FSAIPACK

FSAIPACK: A software package for high performance FSAI preconditioning. The Factorized Sparse Approximate Inverse (FSAI) is an efficient technique for preconditioning parallel solvers of symmetric positive definite sparse linear systems. The key factor controlling FSAI efficiency is the identification of an appropriate nonzero pattern. Currently, several strategies have been proposed for building such a nonzero pattern, using both static and dynamic techniques. This article describes a fresh software package, called FSAIPACK, which we developed for shared memory parallel machines. It collects all available algorithms for computing FSAI preconditioners. FSAIPACK allows for combining different techniques according to any specified strategy, hence enabling the user to thoroughly exploit the potential of each preconditioner, in solving any peculiar problem. FSAIPACK is freely available as a compiled library at http://www.dmsa.unipd.it/ janna/software.html, together with an open-source command language interpreter. By writing a command ASCII file, one can easily perform and test any given strategy for building an FSAI preconditioner. Numerical experiments are discussed in order to highlight the FSAIPACK features and evaluate its computational performance.


References in zbMATH (referenced in 14 articles , 1 standard article )

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  1. Franceschini, Andrea; Castelletto, Nicola; Ferronato, Massimiliano: Approximate inverse-based block preconditioners in poroelasticity (2021)
  2. Bernaschi, Massimo; Carrozzo, Mauro; Franceschini, Andrea; Janna, Carlo: A dynamic pattern factored sparse approximate inverse preconditioner on graphics processing units (2019)
  3. Ferronato, Massimiliano; Franceschini, Andrea; Janna, Carlo; Castelletto, Nicola; Tchelepi, Hamdi A.: A general preconditioning framework for coupled multiphysics problems with application to contact- and poro-mechanics (2019)
  4. Franceschini, Andrea; Castelletto, Nicola; Ferronato, Massimiliano: Block preconditioning for fault/fracture mechanics saddle-point problems (2019)
  5. Franceschini, Andrea; Paludetto Magri, Victor A.; Mazzucco, Gianluca; Spiezia, Nicolò; Janna, Carlo: A robust adaptive algebraic multigrid linear solver for structural mechanics (2019)
  6. Paludetto Magri, Victor A.; Franceschini, Andrea; Janna, Carlo: A novel algebraic multigrid approach based on adaptive smoothing and prolongation for ill-conditioned systems (2019)
  7. Ferronato, Massimiliano; Pini, Giorgio: A supernodal block factorized sparse approximate inverse for non-symmetric linear systems (2018)
  8. Franceschini, Andrea; Paludetto Magri, Victor Antonio; Ferronato, Massimiliano; Janna, Carlo: A robust multilevel approximate inverse preconditioner for symmetric positive definite matrices (2018)
  9. Hook, James; Scott, Jennifer; Tisseur, Françoise; Hogg, Jonathan: A Max-plus approach to incomplete Cholesky factorization preconditioners (2018)
  10. Kyziropoulos, Panagiotis E.; Filelis-Papadopoulos, Christos K.; Gravvanis, George A.: A class of symmetric factored approximate inverses and hybrid two-level solver (2018)
  11. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  12. Castelletto, Nicola; White, Joshua A.; Ferronato, Massimiliano: Scalable algorithms for three-field mixed finite element coupled poromechanics (2016)
  13. White, Joshua A.; Castelletto, Nicola; Tchelepi, Hamdi A.: Block-partitioned solvers for coupled poromechanics: a unified framework (2016)
  14. Janna, Carlo; Ferronato, Massimiliano; Sartoretto, Flavio; Gambolati, Giuseppe: FSAIPACK: a software package for high-performance factored sparse approximate inverse preconditioning (2015)