Oh thank you, this was helpful. I am interested in iterative solvers, so what is the minimum matrix size you think that strong scalability will show up for such methods?
On Mon, Mar 7, 2011 at 9:38 AM, Matthew Knepley <knepley at gmail.com> wrote: > On Mon, Mar 7, 2011 at 8:20 AM, Gaurish Telang <gaurish108 at gmail.com>wrote: > >> Hi, >> >> I have been testing PETSc's scalability on clusters for matrices of sizes >> 2000, 10,000, uptill 60,000. >> > > 1) These matrices are incredibly small. We usually recommend 10,000 > unknowns/process for weak scaling. You > might get some benefit from a shared memory implementation on a > multicore. > > >> All I did was try to solve Ax=b for these matrices. I found that the >> solution time dips if I use upto 16 or 32 processors. However for a larger >> number of processors however the solution time seems to go up rather than >> down. IS there anyway I can make my code strongly scalable ? >> > > 2) These are small enough that direct factorization should be the fastest > alternative. I would try UMFPack, SuperLU, and MUMPS. > > Matt > > >> I am measuring the total time (sec) and KSP_SOLVE time in the -log_summary >> output. Both times show the same behaviour described above. >> >> Gaurish >> > -- > 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 > -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20110307/867816ea/attachment.htm>
