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‎README.md‎

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@@ -25,10 +25,10 @@ Git: https://github.com/raboonik
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## Step 2
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Run "add2path.sh" to automatically update the python enviornment by executing
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    ./add2path.sh
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    . ./add2path.sh
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# Uninstallation
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To uninstall and update the python environment simply run
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    ./uninstall.sh
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    . ./uninstall.sh

‎add2path.sh‎

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#!/bin/bash
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: '
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Add AutoParallelizePy library to .bashrc and source it.
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'
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if [ ! $EUID -ne 0 ]
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then
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echo Please do not run as root and try again without sudo!
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exit
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fi
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if [ ! -f "/home/abbas/.bashrc" ]
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then
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touch "/home/abbas/.bashrc"
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fi
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addLine="export PYTHONPATH=':/home/abbas/.local/lib/AutoParallelizePy/libs'"
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if grep -Fxq $addLine $HOME/.bashrc
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then
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echo .bashrc file already updated
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else
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echo Adding the AutoParallelizePy library to PYTHONPATH in .bashrc
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echo $addLine >> $HOME/.bashrc
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source $HOME/.bashrc
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fi
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‎examples/example01_gather_scalar.py‎

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"""
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Summary: A simple MPI program to demonstrate
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the AutoParallelizePy utilities:
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function gather_scalar
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Aims: Create scalar numbers on each proc and
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gather them on the main rank and print the
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results.
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"""
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import numpy as np
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from mpi4py import *
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import AutoParallelizePy as APP
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# Initialize the MPI environment
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Init Parallel #◈
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comm = MPI.COMM_WORLD #◈
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size = comm.Get_size() #◈
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rank = comm.Get_rank() #◈
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mainrank = 0 #◈
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Define a traceable scalar on each rank
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scalarNum = rank
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# Gather on the main rank and print
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out = APP.gather_scalar(comm, size, rank, mainrank, scalarNum, dtype='float')
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if rank == mainrank:
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out = gather_scalar(comm, size, rank, mainrank, scalarNum, dtype='int')
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print("Gathered data on the main rank = ", out)

‎examples/example02_gather_array_ND.py‎

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"""
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Summary: A simple MPI program to demonstrate
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the AutoParallelizePy utilities:
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function get_subarray_ND
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function gather_array_ND
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class domainDecomposeND
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Aims: Create a 2D array to work as our test
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input data. Use get_subarray_ND for each proc
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to take a chunk of the data according to some
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domain decomposition scheme and use gather_array_ND
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to gather all the subarrays back into another
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array on the main rank which recovers the original
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array.
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"""
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import time
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start = time.time()
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import numpy as np
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from mpi4py import *
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import AutoParallelizePy as APP
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# Initialize the MPI environment
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Init Parallel #◈
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comm = MPI.COMM_WORLD #◈
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size = comm.Get_size() #◈
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rank = comm.Get_rank() #◈
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mainrank = 0 #◈
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Create the 2D input array
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arrShape = [57,89]
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Arr = np.arange(np.prod(arrShape)).reshape(arrShape) * np.pi
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# Configure the domain decomposition scheme such that
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# both axes of the 2D data is parallelized
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parallel_axes = [0,1]
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domDecompND = APP.domainDecomposeND(size,arrShape,parallel_axes)
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# Have each proc to take a chunk of the input data
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myArr = APP.get_subarray_ND(rank,domDecompND,Arr)
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# Gather the subarrays back on the mainrank and compare
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# with the original data
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gatheredArrOnMainRank = APP.gather_array_ND(comm, rank, mainrank, domDecompND, myArr, 'float')
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if rank == mainrank:
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print("")
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if np.all(gatheredArrOnMainRank == Arr):
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print("The original data was successfully reconstructed!")
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else:
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print("Failed!")
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print("Running example02 took ",time.time() - start, " seconds!")

‎examples/example02_gather_vector_1D.py‎

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‎examples/example03_scatter_array_ND.py‎

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"""
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Summary: A simple MPI program to demonstrate
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the AutoParallelizePy utilities:
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function scatter_array_ND
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class domainDecomposeND
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Aims: Create a 2D array ON THE MAIN RANK to work
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as our test input data. Use scatter_array_ND to
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SCATTER chunks of this data according to some
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domain decomposition scheme to all the other cores.
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Finally use gather_array_ND to gather all the sub-
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arrays back into another array on the main rank
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which recovers the original array.
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Tip1: Note that as opposed to example02 where we
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assumed the input data was available on all the
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procs, here the input data only exists on the main
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rank, which makes it more memory efficient. However,
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since scattering the data potentially takes more time
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than slicing it in situ, which makes it slightly less
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time efficient.
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Tip2: Scattering only works for sending subarrays of
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an array on a source proc to other procs. Use broadcasting
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to send a scalar to other procs.
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"""
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import time
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start = time.time()
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import numpy as np
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from mpi4py import *
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import AutoParallelizePy as APP
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# Initialize the MPI environment
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Init Parallel #◈
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comm = MPI.COMM_WORLD #◈
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size = comm.Get_size() #◈
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rank = comm.Get_rank() #◈
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mainrank = 0 #◈
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Create the same 2D input array as example02_gather_array_ND
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# this time only on the main rank, and set to None on other procs
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arrShape = [57,89]
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if rank == mainrank:
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Arr = np.arange(np.prod(arrShape)).reshape(arrShape) * np.pi
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else:
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Arr = None
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# Configure the domain decomposition scheme such that
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# both axes of the 2D data is parallelized
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parallel_axes = [0,1]
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domDecompND = APP.domainDecomposeND(size,arrShape,parallel_axes)
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# Use scatter_array_ND to scatter chunks of the input data to
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# other procs
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myArr = APP.scatter_array_ND(comm,rank,mainrank,domDecompND,Arr,dtype='float')
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# Gather the subarrays back on the mainrank and compare
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# with the original data
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gatheredArrOnMainRank = APP.gather_array_ND(comm, rank, mainrank, domDecompND, myArr, 'float')
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if rank == mainrank:
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print("")
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if np.all(gatheredArrOnMainRank == Arr):
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print("The original data was successfully reconstructed!")
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else:
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print("Failed!")
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print("Running example03 took ",time.time() - start, " seconds!")

‎examples/example04_bcast.py‎

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"""
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Summary: A simple MPI program to demonstrate
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the AutoParallelizePy utilities:
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function bcast
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Aims: Create a scalar, a 1D array, and a 3D
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array on the main rank and use bcast to copy
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them onto other procs.
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Tip: Broadcasting is the copying of the same
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scalar or array onto all the other procs.
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"""
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import time
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start = time.time()
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import numpy as np
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from mpi4py import *
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import AutoParallelizePy as APP
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# Initialize the MPI environment
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Init Parallel #◈
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comm = MPI.COMM_WORLD #◈
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size = comm.Get_size() #◈
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rank = comm.Get_rank() #◈
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mainrank = 0 #◈
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Create a scalar integer on the main rank and bcast to other procs
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if rank == mainrank:
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intScalar = 5
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else:
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intScalar = None
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bcastIntScalar = APP.bcast(comm, rank, mainrank, intScalar, dtype='int')
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# Create a 1D array of length 127 of random real numbers on the main
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# rank and bcast to other procs
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if rank == mainrank:
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arr1D = np.random.uniform(low=-20, high=20, size=([127]))
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else:
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arr1D = None
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bcastArr1D = APP.bcast(comm, rank, mainrank, arr1D, dtype='float')
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# Create a 3D array of shape [23,34,67] of random real numbers on
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# the mainrank and bcast to other procs
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if rank == mainrank:
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arr3D = np.random.uniform(low=-20, high=20, size=([23,34,67]))
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else:
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arr3D = None
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bcastArr3D = APP.bcast(comm, rank, mainrank, arr3D, dtype='float')
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if rank == mainrank:
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print("")
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if (intScalar == bcastIntScalar and
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np.all(arr1D == bcastArr1D) and
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np.all(arr3D == bcastArr3D)):
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print("Broadcasting successful!")
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else:
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print("Failed!")
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print("Running example04 took ",time.time() - start, " seconds!")

‎examples/example05_reshape_vector_ND.py‎

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"""
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Summary: A simple MPI program to demonstrate
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the AutoParallelizePy utilities:
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function reshape_array_ND
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function get_subarray_ND
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function gather_array_ND
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class domainDecomposeND
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Aims: Create a 3D array as the input data
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and use get_subarray_ND to have each proc
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take a slice of it according to some domain
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decomposition rule. Then use reshape_array_ND
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to reshape the subarrays according to a new
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domain decomposition rule.
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"""
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import time
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start = time.time()
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import numpy as np
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from mpi4py import *
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import AutoParallelizePy as APP
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# Initialize the MPI environment
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Init Parallel #◈
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comm = MPI.COMM_WORLD #◈
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size = comm.Get_size() #◈
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rank = comm.Get_rank() #◈
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mainrank = 0 #◈
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#◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈◈
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# Create the 3D input array
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arrShape = [57,89,62]
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Arr = np.arange(np.prod(arrShape)).reshape(arrShape) * np.pi
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# Configure the domain decomposition scheme such that
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# both axes of the 3D data is parallelized
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parallel_axes = [0,1,2]
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domDecompND = APP.domainDecomposeND(size,arrShape,parallel_axes)
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# Have each proc to take a chunk of the input data
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myArr = APP.get_subarray_ND(rank,domDecompND,Arr)
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# Reconfigure the domain decomposition scheme, this time only
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# parallelize the first and third axes
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new_parallel_axes = [0,2]
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new_DomDecompND = APP.domainDecomposeND(size,arrShape,new_parallel_axes)
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# Reshape the subarrays according to the new domain decomposition rule
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new_myArr = APP.reshape_array_ND(comm, rank, mainrank, domDecompND, new_DomDecompND, myArr, dtype='float')
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# Gather both
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gatheredArrOnMainRank = APP.gather_array_ND(comm, rank, mainrank, domDecompND, myArr, 'float')
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new_gatheredArrOnMainRank = APP.gather_array_ND(comm, rank, mainrank, new_DomDecompND, new_myArr, 'float')
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if rank == mainrank:
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print("")
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if (np.all(Arr == gatheredArrOnMainRank) and
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np.all(Arr == new_gatheredArrOnMainRank)):
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print("Reshaping successful!")
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else:
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print("Failed!")
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print("Running example05 took ",time.time() - start, " seconds!")

‎examples/example_bcast_vector_ND.py‎

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‎examples/example_create_randoms_acorss_cores.py‎

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