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If I run a program with the following runtime option (notice that I have not included -pc_type lu ), will the program use mumps to solve the linear system of equations directly, or does it run with default gmres iterative solver? Why do we have to specify a preconditioner for a direct solver ?<br><br> -pc_factor_mat_solver_package mumps<br><br></div>Thanks for your help..<br><br><br><br><div>> From: bsmith@mcs.anl.gov<br>> Date: Thu, 8 Sep 2011 14:02:09 -0500<br>> To: petsc-users@mcs.anl.gov<br>> Subject: Re: [petsc-users] Fwd: nonzero prescribed boundary condition<br>> <br>> <br>> Are you running with -ksp_converged_reason and -ksp_monitor_true_residual to see if the iterative method is actually converging and how rapidly. Also if you impose a tight tolerance on the iterative solver say with -ksp_rtol 1.e-12 how much do the "solutions" differ, it should get smaller the smaller you make this tolerance (in double precision you cannot expect to use a tolerance less than 1.e-3 or 1.e-14)<br>> <br>> If your "big" matrix is very complicated and comes from no well understood simulation it may be that it is very ill-conditioned, in that case you either need to understand the underlying equations real well to develop an appropriate preconditioner or just use parallel direct solvers (which get slow for very large problems but can handle more ill-conditioning.n To use the MUMPS parallel direct solver you can configure PETSc with <br>> --download-mumps --download-scalapack --download-blacs and run the program with -pc_type lu -pc_factor_mat_solver_package mumps<br>> <br>> Barry<br>> <br>> <br>> <br>> On Sep 8, 2011, at 12:57 PM, amrit poudel wrote:<br>> <br>> > After running my simulation multiple times on a multiprocessor computer I've just verified that using iterative solver (default gmres) in PETSc to solve a linear system of equations ( Cx=b) with more than 2 processors setting ALWAYS lead to erroneous result. Running identical code with identical setting except for the number of processors ( set this to 2) ALWAYS gives me correct result .<br>> > <br>> > I am really not sure what is the point behind including iterative solvers if they result into erroneous result on a multiprocessor computer. The result I get from multiprocessor computer is a complete garbage, so I am really not talking about small percentage of error here. Also, if somebody could enlighten why the iterative solvers are error prone on multiprocessors that will be highly appreciated. <br>> > <br>> > I am very hopeful that there is a way around to this problem, because PETSc is such a powerful and useful library that I really do not want to give up on this and start something else from scratch. <br>> > <br>> > <br>> > Would you think that a DIRECT SOLVER would circumvent this problem? My problem is that I have a very large system of equations and the size of a sparse coefficient matrix is huge ( > 1e+8). I assemble this matrix in MATLAB, write to a binary file, and read it in PETSc. So I really need to be able to solve this system of equations in a cluster of computers (which inherently has multiprocessors and distributed memory setting). Does this mean I am completely out of luck with PETSc's iterative solver package and the only hope for me is the direct solver? I do have MUMPS downloaded and compiled with PETSc, so I will give that a try and see what results I obtain, but I am really surprised that iterative solvers are no good in a large multiprocessor settings.<br>> > <br>> > Any insights, suggestions/advice will be highly appreciated.<br>> > <br>> > Thanks.<br>> > <br>> > PS (I can attach my entire code, plots that compare the results obtained by solving Cx=b in 2 processors vs 12 or 6 processors if any body wants to take a look at it. I get garbage if I run iterative solver on 12 processors)<br>> <br></div> </div></body>
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