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distributed processing for eppy

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distributed processing for eppyy


To run eppy on multiple nodes in parallel and collect the results.

So what is a node and why would you want to do this ?

A node can be any or all of the following:

  • a process (such E+ running on a single core on a multi-core computer)
    • so we can do multi-processing and run it on many cores on a single computer

  • a computer
    • so we can run it on multiple computers that are on the same network

  • a group of computer in a local network
    • So we can run multiple groups of machines that may be at different locations on different local networks

    • This can also be computers at different cloud locations

    • a single computer in the local network may act as an access node


Do the distributed processing with a single function call and get all the results back.

Sample code

import zeppy import ppipes

result = ppipes.ipc_parallelpipe(runfunction,

# runfunction is a function you will write,
    # that may run,
    # gather the total energy use and return it
# args_list = {args: [idf1, idf2, idf3, ...]}
    # list of files to run
# if nworkers=None:
    # it will start up as many nodes as there are items in args_list
    # if you don't have enough nodes avaliable, you can set nworkers=n.
    # it will start up n nodes and queue up the runs evenly on the nodes

For example the above code can do the following:

  • runfunction will run the idf file, and return the total energy usage

  • result will be a list total energy usage in the same order as the items in args_list

  • see the comments in the code for greater clarity


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

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