Hi Mathew, Thanks for the response. It actually seems like the matrix is very sparse (0.99% sparsity from what I’m measuring). It’s an FEA solver so it would make sense. My current guess is the optimization flags are making a large difference for the M1 Mac, but I am also surprised it makes such a huge difference.
It’s why I was asking if there was a resource or another to use my own version of PETSc with Conda. I believe a 2-3 x speed up is worth the hassle. Best, Jorge > On Oct 19, 2023, at 4:00 PM, Matthew Knepley <[email protected]> wrote: > > On Thu, Oct 19, 2023 at 3:54 PM Jorge Nin <[email protected] > <mailto:[email protected]>> wrote: >> Hi, >> I was playing around with a self compiled version and, and a the Conda >> binary of Petsc on the same problem, on my M1 Mac. >> Interestingly I found that the Conda binary solves the problem 2-3 times >> slower vs the self compiled version. (For context I’m using the petsc4py >> python interface) >> >> I’ve attached two log views to show the comparison. >> >> I was mostly curious about the possible cause for this. > > All the time is in the LU numeric factorization. I don't know if your matrix > is sparse or dense. I am guessing it is dense and different LAPACK > implementations are linked. If it is sparse, then the compiler options are > different between builds, but I would be surprised if it made this much > difference. > > Thanks, > > Matt > >> I was also curious how I could use my own compiled version of PETSc in my >> Conda install? >> >> >> Best, >> Jorge >> > > > -- > What most experimenters take for granted before they begin their experiments > is infinitely more interesting than any results to which their experiments > lead. > -- Norbert Wiener > > https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
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