Preconditioners, or accelerators are used to accelerate an iterative solution process. In some cases, their use can reduce the number of iterations dramatically and thus lead to better solver performance. Although the terms preconditioner and accelerator are synonyms, hereafter only preconditioner is used.
Intel MKL provides two preconditioners, ILU0 and ILUT, for sparse matrices presented in the format accepted in the Intel MKL direct sparse solvers (threearray variation of the CSR storage format described in Sparse Matrix Storage Format ). The algorithms used are described in [Saad03].
The ILU0 preconditioner is based on a wellknown factorization of the original matrix into a product of two triangular matrices: lower and upper triangular matrices. Usually, such decomposition leads to some fillin in the resulting matrix structure in comparison with the original matrix. The distinctive feature of the ILU0 preconditioner is that it preserves the structure of the original matrix in the result.
Unlike the ILU0 preconditioner, the ILUT preconditioner preserves some resulting fillin in the preconditioner matrix structure. The distinctive feature of the ILUT algorithm is that it calculates each element of the preconditioner and saves each one if it satisfies two conditions simultaneously: its value is greater than the product of the given tolerance and matrix row norm, and its value is in the given bandwidth of the resulting preconditioner matrix.
Both ILU0 and ILUT preconditioners can apply to any nondegenerate matrix. They can be used alone or together with the Intel MKL RCI FGMRES solver (see Sparse Solver Routines). Avoid using these preconditioners with MKL RCI CG solver because in general, they produce a nonsymmetric resulting matrix even if the original matrix is symmetric. Usually, an inverse of the preconditioner is required in this case. To do this the Intel MKL triangular solver routine mkl_dcsrtrsv must be applied twice: for the lower triangular part of the preconditioner, and then for its upper triangular part.
Note
Although ILU0 and ILUT preconditioners apply to any nondegenerate matrix, in some cases the algorithm may fail to ensure successful termination and the required result. Whether or not the preconditioner produces an acceptable result can only be determined in practice.
A preconditioner may increase the number of iterations for an arbitrary case of the system and the initial solution, and even ruin the convergence. It is your responsibility as a user to choose a suitable preconditioner.
General Scheme of Using ILUT and RCI FGMRES Routines
The general scheme for use is the same for both preconditioners. Some differences exist in the calling parameters of the preconditioners and in the subsequent call of two triangular solvers. You can see all these differences in the code examples for both preconditioners (dcsrilu*.f and dcsrilu*.c) in the examples folder of the Intel MKL installation directory:

Fortran examples: examples/solverf/source

C examples: examples/solverc/source
The following pseudocode shows the general scheme of using the ILUT preconditioner in the RCI FGMRES context.
...
generate matrix A
generate preconditioner C (optional)
call dfgmres_init(n, x, b, RCI_request, ipar, dpar, tmp)
change parameters in ipar, dpar if necessary
call dcsrilut(n, a, ia, ja, bilut, ibilut, jbilut, tol, maxfil, ipar, dpar, ierr)
call dfgmres_check(n, x, b, RCI_request, ipar, dpar, tmp)
1 call dfgmres(n, x, b, RCI_request, ipar, dpar, tmp)
if (RCI_request.eq.1)
then
multiply the matrix A by tmp(ipar(22))
and put the result in tmp(ipar(23))
c proceed with FGMRES iterations
goto 1
endif
if (RCI_request.eq.2) then
do the stopping test
if
(test not passed) then
c
proceed with FGMRES iterations
go to 1
else
c
stop FGMRES iterations.
goto 2
endif
endif
if (RCI_request.eq.3)
then
c
Below, trvec
is an intermediate vector of length at least n
c
Here is the recommended use of the result produced by the ILUT routine.
c
via standard Intel MKL Sparse Blas solver routine mkl_dcsrtrsv.
call mkl_dcsrtrsv('L','N','U', n, bilut, ibilut, jbilut, tmp(ipar(22)),trvec)
call mkl_dcsrtrsv('U','N','N', n, bilut, ibilut, jbilut, trvec, tmp(ipar(23)))
c
proceed with FGMRES iterations
goto 1
endif
if (RCI_request.eq.4) then
check the norm of the next orthogonal vector, it is contained in dpar(7)
if
(the norm is not zero up to rounding/computational errors) then
c proceed with FGMRES iterations
goto 1
else
c stop FGMRES iterations
goto 2
endif
endif
2 call dfgmres_get(n, x, b, RCI_request, ipar, dpar, tmp, itercount)
current iteration number is in itercount
the computed approximation is in the array x