Thank you very much for your reply. Given this, when using MUMPS in parallel, I can still get the factor matrix (using getFactorMatrix method of a PC object) and use it to do matrix multiplications (e.g., using matMult method of the factor matrix), correct? I also would like to confirm whether the factor matrix returned is really triangular and multiplying it with another matrix gives the intended result.
> On Nov 16, 2025, at 08:59, Barry Smith <[email protected]> wrote: > > It appears that only MATSOLVERMKL_CPARDISO provides a parallel backward > solve currently. > > The only seperation of forward and backward solves in MUMPS appears to be > provided with (from its users manual) > > A special case is the one > where the forward elimination step is performed during factorization (see > Subsection 3.8), instead of > during the solve phase. This allows accessing the L factors right after they > have been computed, with a > better locality, and can avoid writing the L factors to disk in an > out-of-core context. In this case (forward > > > >> On Nov 15, 2025, at 9:17 AM, Yin Shi via petsc-users >> <[email protected]> wrote: >> >> Dear Developers, >> >> In short, I need to explicitly use A.solveBackward(b, x) in parallel with >> petsc4py, where A is a Cholesky factored matrix, but it seems that this is >> not supported (e.g., for mumps and superlu_dist factorization solver >> backend). Is it possible to work around this? >> >> In detail, the problem I need to solve is to generate a set of correlated >> random numbers (denoted by a vector, w) from an uncorrelated one (denoted by >> a vector n). Denote the covariance matrix of n as C (symmetric). One needs >> to first factorize C, C = L L^T, and then solve the linear system L^T w = n >> for w in parallel. Is it possible to reformulate this problem for it to be >> implemented using petsc4py? >> >> Thank you! >> Yin >
