On Wed, Mar 25, 2020 at 12:18 PM Mark Adams <mfad...@lbl.gov> wrote:

> Also, a better test is see where streams pretty much saturates, then run
> that many processors per node and do the same test by increasing the nodes.
> This will tell you how well your network communication is doing.
> But this result has a lot of stuff in "network communication" that can be
> further evaluated. The worst thing about this, I would think, is that the
> partitioning is blind to the memory hierarchy of inter and intra node
> communication.

Hierarchical partitioning was designed for this purpose.


> The next thing to do is run with an initial grid that puts one cell per
> node and the do uniform refinement, until you have one cell per process
> (eg, one refinement step using 8 processes per node), partition to get one
> cell per process, then do uniform refinement to get a reasonable sized
> local problem. Alas, this is not easy to do, but it is doable.
> On Wed, Mar 25, 2020 at 2:04 PM Mark Adams <mfad...@lbl.gov> wrote:
>> I would guess that you are saturating the memory bandwidth. After
>> you make PETSc (make all) it will suggest that you test it (make test) and
>> suggest that you run streams (make streams).
>> I see Matt answered but let me add that when you make streams you will
>> seed the memory rate for 1,2,3, ... NP processes. If your machine is decent
>> you should see very good speed up at the beginning and then it will start
>> to saturate. You are seeing about 50% of perfect speedup at 16 process. I
>> would expect that you will see something similar with streams. Without
>> knowing your machine, your results look typical.
>> On Wed, Mar 25, 2020 at 1:05 PM Amin Sadeghi <aminthefr...@gmail.com>
>> wrote:
>>> Hi,
>>> I ran KSP example 45 on a single node with 32 cores and 125GB memory
>>> using 1, 16 and 32 MPI processes. Here's a comparison of the time spent
>>> during KSP.solve:
>>> - 1 MPI process: ~98 sec, speedup: 1X
>>> - 16 MPI processes: ~12 sec, speedup: ~8X
>>> - 32 MPI processes: ~11 sec, speedup: ~9X
>>> Since the problem size is large enough (8M unknowns), I expected a
>>> speedup much closer to 32X, rather than 9X. Is this expected? If yes, how
>>> can it be improved?
>>> I've attached three log files for more details.
>>> Sincerely,
>>> Amin

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