distributed processing for eppyy

• Free software: Mozilla Public License 2.0 (MPL-2.0)
• Documentation: https://zeppy.readthedocs.io.

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,
                                args_list,
                                nworkers=None)

# runfunction is a function you will write,
    # that may run idf.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.