> On Nov 23, 2016, at 4:57 PM, Łukasz Kasza <rpgw...@wp.pl> wrote: > > Dear PETSc Users, > > I want to compute approximate matrix product A^-1*B where A^-1 is > approximated by ILU(0) of A matrix for sequential and parallel executions. > For sequential code I can use KSP in preonly mode (PCILU works only for > sequential code), get the ILU(0) by PCFactorGetMatrix and then call > MatMatSolve. However the resulting matrix has to be in dense format because > that's the way MatMatSolve works. In my case A^-1*B product is sparse for > sure, because ILU(0) has the same sparsity pattern.
Even though your ILU(0) factorization of A is sparse the resulting A^-1*B will almost surely be dense since the triangular solves"fill" in the column vector entries in the result. > Suppose that I > will have a factored A matrix for parallel code as well. What is the best way > to get the A^-1*B product without creating dense matrices? Is it even > possible? Why do you want to form A^-1*B? Or do you want to just use A^-1*B in some other computation? Perhaps that can be done "matrix" free by not forming A^-1*B but instead doing matrix vector products of A^-1*B which will be cheap if you have an ILU(0) of A. Barry > > Best Regards, > Damian Goik > > >
