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This abstraction comes with inherent Python overhead, separately for each MPI process.
It is used for fine-grained parallelization within each MPI process.
The program is parallelized using Message Passing Interface (MPI) process parallelization via automatic compiler thread parallelization.
However, MPI process loads the whole dataset into memory during learning tasks, making it wasteful.
The receipt of FABs onto a single MPI process is an inherently serial process.
PDFs scale well, since each MPI process bins only local data.
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In order to speed up the initialization process one may disable all sanity checks on the user input that require MPI communication, e.g. if the same datatype was specified in all MPI processes.
For the in-transit mode, we chose the number of MPI processes in two different ways.
Now we analyze the scaling behavior of d2o when run with several MPI processes.
DistArray either needs an ipython ipcluster [11] as back end or must be run with two or more MPI processes.
The short-dashed purple line indicates the Nyx run time with 2,048 MPI processes without performing any analysis.
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