On Fri, 28 Aug 2020, John Peterson wrote:
On Fri, Aug 21, 2020 at 9:51 AM Nikhil Vaidya <nikhilvaidy...@gmail.com>
wrote:
I need to print the sparse matrices (Petsc) and vectors involved in my
calculations to file using print_matlab(). I have observed that the
matrices and vectors that are written to the matlab scripts in serial and
parallel runs are not identical. Is this actually the case or am I missing
something?
By "not identical" I guess you mean that they don't match in all digits,
but are they at least "close"? It's normal to have floating point
differences between serial and parallel runs, but they should be due to
different orders of operations and therefore of order 10-100 * machine
epsilon.
To expand: it's even normal to have floating point differences between
different parallel runs. Both MPI reductions and threading pool
algorithms typically operate on "I'll begin summing the first data I
see ready" for efficiency, and "the first data I see ready" depends on
how loaded each CPU and network device is, meaning the reductions are
practically done in random order. IIRC there's even a funny bit in
the MPI standard where they find a very polite and professional way to
rephrase "If you don't like it then why don't you go write your own
reduction code!?"
And if you're using PETSc? You're probably not even be using the same
algorithm in serial vs parallel; the default (for performance /
robustness reasons) is Block Jacobi (between processors) + ILU0
(within a processor), so the very definition of your preconditioner
depends on your partitioning. This is a much bigger issue than the
order of operations problem. Because of it, if you want to be able to
do testing on different processor counts (or partitioner settings or
solver algorithm choices or preconditioner algorithm choices), you
can't safely assert that a "gold" regression test standard will be
repeatable to a tolerance any better than your solver tolerance (or
even equal to your solver tolerance, thanks to conditioning issues).
---
Roy
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