Linear Solvers


Any physics solver relying on standard finite element and finite volume techniques requires the solution of algebraic linear systems, which are obtained upon linearization and discretization of the governing equations, of the form:

\mathsf{A} \mathsf{x} = \mathsf{b}

with a \mathsf{A} a square sparse matrix, \mathsf{x} the solution vector, and \mathsf{b} the right-hand side. For example, in a classical linear elastostatics problem \mathsf{A} is the stiffness matrix, and \mathsf{x} and \mathsf{b} are the displacement and nodal force vectors, respectively.

This solution stage represents the most computationally expensive portion of a typical simulation. Solution algorithms generally belong to two families of methods: direct methods and iterative methods. In GEOSX both options are made available wrapping around well-established open-source linear algebra libraries, namely HYPRE, PETSc, SuperLU, and Trilinos.

Direct methods

The major advantages are their reliability, robustness, and ease of use. However, they have large memory requirements and exhibit poor scalability. Direct methods should be used in a prototyping stage, for example when developing a new formulation or algorithm, when the dimension of the problem, namely the size of matrix \mathsf{A}, is small. Irrespective of the selected direct solver implementation, three stages can be idenitified:

  1. Setup Stage: the matrix is first analyzed and then factorized
  2. Solve Stage: the solution to the linear systems involving the factorized matrix is computed
  3. Finalize Stage: the systems involving the factorized matrix have been solved and the direct solver lifetime ends

The default option in GEOSX relies on SuperLU, a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations, that is called taking advantage of the interface provided in HYPRE.

Iterative methods

As the problem size (number of computational cells) increases, global iterative solution strategies are the method of choice—typically nonsymmetric Krylov solvers. Because of the possible poor conditioning of \mathsf{A}, preconditioning is essential to solve such systems efficiently. ‘’Preconditioning is simply a means of transforming the original linear system into one which has the same solution, but which is likely to be easier to solve with an iterative solver’’ [Saad (2003)].

The design of a robust and efficient preconditioner is based on a trade-off between two competing objectives:

  • Robustness: reducing the number of iterations needed by the preconditioned solver to achieve convergence;
  • Efficiency: limiting the time required to construct and apply the preconditioner.

Assuming a preconditioning matrix \mathsf{M} is available, three standard approaches are used to apply the preconditioner:

  1. Left preconditioning: the preconditioned system is \mathsf{M}^{-1} \mathsf{A} \mathsf{x} = \mathsf{M}^{-1} \mathsf{b}
  2. Right preconditioning: the preconditioned system is \mathsf{A} \mathsf{M}^{-1} \mathsf{y} = \mathsf{b}, with \mathsf{x} = \mathsf{M}^{-1} \mathsf{y}
  3. Split preconditioning: the preconditioned system is \mathsf{M}^{-1}_L \mathsf{A} \mathsf{M}^{-1}_R \mathsf{y} = \mathsf{M}^{-1}_L \mathsf{b}, with \mathsf{x} = \mathsf{M}^{-1}_R \mathsf{y}


The following table summarizes the available input parameters for the linear solver.

Name Type Default Description
amgAggresiveCoarseningLevels integer 0
AMG number levels for aggressive coarsening
Available options are: TODO
amgCoarseSolver geosx_LinearSolverParameters_AMG_CoarseType direct AMG coarsest level solver/smoother type. Available options are: default\|jacobi\|l1jacobi\|fgs\|sgs\|l1sgs\|chebyshev\|direct\|bgs
amgCoarseningType string HMIS
AMG coarsening algorithm
Available options are: TODO
amgInterpolationType integer 6
AMG interpolation algorithm
Available options are: TODO
amgNullSpaceType geosx_LinearSolverParameters_AMG_NullSpaceType constantModes AMG near null space approximation. Available options are:constantModes\|rigidBodyModes
amgNumFunctions integer 1
AMG number of functions
Available options are: TODO
amgNumSweeps integer 2 AMG smoother sweeps
amgSmootherType geosx_LinearSolverParameters_AMG_SmootherType fgs AMG smoother type. Available options are: default\|jacobi\|l1jacobi\|fgs\|bgs\|sgs\|l1sgs\|chebyshev\|ilu0\|ilut\|ic0\|ict
amgThreshold real64 0 AMG strength-of-connection threshold
directCheckResidual integer 0 Whether to check the linear system solution residual
directColPerm geosx_LinearSolverParameters_Direct_ColPerm metis How to permute the columns. Available options are: none\|MMD_AtplusA\|MMD_AtA\|colAMD\|metis\|parmetis
directEquil integer 1 Whether to scale the rows and columns of the matrix
directIterRef integer 1 Whether to perform iterative refinement
directParallel integer 1 Whether to use a parallel solver (instead of a serial one)
directReplTinyPivot integer 1 Whether to replace tiny pivots by sqrt(epsilon)*norm(A)
directRowPerm geosx_LinearSolverParameters_Direct_RowPerm mc64 How to permute the rows. Available options are: none\|mc64
iluFill integer 0 ILU(K) fill factor
iluThreshold real64 0 ILU(T) threshold factor
krylovAdaptiveTol integer 0 Use Eisenstat-Walker adaptive linear tolerance
krylovMaxIter integer 200 Maximum iterations allowed for an iterative solver
krylovMaxRestart integer 200 Maximum iterations before restart (GMRES only)
krylovTol real64 1e-06
Relative convergence tolerance of the iterative method
If the method converges, the iterative solution \mathsf{x}_k is such that
the relative residual norm satisfies:
\left\lVert \mathsf{b} - \mathsf{A} \mathsf{x}_k \right\rVert_2 < krylovTol * \left\lVert\mathsf{b}\right\rVert_2
krylovWeakestTol real64 0.001 Weakest-allowed tolerance for adaptive method
logLevel integer 0 Log level
preconditionerType geosx_LinearSolverParameters_PreconditionerType iluk Preconditioner type. Available options are: none\|jacobi\|l1jacobi\|fgs\|sgs\|l1sgs\|chebyshev\|iluk\|ilut\|icc\|ict\|amg\|mgr\|block\|direct\|bgs
solverType geosx_LinearSolverParameters_SolverType direct Linear solver type. Available options are: direct\|cg\|gmres\|fgmres\|bicgstab\|preconditioner
stopIfError integer 1 Whether to stop the simulation if the linear solver reports an error

Preconditioner descriptions

This section provides a brief description of the available preconditioners.

HYPRE MGR Preconditioner

MGR stands for multigrid reduction, a multigrid method that uses the interpolation, restriction operators, and the Galerkin triple product, to reduce a linear system to a smaller one, similar to a Schur complement approach. As such, it is designed to target block linear systems resulting from discretizations of multiphysics problems. GEOSX uses MGR through an implementation in HYPRE. More information regarding MGR can be found here. Currently, MGR strategies are implemented for hydraulic fracturing, poroelastic, compositional flow with and without wells. More multiphysics solvers with MGR will be enabled in the future.

To use MGR for a specific block system, several components need to be specified.

  1. The number of reduction levels and the coarse points (corresponding to fields) for each level. For example, for single-phase hydraulic fracturing, there are two fields, i.e. displacement and fluid pressure, a two-level MGR strategy can be used with the fluid pressure being the coarse degrees of freedom.
  2. Interpolation/restriction operators and the coarse-grid computation strategy. A simple but effective strategy is to use Jacobi diagonalization for interpolation and injection for restriction. For most cases, a Galerkin coarse grid strategy can be used, but for special cases such as poroelastic, a non-Galerkin approach is preferable.
  3. Global smoother. Depending on the problem, a global relaxation step could be beneficial. Some options include ILU(k), (block) Jacobi, (block) Gauss-Seidel.
  4. Solvers for F-relaxation and coarse-grid correction. These solvers should be chosen carefully for MGR to be effective. The choice of these solvers should correspond to the properties of the blocks specified by the C- and F-points. For example, if the \mathsf{A}_{FF} block is hyperbolic, a Jacobi smoother is sufficient while for an elliptic operator an AMG V-cycle might be required. For the single-phase hydraulic fracturing case, an AMG V-cycle is needed for both F-relaxation and coarse-grid correction.

Note that these are only general guidelines for designing a working MGR recipe. For complicated multiphysics problems, experimentation with different numbers of levels, choices of C- and F-points, and smoothers/solvers, etc., is typically needed to find the best strategy. Currently, these options are only available to developers. We are working on exposing these functionalities to the users in future releases.

Block preconditioner

This framework allows the user to design a block preconditioner for a 2 x 2 block matrix. The key component is the Schur complement \mathsf{S} = \mathsf{A}_{11} - \mathsf{A}_{10} \mathsf{\widetilde{A}}_{00}^{-1} \mathsf{A}_{01} computation, that requires an approximation of the leading block. Currently, available options for \mathsf{\widetilde{A}}_{00}^{-1} are:

  • diagonal with diagonal values (essentially, a Jacobi preconditioner);
  • diagonal with row sums as values (e.g., used for CPR-like preconditioners).

Once the Schur complement is computed, to properly define the block preconditioner we need:

  • the preconditioner for \mathsf{A}_{00} (any of the above listed single-matrix preconditioner);
  • the preconditioner for \mathsf{S} (any of the above listed single-matrix preconditioner);
  • the application strategy. This can be:
    • diagonal: none of the coupling terms is used;
    • upper triangular: only the upper triangular coupling term is used;
    • lower-upper triangular: both coupling terms are used.

Moreover, a block scaling is available. Feasible options are:

  • none: keep the original scaling;
  • Frobenius norm: equilibrate Frobenius norm of the diagonal blocks;
  • user provided.