On Wed, Oct 8, 2014 at 3:52 AM, Serbulent UNSAL <[email protected]> wrote:
> There should be some communication overhead but this couldn't explain 2.5 > time slower solution." > > So may be it is a good idea that forwarding problem to Trilinos upstream > if you also confirm the results with 40,000 cells vs. 160,000 cells. > It seems clear to me that the big shared memory machines seem to scale very well, but the smaller workstations have problems. I believe that I'm using the same underlying numerical libraries on both systems. > > Serbulent > > Ps: If you decided to open a bug report to trilinos please share the > report number, so I try to follow and contribute for a solution. > I'm not planning on doing that. Maybe a question to the mailing list demoing different architectures with a simple PyTrilinos example (not FiPy). Whatever is going on is probably well understood by people who are into HPC. -- Daniel Wheeler
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