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The reason so many different distributions are described is because
what is optimal depends so much on your own case.
Even if one disregards CYCLIC axes, there are still all those BLOCK
choices you mention. It isn't just a matter of choosing which axes
will be * since * is just a special case of BLOCK. You have to choose
the shape or aspect ratio of the subgrids.
In some problems, it is desirable to minimize the surface-to-volume
ratio of each local subgrid so that interprocess communication costs
are minimized relative to the computational work each process has to
do. So, you would want to make each subgrid as "cubic" as possible.
Some sort of BLOCK,BLOCK,BLOCK.
The "physics" may argue otherwise. Not all directions may be the
same. E.g., in atmospheric models, the vertical direction is very
different from the horizontal ones.
Algorithms may also drive your choice. E.g., for multidimensional
FFTs, you might want one axis to be local. Then, you would transpose
axes to make another one local.
You might also want the "innermost axis" (in the process's linear
address space) to be as long as possible to benefit from
software/hardware vectorization of computationally expensive loops.
Lots of choices. It depends on your problem.
Alexandru Blidaru wrote:
If there is an already existing implementation of the
*Block or Block* methods that splits the array and sends the individual
pieces to the proper nodes, can you point me to it please?
On Tue, Jul 20, 2010 at 9:52 AM, Alexandru
Blidaru <alexsb92@gmail.com>
wrote:
I
have a 3D array, which I need to split into equal n parts, so that each
part would run on a different node. I found the picture in the
attachment from this website (https://computing.llnl.gov/tutorials/parallel_comp/#DesignPartitioning) on
the different ways to partition data. I am interested in the block
methods, as the cyclic methods wouldn't really work for me at all.
Obviously the *, BLOCK and the BLOCK, * methods would be really easy to
implement for 3D arrays, assuming that the 2D picture would be looking
at the array from the top. My question is if there are other better
ways to do it from a performance standpoint?
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