Dear Dustin,
With regard to a lot of tutorial programms I would say that this is
the usual way to work with /DynamicSparsityPatterns/, isn't it?
Yes, the description you give is exactly the way I would choose for the
sparsity pattern.
So back to the question. Running the program the copy operation takes
a huge amount of time. Just giving you some figures. With
ndofs_m = 823875,
ndofs_s = 1635075,
ndofs_bi = 25155,
meaning about 2.5 million dofs at all
it takes about 10 hours and 50 minutes to copy the
/BlockDynamicSparsityPattern /in contrast to an assembling time of 2
minutes and 38 seconds. So the question is if it is normal the copying
operation taking so much time?
No, copying the sparsity pattern should in general be very fast. The
functions that do this should be reasonably optimized so that you mostly
pay for the memory access. It could be that we missed something for the
block case, though. How is your computational setup, i.e., how many
nonzero entries do you have in your matrix? Have you checked that you do
not run out of memory and see a large swap time? How do the run times
behave when you choose a smaller problem size? (I wonder if there is
some higher than O(N) complexity somewhere.)
And also whether there is way to increase the performance?
By the way the program was compiled in release mode.
It should be possible to do the whole copy operation in say 2x the time
it takes to zero a sparse matrix. If it's a bug we will fix it. It would
be very helpful if you could provide us an example file that only
contains the setup phase so we can investigate the issue further.
Best,
Martin
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