# FSAI

FSAI is a parallel implementation of the Factorized Sparse Approximate Inverse preconditioner, initially proposed by [KoYe1993]. Given a symmetric positive definite matrix $$A$$, FSAI computes a triangular matrix $$G$$ that approximates the inverse of the lower Cholesky factor ($$L$$) of $$A$$. This computation is done by minimizing the Frobenius norm $$|| I - G L ||_F$$ without explicit knowledge of $$L$$. The resulting preconditioner preserves the positive definiteness of $$A$$ and is given by $$M^{-1} = G^{T} G$$.

One of the critical factors determining the quality of sparse approximate inverse preconditioners lies in choosing the sparsity pattern of $$G$$. While ParaSails employs a priori sparsity patterns, FSAI uses an iterative strategy that generates sparsity patterns on the fly, i.e., while computing their nonzero coefficient values concurrently. At every step of the iterative process, the sparsity pattern of a row of $$G$$ is augmented with a fixed number of entries, leading to the most significant reduction of the conditioning number of $$G A G^T$$. Such a strategy is also called “adaptive FSAI” or “dynamic FSAI” and it can lead to more robust sparse approximate inverses than ParaSails. For more details on how it works, see [JaFe2015].

## Parameter Settings

The accuracy and cost of FSAI are determined by three configurations parameters as shown in the table below

param

type

range

sug. values

default

max_steps

int

$$\ge 0$$

5, 10, 30

5

max_step_size

int

$$\ge 0$$

1, 3, 6

3

kap_tolerance

real

$$\ge 0$$

0.0, 1.E-2, 1.E-3

1.E-3

The first parameter, max_steps, controls the number of maximum steps used in the iterative algorithm. The second parameter, max_step_size, gives the maximum number of indices added to the sparsity pattern of $$G$$ at each step. Lastly, the third parameter, kap_tolerance, is a floating-point value used to stop the inclusion of new indices to the sparsity pattern of $$G$$ when the conditioning number of $$G A G^T$$ stagnates. This can be disabled by setting kap_tolerance = 0. Naturally, the preconditioner quality increases for denser sparsity patterns of $$G$$, but so do its setup and solve costs. For a reasonable balance between accuracy and cost, we recommend that $$max\_steps * max\_step\_size \leq 30$$. The configuration parameters of FSAI can be set via the following calls:

HYPRE_FSAISetMaxSteps(HYPRE_Solver solver, HYPRE_Int max_steps);
HYPRE_FSAISetMaxStepSize(HYPRE_Solver solver, HYPRE_Int max_step_size);
HYPRE_FSAISetKapTolerance(HYPRE_Solver solver, HYPRE_Real kap_tolerance);


## FSAI as Smoother to BoomerAMG

As discussed in [PaFa2019], the factorized sparse approximate inverse method can be an effective smoother to AMG for several reasons. Particularly, it leads to a symmetric operator, and thus allows AMG to be used as a preconditioner for the conjugate gradient solver. In hypre, FSAI can be used as a complex smoother to BoomerAMG by calling the functions:

HYPRE_BoomerAMGSetSmoothType(HYPRE_Solver solver, 4);
HYPRE_BoomerAMGSetSmoothNumLevels(HYPRE_Solver solver, HYPRE_Int num_levels);


where num_levels is the last multigrid level where FSAI is used. The configuration parameters of the FSAI smoother, as described above, can be set via the following calls:

HYPRE_BoomerAMGSetFSAIMaxSteps(HYPRE_Solver solver, HYPRE_Int max_steps);
HYPRE_BoomerAMGSetFSAIMaxStepSize(HYPRE_Solver solver, HYPRE_Int max_step_size);
HYPRE_BoomerAMGSetFSAIKapTolerance(HYPRE_Solver solver, HYPRE_Real kap_tolerance);


## Implementation Notes

• When the matrix $$A$$ is distributed across MPI tasks, FSAI considers only the block diagonal portions of $$A$$ for computing $$G$$. The resulting preconditioner is effectively a block-Jacobi sparse approximate inverse in the MPI sense. Although this strategy reduces communication costs, it can degrade convergence performance when several tasks are used, especially when FSAI is employed as a preconditioner to a Krylov solver.

• The CPU version of FSAI supports threading via OpenMP. To enable it, users need to compile hypre with OpenMP support via the configure option --with-openmp. In this case, FSAI relies on an implementation of BLAS/LAPACK that is thread-safe. The one distributed internally with hypre fulfills this criterion, but care must be taken when linking hypre to external BLAS/LAPACK libraries. In HPC platforms, we recommend using vendor implementations of BLAS/LAPACK for better setup performance of FSAI, regardless of whether using OpenMP or not.

• The GPU version of FSAI is under development.