Oh, in that case I will try out BoomerAMG. Getting AMGX to build correctly was also tricky so hopefully the HYPRE build will be easier.
Thanks, Sreeram On Thu, Dec 7, 2023, 3:03 PM Pierre Jolivet <pie...@joliv.et> wrote: > > > On 7 Dec 2023, at 9:37 PM, Sreeram R Venkat <srven...@utexas.edu> wrote: > > Thank you Barry and Pierre; I will proceed with the first option. > > I want to use the AMGX preconditioner for the KSP. I will try it out and > see how it performs. > > > Just FYI, AMGX does not handle systems with multiple RHS, and thus has no > PCMatApply() implementation. > BoomerAMG does, and there is a PCMatApply_HYPRE_BoomerAMG() implementation. > But let us know if you need assistance figuring things out. > > Thanks, > Pierre > > Thanks, > Sreeram > > On Thu, Dec 7, 2023 at 2:02 PM Pierre Jolivet <pie...@joliv.et> wrote: > >> To expand on Barry’s answer, we have observed repeatedly that MatMatMult >> with MatAIJ performs better than MatMult with MatMAIJ, you can reproduce >> this on your own with >> https://petsc.org/release/src/mat/tests/ex237.c.html. >> Also, I’m guessing you are using some sort of preconditioner within your >> KSP. >> Not all are “KSPMatSolve-ready”, i.e., they may treat blocks of >> right-hand sides column by column, which is very inefficient. >> You could run your code with -info dump and send us dump.0 to see what >> needs to be done on our end to make things more efficient, should you not >> be satisfied with the current performance of the code. >> >> Thanks, >> Pierre >> >> On 7 Dec 2023, at 8:34 PM, Barry Smith <bsm...@petsc.dev> wrote: >> >> >> >> On Dec 7, 2023, at 1:17 PM, Sreeram R Venkat <srven...@utexas.edu> wrote: >> >> I have 2 sequential matrices M and R (both MATSEQAIJCUSPARSE of size n x >> n) and a vector v of size n*m. v = [v_1 , v_2 ,... , v_m] where v_i has >> size n. The data for v can be stored either in column-major or row-major >> order. Now, I want to do 2 types of operations: >> >> 1. Matvecs of the form M*v_i = w_i, for i = 1..m. >> 2. KSPSolves of the form R*x_i = v_i, for i = 1..m. >> >> From what I have read on the documentation, I can think of 2 approaches. >> >> 1. Get the pointer to the data in v (column-major) and use it to create a >> dense matrix V. Then do a MatMatMult with M*V = W, and take the data >> pointer of W to create the vector w. For KSPSolves, use KSPMatSolve with R >> and V. >> >> 2. Create a MATMAIJ using M/R and use that for matvecs directly with the >> vector v. I don't know if KSPSolve with the MATMAIJ will know that it is a >> multiple RHS system and act accordingly. >> >> Which would be the more efficient option? >> >> >> Use 1. >> >> >> As a side-note, I am also wondering if there is a way to use row-major >> storage of the vector v. >> >> >> No >> >> The reason is that this could allow for more coalesced memory access when >> doing matvecs. >> >> >> PETSc matrix-vector products use BLAS GMEV matrix-vector products for >> the computation so in theory they should already be well-optimized >> >> >> Thanks, >> Sreeram >> >> >> >