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Our R example is a great comparison of several methods to do parallel processing in R. I would love to see something similar for Python. One quick way to add a useful example would be to include a comparison of Python3's standard library classes concurrent.futures.ThreadPoolExecutor and concurrent.futures.ProcessPoolExecutor. These two classes allow Python to take better advantage of a single compute node. Their performance differences could be highlighted with a comparison using the prime number checking example: https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor-example
In order to take advantage of multiple nodes Python users can take advantage of MPI (at least on Owens) using mpi4py. MPI offers a number of options, but the mpi4py offers a futures-esque interface similar to Python3's concurrent.futures: https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.html
The text was updated successfully, but these errors were encountered:
Our R example is a great comparison of several methods to do parallel processing in R. I would love to see something similar for Python. One quick way to add a useful example would be to include a comparison of Python3's standard library classes
concurrent.futures.ThreadPoolExecutor
andconcurrent.futures.ProcessPoolExecutor
. These two classes allow Python to take better advantage of a single compute node. Their performance differences could be highlighted with a comparison using the prime number checking example: https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor-exampleIn order to take advantage of multiple nodes Python users can take advantage of MPI (at least on Owens) using
mpi4py
. MPI offers a number of options, but thempi4py
offers afutures-esque
interface similar to Python3'sconcurrent.futures
: https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.htmlThe text was updated successfully, but these errors were encountered: