Rajamanickam | 3 | 446 | 33.19 |
| Heidi Thornquist | 4 | 6 | 1.64 |